Showing papers in "Modeling Earth Systems and Environment in 2019"

Prediction of water quality index using artificial neural network and multiple linear regression modelling approach in Shivganga River basin, India

Abstract: The present study has applied artificial neural network (ANN) and multiple linear regression (MLR) techniques to predict the fitness of groundwater quality for drinking from Shivganga River basin, located on the eastern slopes of the Western Ghat region of India. In view of this, thirty-four (34) representative groundwater samples have been collected and analyzed for major cations and anions during pre- and post-monsoon seasons of 2015. The physicochemical parameters such as pH, EC, TDS, TH, Ca, Mg, Na, K, Cl, HCO3, SO4, NO3 and PO4 were considered for computing water quality index (WQI). Analytical results confirmed that all the parameters are within acceptable range; however, EC, TDS, TH, Ca and Mg are exceeding the desirable limit of the WHO drinking standards. The groundwater suitability for drinking was ascertained by WQI method. The WQI value ranges from 25.75 to 129.07 and from 37.54 to 91.38 in pre- and post-monsoon seasons, respectively. Only one sample (DW5) shows 129.07 WQI value indicating poor quality for drinking due to input of domestic and agricultural waste. In the view of generating consistent and precise model for prediction of WQI-based groundwater quality, a Levenberg–Marquardt three-layer back propagation algorithm was used in ANN architecture. Further, MLR model is used to check the efficiency of ANN prediction. The results corroborated that predictions of ANN model are satisfactory and confirms consistently acceptable performance for both the seasons. The proposed ANN model may be useful in similar studies of groundwater quality prediction for drinking purpose.

Investigating the hydrogeochemical processes and quality of water resources in Ojoto and environs using integrated classical methods

Abstract: In an attempt to determine their suitability for consumption and irrigation uses, the prevailing hydrogeochemical processes and quality of both surface and groundwaters in Ojoto province, southeastern Nigeria were studied. Classical scientific methods and indicators such as hydrogeochemistry, stoichiometry, water quality index (WQI), and multivariate statistical analyses were integrated to achieve the research objectives. pH results classified most of the waters as slightly acidic. The order of dominance of the major cations and anions is Na+ > Ca2+ > K+ > Mg2+ and SO42– > Cl– > NO3– > HCO3–, respectively. The dominant water type is Na–Ca–SO4, and the dominant water facies in the area is sodium sulphate (Na–SO4), constituting about 54% of the total samples. Several hydrogeochemical, stoichiometric, and multivariate statistical analyses revealed that both anthropogenic inputs and geogenic processes (such as precipitation, silicate weathering, oxidation, and ionic exchange) influence the chemistry and quality of the waters. WQI of the waters showed that only 17.86% of the analyzed samples are of good quality for drinking purposes, whereas the quality of 53.57, 17.86, and 10.71% of the samples is poor, very poor, and unfit for use, respectively. Various irrigation suitability assessments (including salinity hazard, sodium absorption ratio, sodium percentage, residual sodium carbonate, chloro-alkaline indices, magnesium hazard, Kelly’s ratio, permeability index, and potential salinity) conducted revealed that majority of the analyzed waters have poor irrigation quality.

Application of GIS-based analytic hierarchy process and frequency ratio model to flood vulnerable mapping and risk area estimation at Sundarban region, India

Abstract: Flood is one of the most devastating natural calamities with environmental and socio-economic impacts. Comprehensive flood management is essential to reduce the flood effects on human lives and livelihoods. The main objective of this study was to examine the capability of geographic information system (GIS) in coupling with analytic hierarchy process (AHP) and frequency ratio (FR) model for flood vulnerability mapping. The study was carried out in two stages of analysis. First of all, a flood inventory map was prepared. Consequently, seven flood contributing factors viz. land elevation, slope angle, topographic wetness index, rainfall deviation, land use land cover, clay content in soil and distance from rivers were prepared for spatial analysis. Looking toward the flood records, a total of 270 flood points were marked from field study area, out of which 200 (75%) flood points were randomly selected for training data and the remaining 70 (25%) flood points were used for testing or validation purposes using prediction accuracy and success rate. The result revealed that distance from river, rainfall deviation and land use land cover have the great role for flood occurring in the study area with selected factor weight value (SFWV) of 0.33, 0.21 and 0.14, respectively. The validation result showed that prediction accuracy was 0.8142 and success rate was 0.8450 which may consider for validating the frequency ratio model that applied in present study. The application of AHP and frequency ratio model helps in identifying flood vulnerable zonation and potential risk area estimation. The findings from present study will be helpful for planner in flood mitigation strategies as a part of flood preparedness and will appear as a source for further research in the study area.

Water quality appraisal of selected farm provinces using integrated hydrogeochemical, multivariate statistical, and microbiological technique

Abstract: With increasing use of agrochemicals, inorganic and organic fertilizers in the selected rural farm provinces in Nigeria, the need to assess the quality of their water resources for various purposes became compelling. This research, therefore, investigated the quality and geochemistry of groundwaters and surface waters in the area using integrated physicochemical, hydrogeochemical, multivariate statistical and microbiological approach. Most values from physicochemical analysis were generally below maximum allowable limits. However, some samples are contaminated, with some obtained parameter values beyond allowable limits. The pH, NO3, NO2, PO4, K, and Mn values ranged from 4.1 to 6.9, 0–33 mg/l, 0–0.08 mg/l, 0–19.91 mg/l, 0–7.92 mg/l and 0–0.07 mg/l, respectively. Based on pH, many of the samples are slightly acidic. The dominant water type is Ca-HCO3, with few Ca-Cl types. Hydrogeochemical investigations further showed that the supply of major ions in the waters and the geochemical evolution are mainly controlled by rock–water interactions, silicate weathering and ionic exchanges. However, multivariate statistical analyses showed that the variations in chemistry and quality of the waters are due to combined influences of geogenic processes and human activities. Microbial analysis revealed that many samples are contaminated with Escherichia coli, Klebsiella, Salmonella, Shigella and Staphylococcus species. Treatment of contaminated water before use is, therefore, advised.

Water quality modelling using artificial neural network and multivariate statistical techniques

Abstract: This study investigates and proposes a reduction in the number of water quality monitoring stations, parameters and develops the best input combination for water quality modelling using artificial neural network and multivariate statistical technique. Fourteen water quality physicochemical parameters acquired from eight monitoring sites for 8 years (2006–2013) were investigated. Hierarchical agglomerative cluster analyses (HACA) classify the eight monitoring sites into two significant clusters. Principal component analysis (PCA) accounted for more than 82% of the total variance and attributes the sources of pollution to critical anthropogenic activities, surface run-off and weathering of parent rocks. Furthermore, sensitivity analyses percentage contribution of pollutants revealed dissolved oxygen as the most significant parameter responsible for the pollution (66.3%), followed by ammonia nitrogen (14.4%), chemical oxygen demand (9.4%) and biochemical oxygen demand (5.3%). The result for source category apportionment assigned 39% to rock weathering, 25% anthropogenic activities, 20% surface run-off, 11% faecal waste, 3.4% human and natural factors and 1.4% erosion of river bank. In addition, three input combination models (model 1, 2 and 3) were developed in order to identify the best that can predict water quality index (WQI) at a very high precision. Model 2 using the principal component scores before varimax rotation appears to have the best prediction capability at node eight with coefficient of determination (R2) = 0.999 and root mean square error (RMSE) = 0.159. These findings justify the use of environmetrics modelling technique to reveal the pattern of water quality for decision making by government and stakeholders.

Using simulation model to determine the regulation and to optimize the quantity of chlorine injection in water distribution networks

Abstract: Water can be disinfected by various techniques. Chlorination is the most prevalent and the cheapest method in this regard. One of the most significant factors in chlorination is the location and the amount of chlorine injections, both of which must be chosen in a way that the amount of chlorine remaining in all sections of a city’s water distribution network is within the standard range, and the associated costs are reduced to a considerable extent. Therefore, in this research, we have implemented a technique which takes both of those issues, i.e., the amount of residual chlorine in pipes and the quantity of chlorine consumed in diverse sections of the water distribution network into account leading to a much more cost-effective water disinfection technique. Our technique shows that municipal water distribution network, are effectively in tune with the current highest standards set in the water disinfection procedure for the lowest possible cost. For modeling the water allocation network, the WaterGems software was used. Using this software, the network was modeled into two practical methods, i.e., gravity and direct pumping methods. The productivity of gravity as well as direct pumping methods was shown to control the optimal chlorine injection rate by resolving the two applied models of water distribution networks. The results showed that the residual chlorine content in 70% of the direct pipe network was within the standard range and in 100% of the tubes in the gravity distribution network lower than the standard. Also, the chlorine which was consumed in the direct pumping network was 7% lesser than that in the gravity distribution network. This research showed that the adjusting of chlorine injection into direct-pumped networks compared with gravity distribution networks was more feasible and also required less chlorine consumption.

Relation between urban biophysical composition and dynamics of land surface temperature in the Kolkata metropolitan area: a GIS and statistical based analysis for sustainable planning

Abstract: The major environmental impacts of urbanization have changed urban biophysical components which ultimately promoted land surface temperature (LST) as well as urban heat island (UHI). This study explores the upshot of land use land cover (LULC) and resultant effect on biophysical components to understand the heat island mechanism in the Kolkata Metropolitan Area (KMA) for four selected time period of 1991, 2001, 2011 and 2017. Six satellite-derived biophysical components were selected for the present analysis: NDBI, NDVI, NDWI, MNDWI, NDBaI and SAVI. Selected bands of Landsat-5 TM and OLI-8 were used for this purpose. The result shows that the built-up area has been increased from 322.68 km2 in 1991 to 982.86 km2 in 2017 and accordingly, LST also rises from 18.47 °C mean LST of 1991 to 23.30 °C mean LST of 2017. The correlation coefficient among the biophysical parameters and LST shows that the highest continuous increasing positive relationship between NDBI and LST (R = 0.71). Moreover, multiple linear regression model (MLR) is adapted to the prediction on LST with the variation of biophysical parameters. Finally, we produced hot spot maps using Getis-Ord-Gi* statistics for the selected year to highlights the hot spot and cold spot area in KMA. The methodology presented in this paper can be broadly applied for the planning purposes because LST monitoring is an important parameter of sustainable urban planning.

Assessment of inundation risk in urban floods using HEC RAS 2D

Abstract: Flood is the most common natural disaster upsetting the highest population of the world. In recent times, severe floods in urban areas are occurring more frequently owing to uncontrolled urbanization and climate change and it will continue to grow in upcoming years. Prevention of such events is not possible but with advancement of technology, flood-vulnerable areas can be identified through 2-D modeling of critical rainfall events. The difficulty associated with urban floods is unpredictable flow conditions in urban environment due to rapid alterations in topography and unavailability of extensive raw dataset. Thus, modeling of urban floods becomes a complex process. A vast number of numerical models have evolved over the past few years which are capable of flood mapping; most of them are commercial, rigorous and need extensive dataset to generate precise results. This paper presents a simple sophisticated approach to analyze extreme rainfall events based on past critical events and synthetic hyetographs developed from IDF curves for a part of Hyderabad, India. HEC-RAS, a freely available 2-D hydraulic model with integration to GIS is used to generate depth of flood inundation over underlying terrain and risk maps of flood inundation are developed for different rainfall scenarios. The model results identify 17% of total area is liable to floods out of which 9% area indicates high risk, 52% area shows medium risk and remaining 35% area falls under low risk of flooding.

Accessing the soil erosion rate based on RUSLE model for sustainable land use management: a case study of the Kotmale watershed, Sri Lanka

Abstract: Water based soil erosion is a serious socio-economic and environmental problem across the world especially in the tropical region. Assessing the soil erosion quantitatively and spatially provides information to prioritize the soil conservation area in sustainable land management view point. Among the other soil erosion approaches, erosion modeling has been playing a significant role and provides an accurate result in a cost-effective manner. In this study, revised universal soil loss equation (RUSLE) was integrated with remote sensing (RS) and geographic information system (GIS) to analyse the quantitative and spatial distribution of soil erosion across the entire Kotmale watershed which is located in the western part of the central mountain region in Sri Lanka. In the methodology, the parameters of the RUSLE model were estimated using pixel overlay method in ArcGIS software, both spatial data and remote sensing data facilitated with appropriate calibration. From the analysis, the annual soil erosion ranges from 0 to 472 t ha− 1 year− 1 with the mean and standard deviation 9.8 t ha− 1 year− 1 and 15.7 t ha− 1 year− 1 respectively. The mean erosion rate of the model was correlated with ground based data. After the final model was established, conservation priority area was identified by using hot and cold spot analysis. Here “hot spots” shows the area with high soil erosion clustering value, while “cold spot” refers to area with low soil erosion clustering. The soil conservation priority map has been produced and the result shows that approximately 25% represents hot sport. The result would be an aid and sources for soil and water conservation in the Kotmale watershed.

Modeling of water induced surface soil erosion and the potential risk zone prediction in a sub-tropical watershed of Eastern India

Abstract: Soil is the earth’s fragile skin that anchors all life on earth. Half of the topsoil on the planet has been lost in the last 150 years. Land degradation due to soil loss is one of the major environmental concerns which can be influences by the natural as well as anthropogenic activities. These impacts include compaction, loss of soil structure, nutrient degradation, and soil salinity. The effects of soil erosion go beyond the loss of fertile land. It has led to increased pollution and sedimentation in streams and rivers. And degraded lands are also often less able to hold onto water which can worsen flooding. Revised Universal soil Loss Equation (RUSLE) model and integration with Geographical Information System (GIS) have been taken into consideration for estimating the average annual soil loss in Arkosa watershed. The Overlay Analysis technique have been adopted in RUSLE model for estimating the influences of different factors namely rainfall and runoff erosivity factor (R), soil erodibility factor (K), slope length and steepness factor (LS), cover and management factor (C) and support practice factor (P) etc. The average annual soil loss of Arkosa watershed ranged between 0 to 10 tons/ha/year. Here the combined index method has been adopted to show the impact spatially of combine index of these five factors, i.e., R, K, LS, C and P. Apart from this there are total 29 points have been selected randomly for securing that the present soil loss model sounded with ground reality or not. The actual soil loss and predicted soil loss show the positive relationship with them in an r2 value of 0.882. Besides this the present study provides a reliable prediction for future on potential soil erosion risk zones which ranged between 0 and 16 tons/ha/year. To overcome from extreme or severe soil loss situation suitable soil conservation practices or support practices have to be taken care off for minimizing the erosion of the fertile soil or the top soil for making the region less vulnerable from soil erosion in present rate. Sustainable land use can help to reduce the impact of agriculture and livestock, preventing the soil degradation and erosion and the loss of valuable land to deforestation.

Temperature and precipitation trend over 139 major Indian cities: An assessment over a century

Abstract: The increasing temperature in an urban area has adversely affected the Earth’s environment, human health and has been a subject of prior considerable attention. Hence, for the sustainable development and adaptation of the urban population to climate change, it is essential to find the trend and magnitude of temperature and precipitation over Indian cities. An endeavor has been made in the present study to find the trend and magnitude of temperature and precipitation over 139 major Indian cities with respect to different Koppen climatic zones using Climatic Research Unit datasets of last 115 years (1901–2015). The annual and seasonal trend and magnitude of temperature and precipitation of these cities were assessed using non-parametrical modified Mann–Kendall and Sens’s slope test. The results indicate that the annual and seasonal temperature trend was significantly deceasing over the cities of north western region whereas an increasing trend in the south eastern cities of India. A significant relationship was observed between temperature and precipitation in the hot steppe (BSh), dry winter humid subtropical (Cwa) and tropical wet and dry (Aw) climatic zone. The distribution of precipitation trend is highly heterogeneous and uneven as compared to temperature. The eastern part of India shows decreasing precipitation trend in comparison with the western part.

Comparison of the erosion prediction models from USLE, MUSLE and RUSLE in a Mediterranean watershed, case of Wadi Gazouana (N-W of Algeria)

Abstract: Water erosion is one of the most serious problems of soil degradation in the world, the north of Africa region is particularly exposed to this phenomenon. In fact, the phenomenon gets worse with the climate changes and the adverse anthropogenic environmental interventions. In recent decades, the estimation of soil erosion using empirical models has been a promising research topic. Nevertheless, their application over a large and ungauged areas remains a real challenge due to the availability and quality of the required data. Using the GIS environment, this study aims to estimate and compare the water erosion rates by the three models of Universal Soil Loss Equation (USLE), Modified Universal Soil Loss Equation (MUSLE) and Revised Universal Soil Loss Equation (RUSLE) in Wadi Gazouana North-West of Algeria. The estimated specific erosion in the entire wadi Ghazouana watershed surface is 9.65, (t/ha/year), 9.90 (t/ha/year) and 11.33 (t/ha/year) by USLE, RUSLE and MUSLE models, respectively. We can also conclude that USLE, RUSLE and MUSLE soil erosion models produced relatively similar results, however, the MUSLE model showed a higher spatial dispersion of the erosion risk compared to the others. The rain factor in this model was more effective; which explain its higher erosion rate.

Modelling of river flow in ungauged catchment using remote sensing data: application of the empirical (SCS-CN), Artificial Neural Network (ANN) and Hydrological Model (HEC-HMS)

Abstract: In the present study an attempt is made to provide empirical and deterministic modelling approach for deriving flood frequency curve in ungauged Keseke river catchment, South Nation Nationality and People (SNNP)-Ethiopia. The research work consists of (i) extracting of remote sensing data; (ii) evaluation and validation of remote sensing data; (iii) modelling of river flow using remote sensing data (climate and physiographic data) of the river catchment; (ii) three types of hydrological models validation and evaluation; (iv) developing of flood frequency model for each sub-catchment. The evaluation and validation of remote sensing data and river flow prediction is carried out on eight selected rivers in Keseke River catchment. The single gamma distribution quantile mapping is a good approximation to adjust satellite precipitation product data and the Pearson correlation function has shown a good correlation, mainly on heavy rain events. Results reveals that the SCS-CN and ANN approaches are suitable to predict river runoff in ungauged with reasonable accuracy in the investigated sub-catchments, and appears acceptable correlation between estimated and corrected satellite rainfall. A field campaign to obtain possible data was executed via interview and river cross section measures. The flood quantiles are compared with one time flow observation from field measured value (which is estimated from the river cross-section size) to identify the most representative hydrological model structure.

Erosion risk assessment through morphometric indices for prioritisation of Arjuna watershed using ALOS-PALSAR DEM

Abstract: Pressure of growing population rates and unsustainable usage of resources have greatly affected the productivity of agricultural lands due to land degradation. In a developing country such as India, degradation of land happens mainly due to the action of fluvial resources. The morphometric analysis of the drainage basin and channel network gives a detailed understanding of the geo-hydrological behaviour of drainage basin and expresses the prevailing climate, geology, geomorphology, and structural antecedents of the catchment which is very decisive in identifying regions vulnerable to soil erosion. Advancement in Remote Sensing (DEM) products and GIS techniques has made the assessment of morphometric indices more accurate, effective and less time consuming. Therefore, in this study, to assess the vulnerability of soil erosion in Arjuna watershed of vaippar basin, morphometric indices-based prioritisation of 16 sub-watersheds have been carried out using high-resolution ALOS-PALSAR DEM with a spatial resolution of 12.5 m. The linear and shape parameters which are responsible for soil erodability are assigned ranking using Compound Factor (CF) technique to arrive at the prioritisation of sub-watersheds. Based on the CF values, sub-watershed are classified into three categories of priority as high (5–7), medium (7–9), and low (9–12), respectively. The sub-watersheds such as Kamba Ittu Malai, Arjuna Nadi, Mela Gopulapuram, and Kodikulam come under the high priority category, whereas Muvaraivenran, Thaniparai, Chittar River,Vadugapatti, Perumalswamiuchi, Ayan Karisalkulam, Kurukkamuttu malai, and Satpur R.F come under the medium priority category and the Ramachandrapuram, Arjunapuram, Kovil Aru, and Periyar River sub-watersheds come under the low priority category.

Assessment of groundwater potential zones in parts of the semi-arid region of Anantapur District, Andhra Pradesh, India using GIS and AHP approach

Abstract: Increasing urbanization and the population’s demand for water shortages in urban and rural regions prompts a decrease in groundwater resources, to the need to plan sustainable development and watershed management. Remote sensing, geographic information system (GIS) and the analytical hierarchy process (AHP) are useful approaches for distinguishing possible groundwater areas of India. A total of eight sets of thematic layers have been selected to influence the storage of groundwater potential in the region concerned and a suitable weight is assigned to each factor based on the Saaty’s nine points and the weights are normalized by AHP. The groundwater potential zone (GWPZ) of the study region was prepared by integrating the different thematic layers with the support of ArcGIS software. The present study results indicates that 5.36 km2 has a very good GWP, 193.45 km2 has a good GWP, while 184.09 km2 and 12.27 km2 are under moderate and poor GWPZs respectively. The study found that a simple GIS-based AHP approach is reasonable for making precise and dependable estimates, especially if the order of the criteria used for expectation is consistent. The present study results showed the effect of consistency criteria that the ability of the method to give an accurate prediction depends on many criteria’s that are used. The study found that the basic GIS based AHP approaches is capable of producing accurate and reliable for better planning and managing the resources in an effective way.

Systematic modeling of impacts of land-use and land-cover changes on land surface temperature in Adama Zuria District, Ethiopia

Abstract: Land surface temperature (LST) has been increasing year after year globally. The present investigation was aimed to study the impact of land-use/land-cover changes on LST in the Adama Zuria District in Ethiopia. Land-use/land-cover, LST and Normalized Difference Vegetation Index were extracted from Landsat TM (1989), Landsat ETM + (1999) and Landsat 8 OLI/TIRS (2016) using GIS and remote sensing tools. Land surface temperature was assessed using split window algorithm. Land-use/land-cover changes that occurred during 1989–2016 in the study area were evaluated and analyzed using geospatial tools and verified by field data. Results indicated that farmland covered more than 60% during the study period (1989–2016) followed by shrub land (> 12%). Most areas with lower LST in 1989 were changed to higher LST in 1999 and 2016 in response to different changes in land-use/land-cover pattern. By linking land-use/land-cover pattern changes and LST using zonal statistics, LST is found to have negative relationship with the extent of vegetation cover. Land surface temperature results showed that the northwestern, south, lake Koka area and along Awash river relatively low LST that ranged between 9 and 21 °C in response to the high NDVI values. The eastern, Adama town and western part of the study area showed high LST of up to 42 °C. Visual comparison of 1989, 1999 and 2016 images showed that the land-use/land-cover type and NDVI status play a major role for the variability of LST values. Correlation between LST and land-use changes has indicated that changes to settlement/urban land-use/land-cover have influenced LST proportionately. Relevant measures are to be taken by the bodies concerned to minimize land-use/land-cover changes to gain effective control over increasing LST in the study area.

Spatial prediction of susceptibility to gully erosion in Jainti River basin, Eastern India: a comparison of information value and logistic regression models

Abstract: The research on effective methods for assessing areas susceptible to gully erosion has environmental, agricultural and economic benefits to society. This study aimed to identify the areas with high susceptibility to gully erosion using two statistical methods and compare the performances. Remote sensing and field data along with two statistical methods, information value (InfVal) and logistic regression, were applied in the analysis. Gullies, boundary demarcation and regulating erosion processes were surveyed and mapped in a GIS environment. Gully sites were split into training and validation set for modelling and validating susceptibility results. The geo-environmental variables selected, according to study area, were land use/land cover, slope steepness, slope aspect, elevation range, length-slope factor, topographical wetness and position index, stream power index, geology, soil type, rainfall erosivity index and altitude. Weights for each class of each variable were computed through InfVal method and probabilities of occurrence of 1,000,000 random locations were extracted to run the logistic regression operation. A weighted linear combination approach and an inverse distance weighted approach were applied for the InfVal and logistic regression methods, respectively, to generate the gully proneness maps. Both results were classified into five susceptibility classes and they depict 18.44% (Infval) and 12.67% (logistic regression) area of the catchment as high to very highly susceptible to gully erosion. Correlation analysis between the results revealed a coefficient value of 0.8379. For InfVal model, the success and prediction rate is 78% and 75% whereas for the logistic regression model, it is 73% and 72.9% accordingly.

Assessment of green space cooling effects in dense urban landscape: a case study of Delhi, India

Abstract: Most noticeable and direct impacts of urbanization on the environment is the changes in daily and seasonal thermal variable and increase the hydraulic stress. Both are cumulatively formed the urban heat island (UHI) effects. From the sustainability point of view, it is very crucial for research to focus on how to mitigate UHI. Therefore, objective of this study is to evaluate the cooling effects of different types of urban greenery at local and park level. For land surface temperature, Landsat 8—TIRS and for greenery mapping Sentinel 2A imageries have been used. LST was derived using the single channel algorithm, and greenery was mapping using combination of unsupervised (i.e., ISODATA) and supervised (i.e., maximum likelihood) image classification techniques. Numbers of class level metrics like PLAND PD; LPI; AREA_MN etc. used to measure composition and configuration of urban greenery. Ordinary least square regression, multiple linear regression, and spatial auto-regression model were used to measure the cooling effects of urban green space quantitatively. Besides, park level analysis also was done using park cooling intensity (PCI) and its relationship with park land use. Results shows that amount of urban greenery in terms of percentage of land cover or average size of the green patch is very important to reduce the UHI. PLAND, LPI, AREA_MN and COHESION negatively related with LST whereas PD, LSI, SHAPE_MN, and ENN_MN positively related with LST. So, fragmented vegetation area, green space with complex shape could increase the LST. If we look at the vegetation types and relation with LST, only dense vegetation and trees and plantation can effectively reduce the LST. Besides PCI negatively related with amount of built-up area and open space within parks. Finally, this study can help to the urban and land use planning to build a heat resilience city.

Transparent open-box learning network provides auditable predictions for coal gross calorific value

Abstract: Auditing and forensic analysis of how each prediction is calculated are key attributes of transparent open-box learning networks (TOB). It provides the full calculation and input metric contributions for each of the predictions it derives. There are two stages in executing TOB predictions (stage 1 matches and ranks using squared-error analysis; stage 2 optimizes and conducts sensitivity analysis). Neither stage involves generating or extrapolating correlations between the input variables. Both stages of the calculation generate accurate predictions for datasets with multiple, highly-dispersed and non-linear influencing inputs. The transparent way in which generates predictions leads to better understanding of the interplays between the input variables. Such attributes have direct relevance to the complex systems modelled in the coal industry [e.g., gas calorific value (GCV) prediction and coal petrology–grindability relationships]. The algorithm is applied here to predict GCA for a large published database (6339 records) of US coals including proximate and ultimate analysis metrics. The TOB predicts GCV with accuracy (RMSE ≤ 0.3 MJ/kg; R2 > 0.99). The transparency of the TOB method contrasts with the hidden relationships involved in many neural-network based prediction systems. Worked examples are provided to show the detailed prediction calculations associated with individual data points. The TOB approach applied to predicting coal GCV can help to verify the source of specific samples (e.g. specific mines or coal basins) using readily understandable underlying calculations available for audit and display. The TOB is therefore also suitable for identifying the provenance of specific coal samples based on proximate and/or ultimate analysis.

Landslide vulnerability assessment based on entropy method: a case study from Kegalle district, Sri Lanka

Abstract: The concept of landslide vulnerability to a given location is hard to quantify. Few studies have been carried to determine susceptibility using social and physical factors. This study is the first attempt in Sri Lanka to quantify level of vulnerability by integrating major physical and social indicators to map the spatial distribution of vulnerability. Considering the limitations of traditional weight evaluation method in calculation of the multiple indicators and ignorance of the associations among evaluating indicators, a new weight evaluation process, entropy method was introduced in this study. This improved method for determination of weight of the evaluating indicators was applied to estimate weight for the 14 selected indicators. The primary data were obtained from a comprehensive questioner survey (n = 402) of households or buildings (elements) with their coordinates based on a spatially balanced approach for ensuring spatial coverage of the entire landslide distribution. The spatial distribution of vulnerability was mapped using Kriging interpolation. According to the map, landslide vulnerabilities in the study area demonstrate notable regional specifications. Besides, the spatial distribution of vulnerability has shown a close relationship with rural and urban settlements. Results of spatial vulnerability reflect discrimination and inequalities in the development of the study area. According to landslide vulnerability analyses, 14.6% (247 km2) of the entire area is found to be the highest vulnerable zone for a landslide and 39.8% (675 km2) of area categorized under the lowest zone to vulnerability. Further, the study revealed a reasonable contribution by entropy method on analysis of social and physical indicators, which is useful for other vulnerability assessments.

Simulation of extreme event-based rainfall–runoff process of an urban catchment area using HEC-HMS

Abstract: Flood is ranked as the deadliest natural disaster that has been experienced by the urban basins in the world. Its detrimental effects can be minimized by appropriate modeling, analysis and management methods. Such modeling and analysis techniques help in flood risk assessment predicting flood occurrence, aid in the emergency preparation for evacuation and reduce damage from the impact of floods. Numerous modeling techniques are available for analyzing flood events, of which HEC-HMS software is chosen for this explorative study because of its simplicity and as it is a freely available open-source software. The present study aims to develop a rainfall–runoff simulation model by generating peak flow and volume of the extreme rainfall event that occurred on 22 November 1999 in the ungauged Koraiyar basin located south of Tiruchirappalli City in South India. The hydrographs are generated for the basin by using specified hyetograph and frequency storm method to identify the best method to be adopted in the study. Digital elevation model processed with geographic information system (GIS) and HEC-Geo HMS, which is an extension of GIS, is used for the analysis. Using the terrain processing tools in ArcGIS, the basin delineation and parameters such as slope and river length are extracted from the basin. The data generated during the HEC-Geo HMS process are the hydrologic parameters of Koraiyar basin, and it is imported to HEC-HMS modeling for generating peak flow and volume. In the modeling process, HEC-HMS has three modules, namely transform, loss and base flow. SCS curve number and SCS unit hydrograph are used to determine the losses and transformation of rainfall into the runoff process in the present study. The SCS method is adopted because of its simplicity and requirement of limited data approach for modeling. The peak flow and volume prepared from the model are compared with the standard Nash–Sutcliffe values. The frequency storm method has a Nash value of 0.7, which is higher than the value obtained from the specified hyetograph process, and it is chosen as a better model for generating flood peak and volume for different return periods in the basin. It can therefore be adopted for other studies of similar basin conditions.

Integration of GIS and statistical approach in mapping of urban sprawl and predicting future growth in Midnapore town, India

Abstract: The spatial expansion of cities appears as an accelerated phenomenon and known as urban sprawl. Usually, the exertion of creating smart growth and sustainable growth becomes in curb because urban sprawl is an unplanned and haphazard growth of urban areas. Therefore, planners should be accurately investigate the trend, patterns and directions of urban growth for sustainable management. This study highlights the existing pattern of the urban sprawl of Midnapore town from 1991 to 2017, using the Normalized Difference Built-up Index and Shannon’s entropy and simulated urban growth of 2030 by Markov chain model. Without overlooking the proviso of scientific urban research, an intensive field survey had been done to find out spatial determinants of urban expansion. Four hypotheses have been selected and factor analysis was applied with the multiple regression analysis to find out the factors of urban growth. Comparatively low land price, distribution of reclaimed land, the benefit of open space in the urban fringe and an opportunity of income are major factors of urban growth. Finally, the potential strategies have been proposed for sustainable management and conservation of the local environment.

Use of ANN models in the prediction of meteorological data

Abstract: The present study is to use artificial neural network (ANN) models for the prediction of meteorological data. Artificial neural network models namely multiple layer perceptron (MLP) and radial base function (RBF) have been used for the prediction of meteorological data. To confirm the performance of the models, the hourly and monthly predictions of variables have been compared with results obtained by multi-linear regression model, recorded by meteorological stations. The MLP and RBF have given 91–96% accuracy for predictions of all cases. In addition, the forecasts demonstrated a strong linear correlation with the data recorded in between 0.61 and 0.94. The present work has given assurance to use artificial neural network as a strong tool to predict the meteorological data. The investigation is conducted as a case study of two meteorological stations situated in India. An extension of the present study is to apply these ANNs in other regions with different data types of meteorological data will be the interest of future work.

GIS-based approach for identification of potential rainwater harvesting sites in Arsi Zone, Central Ethiopia

Abstract: Water plays a crucial role in fulfilling basic human needs, for socio-economic developments and for ecosystem services. Ethiopia is experiencing pressure on water shortage for agricultural and domestic uses. Arsi Zone frequently faces drought and crop failure due to lack of water sources. Eleven physical characteristics of the study area layers were adopted integrating multi criteria decision analysis, which uses analytical hierarchy processes with a fuzzy logic approach and geographical information system. Soil conservation service model was used to estimate the runoff depth layer of the study area. Weighting was made based on environmental, socio-economical and hydro-geological characteristics of the study area, and available literature. Results show that potential suitability class was not suitable with constraints 5769.8 km2 (27.88%), less suitable 3104.34 km2 (15%), suitable 5695.42 km2 (27.52%), very suitable 4097 km2 (19.8%) and extremely suitable 2027.38 km2 (9.8%). The area coverage of constraints were 4540.37 km2 (21.94%) of the study area. Outcome of this study emphasized the importance of geospatial modeling in assessing rainwater harvesting potential sites, proposed to assist in planning water facility and to address water scarcity problem in the study area. The model developed in this research can be used in other areas to determine the potential of rainwater harvesting and integrate rainwater as an alternative water source to ensure availability for domestic, agricultural and industrial uses. It is recommended that detailed ground validation and socio-economic factors should be analyzed to increase its effectiveness before implementation.

Hydrological modeling of water and soil resources in the basin upstream of the Allal El Fassi dam (Upper Sebou watershed, Morocco)

Abstract: The main objective of this work is to develop a hydrological model of the Upper Sebou watershed located in northwest of Morocco, by applying the agro-hydrological model SWAT (Soil and Water Assessment Tool). The modeling was carried out on a period of 14 years including 2 years as warm-up period, 5 years for calibration and 7 years for validation. The results indicated that R2 was 0.69 and NS was 0.70 in calibration period (2002–2006), while R2 was 0.61 and NS was 0.62 in validation period (2007–2013) which means this study showed a good agreement between simulated and observed monthly flow. The results obtained helped firstly to establish and understand the water balance at its different spatio-temporal scales, whose average annual precipitation during the study period is of the order of 431 mm, of which 69.3% is lost by evapotranspiration (298.7 mm). Surface runoff is approximately 37 mm, or 8.58% of the precipitation. As for the total flow in the river, it is of the order of 99.8 mm. And on other hand, this model helped to identify areas where soil loss is very high. This degradation is in fact closely related to the physical basin properties. However, the soil loss can reach a maximum value (more than 12.11 t/ha/year) upstream of the basin and a minimum value (less than 4 t/ha/year). The siltation rate of the retention dam was estimated at 2.12 Mm3/year.

Graphene oxide–tannic acid nanocomposite as an efficient adsorbent for the removal of malachite green from water samples

Abstract: Graphene oxide–tannic acid (GO–TA) nanocomposite was used as an efficient, green and rapid adsorbent for the removal of malachite green (MG) from water samples. GO was synthesized from graphite by Hummer method and modified by tannic acid to produce GO–TA nanocomposite. The results of Fourier Transform-Infrared Spectroscopy, atomic force microscopy and Brunner–Emmett–Teller show that the GO–TA nanocomposite with the surface area of 79.6 m2 g−1 has been synthesized successfully. The effect of pH, removal time, initial concentration of MG and stirring rate on adsorption capacity of MG were investigated and the experimental isotherm data were analyzed using the Langmuir and Freundlich equations. Also, two kinetic models including the pseudo first- and second-order equations were investigated and kinetic parameters were calculated and discussed. The results show that, the adsorption of MG onto the GO–TA nanocomposite followed by both Langmuir and Freundlich isotherms with a maximum theoretical adsorption capacity of 500 mg g−1 at 25 °C. Also, the results of kinetic models show that the adsorption of MG onto the GO–TA nanocomposite could be described by the pseudo first order kinetic model. Finally, based on the obtained results, it was concluded that GO–TA nanocomposite is very efficient and rapid adsorbent for the removal of MG from water samples.

Seasonal prediction of particulate matter over the steel city of India using neural network models

Abstract: The particulate matter (PM) concentration forecast is an important component to evaluate the air quality over any region of the world. The results are vital when it comes to health concern issues related to air pollution in developing countries like India and China. The present study is focused on the prediction of future concentrations of respirable suspended particulate matter (RSPM, also called PM2.5) and suspended particulate matter (SPM, also called PM10) using the artificial neural networks (ANN) over tropical inland industrial site, Rourkela. Rourkela, popularly known as steel city of India is situated at the heart of a rich mineral belt and is endowed with many small, medium and large scale industries. This study aims to develop a seasonal ANN modelling approach for prediction of RSPM and SPM for the year 2013 by using the datasets of meteorology and RSPM and SPM concentrations covered from 2009 to 2013. Four neural network models namely, wavelet-based multi-layer perceptron feed forward neural network (WMLPNN), wavelet-based recurrent neural network, multi-layer perceptron feed forward neural network (MLPNN) and recurrent neural network (RNN) are used. The networks are trained using daily data from 2009 to 2012 on a seasonal basis, and daily predictions are performed for 2013 using seasonal based trained model. Five meteorological variables [temperature (T), relative humidity (RH), boundary layer height (BLH), surface pressure (SP), wind direction (WD) and wind speed (WS)] along with the Daubechies wavelet decomposed coefficients are considered as predictor variables in the models. A lagged scheme is introduced in wind speed input vector and networks are trained and tested up to 3 days lagged wind speed variable which greatly improved the prediction accuracy. WMLPNN is found to be outperforming all other tested schemes by providing promising results among all the tested models with lag in pre-monsoon, monsoon and winter seasons and without lag in wind speed for the post-monsoon season for both RSPM and SPM, elucidating the importance of predictor wind speed in the models. The values of R2 varied from 0.6 to 0.91 for RSPM and 0.7 to 0.98 for SPM and RMSE values varied from 0.09 to 0.14 for RSPM and 0.03 to 0.13 for SPM for WMLPNN model. The proposed WMLPNN model in the present study appeared to be reliable and promising for the prediction of PM2.5 and PM10 by providing aid in passing the alerts and notices for the betterment of living beings.

Mapping and monitoring of land use dynamics with their change hotspot in North 24-Parganas district, India: a geospatial- and statistical-based approach

Abstract: Interlinking between anthropogenic activities and natural environment can be monitored through the changing pattern of land use dynamics. The present research has been completed with the amalgamation of Geographical Information System (GIS) and statistical techniques in Sundarbans contiguity North 24 Parganas district. This study aims towards unveiling the existing land use/land cover and their recent transformation pattern, rate and their change ‘hotspot’ over the entire 27-year period. In this study, integration of supervised maximum likelihood classification approach (MLCA) and post-classification comparison approach (PCCA) have been used for accumulating the dynamic information regarding the land use dynamics. The result undoubtedly indicates that the built-up area had been drastically increased and vegetation area had been extremely decreased. Transition matrix shows that the maximum agricultural land was converted into a built-up area and water bodies at the same time agriculture have lost maximum area and built up gained maximum area. However, Moran’s I and Getis–Ord (Gi*) statistic indicate that most of the hotspot have been found in built-up area spacially in the western and south-western part of the district. The overall accuracy of the classification is an acceptable range (> 85%). Finally, this study concludes, the present trend of existing land use and land cover should be monitored for the preservation of standing vegetation, control the lopsided growth of built-up area and natural resource to maintain the natural ecosystem. The potential transformation among the land use classes is imperative towards the planning for sustainable land resource management, appropriate land use for the exact purpose, and potential development in this area.

Mathematical modeling and use of orbital products in the environmental degradation of the Araripe Forest in the Brazilian Northeast

Abstract: Vegetation cover is indispensable in the process of inhibiting environmental degradation. In the Northeast of Brazil, especially in the Araripe Nacional Forest (FLONA), this problem is related to the removal of vegetation for industrial and domestic use, in addition to the expansion of livestock. Thus, the objective of this work was to evaluate the environmental degradation in the area of FLONA from orbital products via remote sensing with the aid of mathematical modeling. For this, two orbital images of the orbit 65, point 217 were used for processing and obtaining the variables: (1st) July 7, 2003 from TM/Landsat-5 and (2nd) July 15, 2018 from OLI/Landsat-8. In mathematical modeling, the multiple linear regression (MRL) model was applied to the orbital products: land surface temperature (LST), normalized burn ratio (NBR), Normalized Difference Moisture Index, Normalized Difference Water Index (NDWI) to estimate the Soil Adjusted Vegetation Index (SAVI) and hence to predict the Normalized Difference Vegetation Index (NDVI). All the processing to obtain the results was carried out in the software R version 3.4-1. O NDVI pointed out a significant increase of 72.05% in dense vegetation, from 158.33 to 272.40 km2. However, vegetation is more likely to suffer from stress due to the increase in LST at 5 °C, which increased from 17.5 to 25.0 °C, reaching its highest value of 42 °C in July 2011. The MRL results indicated that the models have an excellent predictive capacity in the estimation of degradation, with R2 value greater than 92% of the explained variance. In addition, the MAE and root mean square error were less than 0.03 for both models. The models pointed out that SAVI, NBR and NDWI are responsible for the variability of NDVI in environmental degradation of FLONA. Highlight for the theoretical-conceptual model that can be applied to any semi-arid and highly-sensitive region to changes in the rainfall pattern.

Changes and variability of precipitation and temperature in the Guna Tana watershed, Upper Blue Nile Basin, Ethiopia

Abstract: This paper studied the spatial and temporal variability of the statistical structures of precipitation Guna Tana watershed, Upper Blue Nile, Ethiopia, by analyzing the time series of temperature and precipitation from six weather stations during the period from 1990 to 2016. Inverse distance weight, precipitation concentration index and MK test statics were used to detect annual and seasonal precipitation concentrations and the associated spatial patterns. The results show that precipitation concentration index values were mainly observed in Guna Tana watershed in which about 83.33% and 16.67% were the uniform concentration of precipitation and strong irregularity of precipitation distribution was observed in the kiremt seasons’ rainfall. The demonstration using Mann–Kendall trend test depicted that most parts of Guna Tana watershed are characterized by variability of precipitation and temperature. The results reveal that significant trends in average rainfall were observed (both positive and negative trends). Those significantly decreasing trends of average monsoon rainfall have the highest value of decreasing slope (i.e., − 1.88 mm/year) for Luwaye station and percentage change of − 31.07% in Bega season. Decreasing slopes (− 0.14 °C/year) and percentage change (− 22.79%) were observed in Luwaye station at the average annual minimum temperature and increasing Sen’s slope temperature recorded at 24.64 °C/year and percentage changes at 0.136% in 10% level of significance at Woreta station in average annual minimum temperature.