This study evaluated and compared groundwater spring potential maps produced with two different models—namely multivariate adaptive regression spline (MARS) and random forest (RF)—using geographic information system (GIS). In total, 234 spring locations were identified in the Boujnord, North Khorasan, Iran and a GIS spring inventory map was prepared. Of these, 176 (70 %) locations were employed to produce spring potential maps (training), while the remaining 58 (30 %) cases were used to validate the model. The explanatory variables used to predict spring location were altitude, slope aspect, slope degree, slope length, topographic wetness index (TWI), plan curvature, profile curvature, land use, lithology, distance to rivers, drainage density, distance to faults, and fault density. Furthermore, the spatial relationships between spring occurrence and explanatory variables were performed using a Certainty Factor (CF) model. For validation, area under a receiver operating characteristics (ROC) curves (AUC) was used. The validation results showed that the AUC for calibration is almost identical (0.79) in both models, while for prediction, the MARS model (73.26 %) performed better than RF (70.98 %) model. These results indicate that the MARS and RF models are good estimators of groundwater spring potential in the study area. These groundwater spring potential maps can be applied to groundwater management and groundwater resource exploration.

GIS-based multivariate adaptive regression spline and random forest models for groundwater potential mapping in Iran

Groundwater is a vital water source in the rural and urban areas of developing and developed nations. In this study, a novel hybrid integration approach of Fisher’s linear discriminant function (FLDA) with rotation forest (RFLDA) and bagging (BFLDA) ensembles was used for groundwater potential assessment at the Ningtiaota area in Shaanxi, China. A spatial database with 66 groundwater spring locations and 14 groundwater spring contributing factors was prepared; these factors were elevation, aspect, slope, plan and profile curvatures, sediment transport index, stream power index, topographic wetness index, distance to roads and streams, land use, lithology, soil and normalized difference vegetation index. The classifier attribute evaluation method based on the FLDA model was implemented to test the predictive competence of the mentioned contributing factors. The area under curve, confidence interval at 95%, standard error, Friedman test and Wilcoxon signed-rank test were used to compare and validate the success and prediction competence of the three applied models. According to the achieved results, the BFLDA model showed the most prediction competence, followed by the RFLDA and FLDA models, respectively. The resulting groundwater spring potential maps can be used for groundwater development plans and land use planning.

/pdf/novel-hybrid-integration-approach-of-bagging-based-fisher-s-4pqbgejc27.pdf

Novel Hybrid Integration Approach of Bagging-Based Fisher’s Linear Discriminant Function for Groundwater Potential Analysis

. Groundwater is one of the most valuable natural resources in the world (Jha
et al., 2007). However, it is not an unlimited resource; therefore
understanding groundwater potential is crucial to ensure its sustainable use.
The aim of the current study is to propose and verify new artificial
intelligence methods for the spatial prediction of groundwater spring
potential mapping at the Koohdasht–Nourabad plain, Lorestan province, Iran.
These methods are new hybrids of an adaptive neuro-fuzzy inference system
(ANFIS) and five metaheuristic algorithms, namely invasive weed optimization
(IWO), differential evolution (DE), firefly algorithm (FA), particle swarm
optimization (PSO), and the bees algorithm (BA). A total of 2463 spring
locations were identified and collected, and then divided randomly into two
subsets: 70 % (1725 locations) were used for training models and the
remaining 30 % (738 spring locations) were utilized for evaluating the
models. A total of 13 groundwater conditioning factors were prepared for
modeling, namely the slope degree, slope aspect, altitude, plan curvature,
stream power index (SPI), topographic wetness index (TWI), terrain roughness
index (TRI), distance from fault, distance from river, land use/land cover,
rainfall, soil order, and lithology. In the next step, the step-wise
assessment ratio analysis (SWARA) method was applied to quantify the degree
of relevance of these groundwater conditioning factors. The global
performance of these derived models was assessed using the area under the
curve (AUC). In addition, the Friedman and Wilcoxon signed-rank tests were
carried out to check and confirm the best model to use in this study. The
result showed that all models have a high prediction performance; however,
the ANFIS–DE model has the highest prediction capability (AUC  =  0.875),
followed by the ANFIS–IWO model, the ANFIS–FA model (0.873), the ANFIS–PSO
model (0.865), and the ANFIS–BA model (0.839). The results of this research
can be useful for decision makers responsible for the sustainable management
of groundwater resources.

/pdf/spatial-prediction-of-groundwater-spring-potential-mapping-19c8mk4r18.pdf

Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization

This study mapped and analyzed groundwater potential using two different models, logistic regression (LR) and multivariate adaptive regression splines (MARS), and compared the results. A spatial database was constructed for groundwater well data and groundwater influence factors. Groundwater well data with a high potential yield of ≥70 m3/d were extracted, and 859 locations (70%) were used for model training, whereas the other 365 locations (30%) were used for model validation. We analyzed 16 groundwater influence factors including altitude, slope degree, slope aspect, plan curvature, profile curvature, topographic wetness index, stream power index, sediment transport index, distance from drainage, drainage density, lithology, distance from fault, fault density, distance from lineament, lineament density, and land cover. Groundwater potential maps (GPMs) were constructed using LR and MARS models and tested using a receiver operating characteristics curve. Based on this analysis, the area under the curve (AUC) for the success rate curve of GPMs created using the MARS and LR models was 0.867 and 0.838, and the AUC for the prediction rate curve was 0.836 and 0.801, respectively. This implies that the MARS model is useful and effective for groundwater potential analysis in the study area.

Evaluation of Logistic Regression and Multivariate Adaptive Regression Spline Models for Groundwater Potential Mapping Using R and GIS

Groundwater potential assessment of an alluvial aquifer in Yamuna sub-basin (Panipat region) using remote sensing and GIS techniques in conjunction with analytical hierarchy process (AHP) and catastrophe theory (CT)

Impacts of climate change on streamflow and sediment concentration under RCP 4.5 and 8.5: A case study in Purna river basin, India.

Groundwater is an important resource contributing significantly in total annual supply. However, overexploitation has depleted groundwater availability considerably and also led to land subsidence at some places. Assessing the potential zone of groundwater recharge is extremely important for the protection of water quality and the management of groundwater systems. Groundwater potential zones are demarked with the help of remote sensing and Geographic Information System (GIS) techniques. In this study a standard methodology is proposed to determine groundwater potential using integration of RS & GIS technique. The composite map is generated using GIS tools. The accurate information to obtain the parameters that can be considered for identifying the groundwater potential zone such as geology, slope, drainage density, geomorphic units and lineament density are generated using the satellite data and survey of India (SOI) toposheets of scale 1:50000. It is then integrated with weighted overlay in ArcGIS. Suitable ranks are assigned for each category of these parameters. For the various geomorphic units, weight factors are decided based on their capability to store groundwater. This procedure is repeated for all the other layers and resultant layers are reclassified. The groundwater potential zones are classified into five categories like very poor, poor, moderate, good & excellent. The use of suggested methodology is demonstrated for a selected study area in Parbhani district of Maharashtra. This groundwater potential information will be useful for effective identification of suitable locations for extraction of water.

/pdf/identification-of-groundwater-potential-zoneusing-remote-1ftcoee3b3.pdf

Identification of Groundwater Potential Zoneusing Remote Sensing and GIS Technique

A semi-distributed, physically based, basin-scale Soil and Water Assessment Tool (SWAT) model was developed to determine the key factors that influence streamflow and sediment concentration in Purna river basin in India and to determine the potential impacts of future climate and land use changes on these factors. A SWAT domain with a Geographical Information System (GIS) was utilized for simulating and determining monthly streamflow and sediment concentration for the period 1980–2005 with a calibration period of 1980–1994 and validation period of 1995 to 2005. Additionally, a sequential uncertainty fitting (SUFI-2) method within SWAT-CUP was used for calibration and validation purpose. The overall performance of the SWAT model was assessed using the coefficient of determination (R2) and Nash–Sutcliffe efficiency parameter (ENS) for both calibration and validation. For the calibration period, the R2 and ENS values were determined to be 0.91 and 0.91, respectively. For the validation period, the R2 and ENS were determined to be 0.83 and 0.82, respectively. The model performed equally well with observed sediment data in the basin, with the R2 and ENS determined to be 0.80 and 0.75 for the calibration period and 0.75 and 0.65 for the validation, respectively. The projected precipitation and temperature show an increasing trend compared to the baseline condition. The study indicates that SWAT is capable of simulating long-term hydrological processes in the Purna river basin.

Use of SWAT to determine the effects of climate and land use changes on streamflow and sediment concentration in the Purna River basin, India

Soil moisture is placed at the interface between land and atmosphere which influences water and energy flux However, soil moisture information has a significant importance in hydrological modelling and environmental processes Recent advances in acquiring soil moisture from the satellite and its effective utilization provide an alternative to the conventional soil moisture methods In this study, an attempt is made to apply physically based, distributed-parameter, Soil and Water Assessment Tool (SWAT) to validate Advanced Microwave Scanning Radiometer (AMSR2) soil moisture in parts of Puerto Rico For this, calibration is performed for the years 2010 to 2012 with known observed discharge sites, Rio Gunajibo and Rio Grande de Anasco in Puerto Rico and validation, with the observed stream flow for the year 2013 using the AMSR2 soil moisture Moreover, the SWAT and AMSR2 soil moisture outcome are compared on a monthly basis The model capability and performance in simulating the stream flow are evaluated utilizing the statistical method The results indicated a negligible difference in SWAT soil moisture and AMSR2 soil moisture for stream flow estimation Finally, the model retrievals show a satisfactory agreement between observed and simulated streamflow

Satellite Soil Moisture Validation Using Hydrological SWAT Model: A Case Study of Puerto Rico, USA

Storm water plays an important role in analysis, design and planning of drains in case of rapidly growing urbanization. The change in rainfall pattern and intensity is becoming a great concern for hydrologic engineers and planners. The rainfall Intensity-Duration-Frequency (IDF) curves are commonly used in storm water management, and other engineering design applications across the world and these curves are developed based on historical rainfall time series data by fitting a theoretical probability distribution to extreme rainfall series. In recent years, it has been found that extreme precipitation events are increasing due to global climate change. Also, due to population and property concentration in a relatively urban developing area, the flood damage potential in urban areas is high, and the extreme rainfall events are the main cause for urban floods. Therefore, it is important to study the climate change impacts on rainfall IDF curves of a developing urban area. In this study, with the help of 16 global climate (GCMs) simulations, reliability ensemble average (REA) method and Gumbel’s distribution type-I, the impacts of climate change on rainfall IDF curves of study area-Bidkin (DMIC) Aurangabad (MS), India are studied for two future time periods (2016–2057) and (2058–2100), IDF curve is developed for two future time periods. Results of this study indicate that climate change is increasing extreme rainfall events of the study area. Also, it is also observed that the return of period of an extreme rainfall of the study area is reducing and noted that the IDF curve developed by modeling trend in the observed extreme rainfall with covariate is an appropriate choice for designing the study area infrastructure under climate change.

Milind L. Waikar

Papers

Impacts of climate change on streamflow and sediment concentration under RCP 4.5 and 8.5: A case study in Purna river basin, India.

Identification of Groundwater Potential Zoneusing Remote Sensing and GIS Technique

Use of SWAT to determine the effects of climate and land use changes on streamflow and sediment concentration in the Purna River basin, India

Satellite Soil Moisture Validation Using Hydrological SWAT Model: A Case Study of Puerto Rico, USA

Precipitation Intensity-Duration-Frequency Curves Under Changing Climate on Developing Urban Catchment – Aurangabad (Ms), India