Showing papers in "Stochastic Environmental Research and Risk Assessment in 2018"

Monthly runoff forecasting based on LSTM–ALO model

Abstract: Accurate runoff forecasting plays an important role in management and utilization of water resources. This paper investigates the accuracy of hybrid long short-term memory neural network and ant lion optimizer model (LSTM–ALO) in prediction of monthly runoff. As the parameters of long short-term memory neural network (LSTM) have influence on the prediction performance, the parameters of the LSTM are calibrated by using ant lion optimizer. Then the selection of suitable input variables of the LSTM–ALO is discussed for monthly runoff forecasting. Finally, we decompose root mean square error into three parts, which can help us better understanding the origin of differences between the observed and predicted runoff. To test the merits of the LSTM–ALO for monthly runoff forecasting, other models are employed to compare with the LSTM–ALO. The scatter-plots and box-plots are adopted for evaluating the performance of all models. In the case study, simulation results with the historical monthly runoff of the Astor River Basin show that the LSTM–ALO model has higher accuracy than that of other models. Therefore, the proposed LSTM–ALO provides an effective method for monthly runoff forecasting.

Non-crossing nonlinear regression quantiles by monotone composite quantile regression neural network, with application to rainfall extremes

Abstract: The goal of quantile regression is to estimate conditional quantiles for specified values of quantile probability using linear or nonlinear regression equations. These estimates are prone to “quantile crossing”, where regression predictions for different quantile probabilities do not increase as probability increases. In the context of the environmental sciences, this could, for example, lead to estimates of the magnitude of a 10-year return period rainstorm that exceed the 20-year storm, or similar nonphysical results. This problem, as well as the potential for overfitting, is exacerbated for small to moderate sample sizes and for nonlinear quantile regression models. As a remedy, this study introduces a novel nonlinear quantile regression model, the monotone composite quantile regression neural network (MCQRNN), that (1) simultaneously estimates multiple non-crossing, nonlinear conditional quantile functions; (2) allows for optional monotonicity, positivity/non-negativity, and generalized additive model constraints; and (3) can be adapted to estimate standard least-squares regression and non-crossing expectile regression functions. First, the MCQRNN model is evaluated on synthetic data from multiple functions and error distributions using Monte Carlo simulations. MCQRNN outperforms the benchmark models, especially for non-normal error distributions. Next, the MCQRNN model is applied to real-world climate data by estimating rainfall Intensity–Duration–Frequency (IDF) curves at locations in Canada. IDF curves summarize the relationship between the intensity and occurrence frequency of extreme rainfall over storm durations ranging from minutes to a day. Because annual maximum rainfall intensity is a non-negative quantity that should increase monotonically as the occurrence frequency and storm duration decrease, monotonicity and non-negativity constraints are key constraints in IDF curve estimation. In comparison to standard QRNN models, the ability of the MCQRNN model to incorporate these constraints, in addition to non-crossing, leads to more robust and realistic estimates of extreme rainfall.

Implementation of a hybrid MLP-FFA model for water level prediction of Lake Egirdir, Turkey

Abstract: The predictive ability of a hybrid model integrating the Firefly Algorithm (FFA), as a heuristic optimization tool with the Multilayer Perceptron (MLP-FFA) algorithm for the prediction of water level in Lake Egirdir, Turkey, is investigated. The accuracy of the hybrid MLP-FFA model is then evaluated against the standalone MLP-based model developed with the Levenberg–Marquadt optimization scheme applied for in the backpropagation-based learning process. To develop and investigate the veracity of the proposed hybrid MLP-FFA model, monthly time scale water level data for 56 years (1961–2016) are applied to train and test the hybrid model. The input combinations of the standalone and the hybrid predictive models are determined in accordance with the Average Mutual Information computed from the historical water level (training) data; generating four statistically significant lagged combinations of historical data to be adopted for the 1-month forecasting of lake water level. The proposed hybrid MLP-FFA model is evaluated with statistical score metrics: Nash–Sutcliffe efficiency, root mean square and mean absolute error, Wilmott’s Index and Taylor diagram developed in the testing phase. The analysis of the results showed that the hybrid MLP–FFA4 model (where 4 months of lagged combinations of lake water level data are utilized) performed more accurately than the standalone MLP4 model. For the fully optimized hybrid (MLP-FFA4) model evaluated in the testing phase, the Willmott’s Index was approximately 0.999 relative to 0.988 (MLP 4) and the root mean square error was approximately 0.029 m and compared to 0.102 m. Moreover, the inter-comparison of the forecasted and the observed data with various other performance metrics (including the Taylor diagram) verified the robustness of the proposed hybrid MLP-FFA4 model over the standalone MLP4 model applied in the problem of forecasting lake water level prediction in the current semi-arid region in Turkey.

Multi-step water quality forecasting using a boosting ensemble multi-wavelet extreme learning machine model

Abstract: The use of electrical conductivity (EC) as a water quality indicator is useful for estimating the mineralization and salinity of water. The objectives of this study were to explore, for the first time, extreme learning machine (ELM) and wavelet-extreme learning machine hybrid (WA-ELM) models to forecast multi-step-ahead EC and to employ an integrated method to combine the advantages of WA-ELM models, which utilized the boosting ensemble method. For comparative purposes, an adaptive neuro-fuzzy inference system (ANFIS) model, and a WA-ANFIS model, were also developed. The study area was the Aji-Chay River at the Akhula hydrometric station in Northwestern Iran. A total of 315 monthly EC (µS/cm) datasets (1984–2011) were used, in which the first 284 datasets (90% of total datasets) were considered for training and the remaining 31 (10% of total datasets) were used for model testing. Autocorrelation function (ACF) and partial autocorrelation function (PACF) demonstrated that the 6-month lags were potential input time lags. The results illustrated that the single ELM and ANFIS models were unable to forecast the multi-step-ahead EC in terms of root mean square error (RMSE), coefficient of determination (R2) and Nash–Sutcliffe model efficiency coefficient (NSC). To develop the hybrid WA-ELM and WA-ANFIS models, the original time series of lags as inputs, and time series of 1, 2 and 3 month-step-ahead EC values as outputs, were decomposed into several sub-time series using different maximal overlap discrete wavelet transform (MODWT) functions, namely Daubechies, Symlet, Haar and Coiflet of different orders at level three. These sub-time series were then used in the ELM and ANFIS models as an input dataset to forecast the multi-step-ahead EC. The results indicated that single WA-ELM and WA-ANFIS models performed better than any ELM and ANFIS models. Also, WA-ELM models outperformed WA-ANFIS models. To develop the boosting multi-WA-ELM and multi-WA-ANFIS ensemble models, a least squares boosting (LSBoost) algorithm was used. The results showed that boosting multi-WA-ELM and multi-WA-ANFIS ensemble models outperformed the individual WA-ELM and WA-ANFIS models.

Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster

Abstract: We develop a stochastic modeling approach based on spatial point processes of log-Gaussian Cox type for a collection of around 5000 landslide events provoked by a precipitation trigger in Sicily, Italy. Through the embedding into a hierarchical Bayesian estimation framework, we can use the integrated nested Laplace approximation methodology to make inference and obtain the posterior estimates of spatially distributed covariate and random effects. Several mapping units are useful to partition a given study area in landslide prediction studies. These units hierarchically subdivide the geographic space from the highest grid-based resolution to the stronger morphodynamic-oriented slope units. Here we integrate both mapping units into a single hierarchical model, by treating the landslide triggering locations as a random point pattern. This approach diverges fundamentally from the unanimously used presence–absence structure for areal units since we focus on modeling the expected landslide count jointly within the two mapping units. Predicting this landslide intensity provides more detailed and complete information as compared to the classically used susceptibility mapping approach based on relative probabilities. To illustrate the model’s versatility, we compute absolute probability maps of landslide occurrences and check their predictive power over space. While the landslide community typically produces spatial predictive models for landslides only in the sense that covariates are spatially distributed, no actual spatial dependence has been explicitly integrated so far. Our novel approach features a spatial latent effect defined at the slope unit level, allowing us to assess the spatial influence that remains unexplained by the covariates in the model. For rainfall-induced landslides in regions where the raingauge network is not sufficient to capture the spatial distribution of the triggering precipitation event, this latent effect provides valuable imaging support on the unobserved rainfall pattern.

Species distribution modeling: a statistical review with focus in spatio-temporal issues

Abstract: The use of complex statistical models has recently increased substantially in the context of species distribution behavior. This complexity has made the inferential and predictive processes challenging to perform. The Bayesian approach has become a good option to deal with these models due to the ease with which prior information can be incorporated along with the fact that it provides a more realistic and accurate estimation of uncertainty. In this paper, we first review the sources of information and different approaches (frequentist and Bayesian) to model the distribution of a species. We also discuss the Integrated Nested Laplace approximation as a tool with which to obtain marginal posterior distributions of the parameters involved in these models. We finally discuss some important statistical issues that arise when researchers use species data: the presence of a temporal effect (presenting different spatial and spatio-temporal structures), preferential sampling, spatial misalignment, non-stationarity, imperfect detection, and the excess of zeros.

Stochastic synthesis approximating any process dependence and distribution

Abstract: An extension of the symmetric-moving-average (SMA) scheme is presented for stochastic synthesis of a stationary process for approximating any dependence structure and marginal distribution. The extended SMA model can exactly preserve an arbitrary second-order structure as well as the high order moments of a process, thus enabling a better approximation of any type of dependence (through the second-order statistics) and marginal distribution function (through statistical moments), respectively. Interestingly, by explicitly preserving the coefficient of kurtosis, it can also simulate certain aspects of intermittency, often characterizing the geophysical processes. Several applications with alternative hypothetical marginal distributions, as well as with real world processes, such as precipitation, wind speed and grid-turbulence, highlight the scheme’s wide range of applicability in stochastic generation and Monte-Carlo analysis. Particular emphasis is given on turbulence, in an attempt to simulate in a simple way several of its characteristics regarded as puzzles.

In spatio-temporal disease mapping models, identifiability constraints affect PQL and INLA results

Abstract: Disease mapping studies the distribution of relative risks or rates in space and time, and typically relies on generalized linear mixed models (GLMMs) including fixed effects and spatial, temporal, and spatio-temporal random effects. These GLMMs are typically not identifiable and constraints are required to achieve sensible results. However, automatic specification of constraints can sometimes lead to misleading results. In particular, the penalized quasi-likelihood fitting technique automatically centers the random effects even when this is not necessary. In the Bayesian approach, the recently-introduced integrated nested Laplace approximations computing technique can also produce wrong results if constraints are not well-specified. In this paper the spatial, temporal, and spatio-temporal interaction random effects are reparameterized using the spectral decompositions of their precision matrices to establish the appropriate identifiability constraints. Breast cancer mortality data from Spain is used to illustrate the ideas.

Bayesian maximum entropy approach and its applications: a review

Abstract: The present paper reviews the conceptual framework and development of the Bayesian Maximum Entropy (BME) approach. BME has been considered as a significant breakthrough and contribution to applied stochastics by introducing an improved, knowledge-based modeling framework for spatial and spatiotemporal information. In this work, one objective is the overview of distinct BME features. By offering a foundation free of restrictive assumptions that limit comparable techniques, an ability to integrate a variety of prior knowledge bases, and rigorous accounting for both exact and uncertain data, the BME approach was coined as introducing modern spatiotemporal geostatistics. A second objective is to illustrate BME applications and adoption within numerous different scientific disciplines. We summarize examples and real-world studies that encompass the perspective of science of the total environment, including atmosphere, lithosphere, hydrosphere, and ecosphere, while also noting applications that extend beyond these fields. The broad-ranging application track suggests BME as an established, valuable tool for predictive spatial and space–time analysis and mapping. This review concludes with the present status of BME, and tentative paths for future methodological research, enhancements, and extensions.

Effect of projected climate change on the hydrological regime of the Yangtze River Basin, China

Abstract: The hydrological response to the potential future climate change in Yangtze River Basin (YRB), China, was assessed by using an ensemble of 54 climate change simulations. The Coupled Model Intercomparison Project 5 simulations under two new Representative Concentration Pathways (RCP) 4.5 and 8.5 emission scenarios were downscaled and used to drive the Variable Infiltration Capacity hydrological model. This study found that the range of temperature changes is homogeneous for almost the entire region, with an average annual increase of more than 2 °C under RCP4.5 and even more than 4 °C under RCP8.5 in the end of the twenty first century. The warmest period (June–July–August) of the year would experience lower changes than the colder ones (December–January–February). Overall, mean precipitation was projected to increase slightly in YRB, with large dispersion among different global climate models, especially during the dry season months. These phenomena lead to changes in future streamflow for three mainstream hydrological stations (Cuntan, Yichang, and Datong), with slightly increasing annual average streamflows, especially at the end of twenty first century. Compared with the percentage change of mean flow, the high flow shows (90th percentile on the probability of no exceedance) a higher increasing trend and the low flow (10th percentile) shows a decreasing trend or lower increasing trend. The maximum daily discharges with 5, 10, 15, and 30-year return periods show an increasing trend in most sub-basins in the future. Therefore, extreme hydrological events (e.g., floods and droughts) will increase significantly, although the annual mean streamflow shows insignificant change. The findings of this study would provide scientific supports to implement the integrated adaptive water resource management for climate change at regional scales in the YRB.

Assessment of the impact of climate change on flow regime at multiple temporal scales and potential ecological implications in an alpine river

Abstract: The source region of Yellow river is an alpine river sensitive to climate changes, but the potential effects of climate change on hydrological regime characteristics and ecological implications are less understood. This study aims to assess the response of the alterations in the flow regimes over the source region of Yellow river to climate change using Soil and Water Integrated Model driven by different Global Circulation Models (GFDL-ESM2M, IPSL-CM5A-LR and MIROC-ESM-CHEM) under three Representative Concentration Pathway emission scenarios (RCP2.6, RCP4.5 and RCP8.5). Indicators of hydrological alteration and River impact index are employed to evaluate streamflow regime alterations at multiple temporal scales. Results show that the magnitude of monthly and annual streamflow except May, the magnitude and duration of the annual extreme, and the number of reversals are projected to increase in the near future period (2020–2049) and far future period (2070–2099) compared to the baseline period (1971–2000). The timing of annual maximum flows is expected to shift backwards. The source region of Yellow river is expected to undergo low change degree as per the scenarios RCP2.6 for both two future periods and under the scenarios RCP4.5 for the near future period, whereas high change degree under RCP4.5 and RCP8.5 in the far period on the daily scale. On the monthly scale, climate changes mainly have effects on river flow magnitude and timing. The basin would suffer an incipient impact alteration in the far period under RCP4.5 and RCP8.5, while low impact in other scenarios. These changes in flow regimes could have several positive impacts on aquatic ecosystems in the near period but more detrimental effects in the far period.

Spatio-temporal analysis of daily, seasonal and annual precipitation concentration in Jharkhand state, India

Abstract: Nowadays, climate change and global warming have led to changes in the distribution of precipitation, which affect on the availability of water resources. Therefore, investigating the temporal and spatial variations of precipitation in the previous period is highly important in the future planning for flood control and local management of water resources. Considering the importance of this issue, in the present study, the precipitation concentration indices have been used for analysing precipitation changes at daily, seasonal, and annual time scales in the period of 1971 to 2011 over the Jharkhand state, India. Also, Modified Mann–Kendall test has used to study the trend of precipitation concentration indices in annual and seasonal time scales. The result shows a highly irregular and non-uniform distribution in the annual scale. For the seasonal scale an irregular and non-uniform distribution has been also observed, although the summer had a better situation than other seasons. For daily scale, none of the stations had a regular concentration and in the northeast and southern parts of the study area, there have been more irregularities. Furthermore, the results of investigating annual precipitation trend showed a combination of increasing and decreasing trend over the study area. The results of this study can be applied to manage water supplies, drainage projects, construct collection structures of urban flood, develop plans to prevent soil erosion, and designing appropriate plans to cope with drought conditions.

Development of regional flood frequency analysis techniques using generalized additive models for Australia

Abstract: Estimation of flood quantiles in ungauged catchments is a common problem in hydrology. For this, the log-linear regression model is widely adopted. However, in many cases, a simple log transformation may not be able to capture the complexity and nonlinearity in flood generation processes. This paper develops generalized additive model (GAM) to deal with nonlinearity between the dependent and predictor variables in regional flood frequency analysis (RFFA) problems. The data from 85 gauged catchments from New South Wales State in Australia is used to compare the performances of a number of alternative RFFA methods with respect to variable selection, variable transformation and delineation of regions. Four RFFA methods are compared in this study: GAM with fixed region, log-linear model, canonical correlation analysis (to form neighbourhood in the space catchment attributes) and region-of-influence approach. Based on the outcome from a leave-one-out validation approach, it has been found that the GAM method generally outperforms the other methods even without linking GAM with a neighbourhood/region-of-influence approach. The main strength of GAM is that it captures the non-linearity between the dependent and predictor variables without any restrictive assumption. The findings of this study will encourage other researchers worldwide to apply GAM in RFFA studies, allowing development of more flexible and realistic RFFA models and their wider adoption in practice.

Estimation of carbon stock for greenhouse gas emissions from hydropower reservoirs

Abstract: The identification and accurate quantification of sources or sinks of greenhouse gas (GHG) have become a key challenge for scientists and policymakers working on climate change. The creation of a hydropower reservoir, while damming a river for power generation, converts the terrestrial ecosystems into aquatic and subsequently aerobic and anaerobic decomposition of flooded terrestrial soil organic matter resulting in the emission of significant quantity of GHG to the atmosphere. Tropical/subtropical hydropower reservoirs are more significant sources of GHG compared to boreal or temperate one. This paper aims to estimate the emission factor (gCO2eq./kWh) and net GHG emission from Koteshwar hydropower reservoir in Uttarakhand, India. Further, estimated GHG are compared with those from global reservoirs located in the same eco-region so that its impact could be timely minimized/mitigated. Results have shown that emission factor and net GHG emission of Koteshwar reservoir are, respectively, estimated as 13.87 gCO2eq./kWh and 167.70 Gg C year−1 which are less than other global reservoirs located in the same eco-region. This information could be helpful for the hydropower industries to construct reservoirs in tropical eco-regions.

Spatiotemporal characteristics of droughts and floods in northeastern China and their impacts on agriculture

Abstract: Heilongjiang Province is a major grain production base in China, and its agricultural development plays an important role in China’s social economy. Drought and flood events are the primary disasters in Heilongjiang Province and have considerable impacts on agriculture. In this study, relatively complete monthly precipitation data from 26 meteorological stations in Heilongjiang Province during the period of 1958–2013 were analyzed using the standardized precipitation index (SPI) combined with principal component analysis, Mann–Kendall trend analysis and Morlet wavelet analysis to determine the spatial and temporal distributions of drought and flood events in this province. The results were as follows: (1) the whole of Heilongjiang exhibited an aridity trend. In northern Heilongjiang, spring and summer experienced a wetting trend, and autumn and winter experienced an aridity trend. (2) The SPI3 exhibited 8- and 16-year periodic variation characteristics in spring, 10- and 22-year periodic variation characteristics in summer, and 10- and 32-year periodic variation characteristics in autumn. In addition to the 10-year periodic variation characteristics in winter, other periodic variation characteristics were observed. (3) The increasing trend in the percentage of stations affected by flood was more obvious than that affected by drought. Therefore, Heilongjiang Province is more vulnerable to flooding. (4) The influence of drought and flood disasters in Heilongjiang Province showed a growth trend, but the flood effect was more remarkable. (5) The agricultural area affected by drought and flood disasters in Heilongjiang Province showed an increasing trend. Although there was a greater increase in flood disaster area, the main types of disasters were drought-dominated.

Assessment of the Kraków inhabitants’ health risk caused by the exposure to inhalation of outdoor air contaminants

Abstract: Health risk associated with the exposure to the polluted atmospheric air inhalation was estimated for the residents of Krakow, Poland. The air pollution concentration data were obtained from the air-quality monitoring system of the city in 2007–2016. The carcinogenic risk of the studied subpopulations was not acceptable under the formula of C6H6 > BaP > As(PM10) > Cd(PM10) > Pb(PM10) > Ni(PM10). The total carcinogenic risk (Rt) amounted to 3.04E−04 for children, 2.22E−04 for infants, 1.45E−04 for women, and 1.22E−04 for men. The same risk was calculated for the top three locations of the monitoring stations in this respect, within the city of Krakow: Kurdwanow Housing Estate, Nowa Huta district, and Krasinskiego Av. Non-carcinogenic risk in the case of all six monitoring stations and in respect of all the studied subpopulations, resulting from the exposure to PM10 and for NO2 for all stations in case of children and infants, as well as, for adults at Krasinskiego Av. and Dietla Str. stations was rated medium. For C6H6 in the case of adults, children, and infants the risk was rated low. The total risk (HI) of non-carcinogenic pollution was rated medium and ranged as follows: 6.53 for children, 4.70 for infants, 3.19 for women, and 2.67 for men. That type of risk was decreasing at the station locations as follows: Krasinskiego Av. > Dietla Str. > Nowa Huta district > Kurdwanow Housing Estate > Zloty Rog Str. > Piastow Housing Estate.

Water quality variations in different climates of Iran: toward modeling total dissolved solid using soft computing techniques

Abstract: In present paper, wavelet analysis of total dissolved solid that monitored at Nazlu Chay (northwest of Iran), Tajan (north of Iran), Zayandeh Rud (central of Iran) and Helleh (south of Iran) basins with various climatic conditions, have been studied. Daubechies wavelet at suitable level (db4) has been calculated for TDS of each selected basins. The performance of artificial neural networks (ANN), two different adaptive-neurofuzzy inference system (ANFIS) including ANFIS with grid partition (ANFIS-GP) and ANFIS with subtractive clustering (ANFIS-SC), gene expression programming (GEP), wavelet-ANN, wavelet-ANFIS and wavelet-GEP in predicting TDS of mentioned basins were assessed over a period of 20 years at twelve different hydrometric stations. EC (μmhos/cm), Na (meq L−1) and Cl (meq L−1) parameters were selected (based on Pearson correlation) as input variables to forecast amount of TDS in four studied basins. To develop hybrid wavelet-AI models, the original observed data series was decomposed into sub-time series using Daubechies wavelets at suitable level for each basin. Based on the statistical criteria of correlation coefficient (R), root mean square error (RMSE) and mean absolute error (MAE), the hybrid wavelet-AI models performance were better than single AI models in all basins. A comparison was made between these artificial intelligence approaches which emphasized the superiority of wavelet-GEP over the other intelligent models with amount of RMSE 18.978, 6.774, 9.639 and 318.363 mg/l, in Nazlu Chay, Tajan, Zayandeh Rud and Helleh basins, respectively.

Trends and variability in African long-term precipitation.

Abstract: African precipitation trends are commonly analyzed using short-term data observed over small areas. This study analyzed changes in long-term (1901–2015) annual and seasonal precipitation of high spatial (0.5° × 0.5° grid) resolution covering the entire African continent. To assess an acceleration/deceleration of the precipitation increase/decrease, trend magnitude (mm/year) over the period 1991–2015 was subtracted from that of 1965–1990 to obtain Slope Difference (SD, mm/year). Co-variation of precipitation sub-trends with changes in large-scale ocean–atmosphere conditions was investigated. Regardless of the trend significance, in most parts of Africa, annual precipitation exhibited negative (positive) trends over the period 1965–1990 (1991–2015). Thus, the continent was, on average, recently (from 1991 to 2015) wetter than it was over the period 1965–1990. From 1901 to 2015, the null hypothesis H0 (no trend) was rejected (p < 0.05) for annual precipitation decrease over West Africa especially along the coastal areas near the Gulf of Guinea. The H0 was also rejected (p < 0.05) for the increase in annual and September–November precipitation of some areas along the Equatorial region (such as in Gabon and around Lake Victoria). For both annual and seasonal precipitation, the least SD values in the range − 1 to 1 mm/year were obtained in areas north of 10° N. The SD value went up to about 20 mm/year over the Sahel belt especially for the peak monsoon (June–August season). For the March–May precipitation, positive SD values were obtained in the Western part of Southern Africa. However, negative SD values (around − 5 mm/year) were obtained in the Horn of Africa. Variation in sub-trends of the East African precipitation was found to be driven by changes in Sea Surface Temperature (SST) of the Indian and Atlantic Oceans. Variability in sub-trends of the West African precipitation is linked to changes in SST of the Atlantic Ocean. Changes in sub-trends of the South African precipitation correspond to anomalies in SST from the Pacific and Indian Oceans. Knowledge of precipitation changes and possible drivers is vital for predictive adaptation regarding the impacts of climate variability on hydro- or agro-meteorology.

Assessment of workplace accident risks in underground collieries by integrating a multi-goal cause-and-effect analysis method with MCDM sensitivity analysis

Abstract: This study introduces a method using a multi-goal fuzzy cognitive map (FCM) and multi-criteria decision making based on sensitivity analysis to assess the risks associated with working accidents in underground collieries. Safety, stoppage in operation, and operational and capital costs are considered as the main goals during the FCM process with significant emphasis on safety. Workplace accidents data from Kerman underground collieries are statistically evaluated to find the degrees of occurrence probability, severity, and work-disability duration as the main risk factors. The causes and effects of accidents are analyzed using FCM based on three goals and the effects of risk factors. A sensitivity analysis on the weights of the goals is conducted with the aim of increasing the workplace safety in TOPSIS environment after solving the designed multi-goal FCM. Results indicate that “gas poisoning,” “roof fall,” and “debris and destruction” take the first three ranks and impose high risks to the system. By contrast, “collision, hit, and crash” presents the lowest risk among all accidents.

Effect of single and multi-site calibration techniques on hydrological model performance, parameter estimation and predictive uncertainty: a case study in the Logone catchment, Lake Chad basin

Abstract: Understanding hydrological processes at catchment scale through the use of hydrological model parameters is essential for enhancing water resource management. Given the difficulty of using lump parameters to calibrate distributed catchment hydrological models in spatially heterogeneous catchments, a multiple calibration technique was adopted to enhance model calibration in this study. Different calibration techniques were used to calibrate the Soil and Water Assessment Tool (SWAT) model at different locations along the Logone river channel. These were: single-site calibration (SSC); sequential calibration (SC); and simultaneous multi-site calibration (SMSC). Results indicate that it is possible to reveal differences in hydrological behavior between the upstream and downstream parts of the catchment using different parameter values. Using all calibration techniques, model performance indicators were mostly above the minimum threshold of 0.60 and 0.65 for Nash Sutcliff Efficiency (NSE) and coefficient of determination (R 2) respectively, at both daily and monthly time-steps. Model uncertainty analysis showed that more than 60% of observed streamflow values were bracketed within the 95% prediction uncertainty (95PPU) band after calibration and validation. Furthermore, results indicated that the SC technique out-performed the other two methods (SSC and SMSC). It was also observed that although the SMSC technique uses streamflow data from all gauging stations during calibration and validation, thereby taking into account the catchment spatial variability, the choice of each calibration method will depend on the application and spatial scale of implementation of the modelling results in the catchment.

Design and evaluation of SVR, MARS and M5Tree models for 1, 2 and 3-day lead time forecasting of river flow data in a semiarid mountainous catchment

Abstract: Accurate and reliable river flow forecasts attained with data-intelligent models can provide significant information about future water resources management. In this study we employed a 50-model ensemble of three data-driven predictive models, namely the support vector regression (SVR), multivariate adaptive regression spline (MARS) and M5 model tree (M5Tree) to forecast river flow data in a semiarid and ecologically significant mountainous region of Pailugou catchment in northwestern China. To attain stable and accurate forecast results, 50 different models were trained by randomly sampling the entire river flow data into 80% for training and 20% for testing subsets. To attain a complete evaluation of the ensemble-model based results, the global mean of six quantitative statistical performance evaluation measures: the coefficient of correlation (R), mean absolute relative error (MAE), root mean squared error (RMSE), Nash–Sutcliffe efficiency coefficient (NS), relative RMSE, and the Willmott’s Index (WI), and Taylor diagrams, including skill scores relative to a persistence model, were selected to assess the performances of the developed predictive models. The results indicated that all of the averaged R value attained was higher than 0.900 and all of the averaged NS values were higher than 0.800, representing good performance of the SVR, MARS and M5Tree models applied in the 1-, 2- and 3-day ahead modeling horizon, and this also accorded with the deductions made through an assessment of the Willmott’s Index. However, the M5Tree model outperformed both the SVR and MARS models (with NS = 0.917 vs. 0.904 and 0.901 for 1-day, 0.893 vs. 0.854 and 0.845 for 2-day, and 0.850 vs. 0.828 and 0.810 for 3-day forecasting horizons, respectively), which was in concurrence with the high value of WI. Therefore, based on the ensemble of 50 models, the performance of the M5Tree can be considered as superior to the SVR and MARS models when applied in a problem of river flow forecasting at multiple forecast horizon. A detailed comparison of the overall performance of all three models evaluated through Taylor diagrams and boxplots indicated that the 1-day ahead forecasting results were more accurate for all of the predictive models compared to the 2- and 3-day ahead forecasting horizons. Data-intelligent models designed in this study indicate that the M5Tree method could successfully be explored for short-term river flow forecasting in semiarid mountainous regions, which may have useful implications in water resources management, ecological sustainability and assessment of river systems.

Flood risk assessment and mapping based on a modified multi-parameter flood hazard index model in the Guanzhong Urban Area, China

Abstract: The aim of this study is to promote appropriate land development policies and to improve operations of flood risk in urban areas. This study first illustrated a multi-parameter flood hazard index (FHI) model for assessing potential flood risk areas in the Guanzhong Urban Area (GUA), a large-scale urban area in northwestern China. The FHI model consisted of the following seven parameters: rainfall intensity, flow accumulation, distance from the river network, elevation, land use, surface slope, and geology. The parameter weights were assigned using an analytical hierarchy process and the sum weight of the first three parameters accounted for 71.21% of the total weight and had significant influence on flooding. By combining with population factor, the FHI model was modified to estimate the flood control area in the GUA. The spatial distribution of the flood risk was obviously different in the flood hazard area and flood control area. The very low risk and medium risk area in the flood control area increased by 11.19% and reduced by 9.03% compared to flood hazard area, but there were no obvious differences in other levels of risk areas. The flood control assessment indicated that very high flood risk areas were principally concentrated along river banks (the Weihe River and its tributaries) and in the middle of the Guanzhong Plain. Land use and population distribution are related to flooding. Especially, forestland was located in 84.48% of the very low risk area, while low risk areas were mainly located in 91.49% of high population dispersion area.

Risk assessment and ranking of heavy metals concentration in Iran’s Rayen groundwater basin using linear assignment method

Abstract: Groundwater is an important source of freshwater for domestic, agricultural and industrial uses in Iran. Groundwater quality assessment and environmental evaluation are considered as critical issues in recent years. Intensive human activities have resulted in significant changes in environment leading to serious groundwater contamination. This research proposes a two-part systematic approach to tackle heavy metals contamination problem in Rayen Basin (southeast Iran). The first part consists of determining geochemical characteristics and evaluating groundwater quality through application of water quality index and heavy metal pollution indices (i.e. HPI and MI). The second part includes ranking sampling stations based on heavy metals concentration in groundwater using linear assignment method. Six types of water could be identified according to the dominant cations and anions in samples: Ca–HCO3, Ca–SO4, Na–Cl, Na–HCO3, Na–SO4 and mixed water type. Calculation of indices revealed that natural and anthropogenic activities are playing a vital role in degrading groundwater quality in the study area. The proposed methodology can help in groundwater resource management and preventative activities by identifying risk factors and recognizing their pollution level. The results of this research provide useful and effective information for water pollution control and management and can be used in environmental studies in order to protect groundwater resources in the future.

Incorporating probabilistic approach into local multi-criteria decision analysis for flood susceptibility assessment

Abstract: Flood risk management can be enhanced by integrating geographic information system (GIS) with multi-criteria decision analysis (MCDA). However, the conventional, deterministic MCDA methods ignore uncertainty in the decision-making process and fail to account for local variability in criteria values and preferences. Therefore, a spatially explicit MCDA model which effectively incorporates spatial heterogeneity is required. In this paper, a probabilistic or stochastic MCDA method which incorporates the uncertainty into a local weighted linear combination (WLC) was utilized to evaluate flood susceptibility; and an application case in Gucheng County, Central China, was developed. A GIS database of geomorphological and hydro-meteorological criteria contributing to flood susceptibility analysis was constructed using six conditioning factors: digital elevation model (DEM), slope (SL), maximum three-day precipitation (M3DP), topographic wetness index (TWI), distance from the river (DR), and Soil Conservation Service Curve Number (SCS-CN). The results of local WLC were compared with those of the global WLC. It shows that the local WLC model can provide much more valuable information about the spatial patterns of criterion values, ranges, weights, trade-offs and overall scores, whereas the global WLC can only depict the spatial distribution of criterion values and overall scores. The local WLC can also help to prioritize the most susceptible locations within a neighborhood when navigating the disaster assistance process. Moreover, the uncertainty analysis of criteria weights increases the degree of confidence in the model output. It is concluded that the presented approach can provide more insights and understanding of the nature of the flood susceptibility than global WLC.

Three-dimensional analysis of ozone and PM 2.5 distributions obtained by observations of tethered balloon and unmanned aerial vehicle in Shanghai, China

Abstract: A lightweight unmanned aerial vehicle (UAV) and a tethered balloon platform were jointly used to investigate three-dimensional distributions of ozone and PM2.5 concentrations within the lower troposphere (1000 m) at a localized coastal area in Shanghai, China. Eight tethered balloon soundings and three UAV flights were conducted on May 25, 2016. Generalized additive models (GAMs) were used to quantitatively describe the relationships between air pollutants and other obtained parameters. Field observations showed that large variations were captured both in the vertical and horizontal distributions of ozone and PM2.5 concentrations. Significant stratified layers of ozone and PM2.5 concentrations as well as wind directions were observed throughout the day. Estimated bulk Richardson numbers indicate that the vertical mixing of air masses within the lower troposphere were heavily suppressed throughout the day, leading to much higher concentrations of ozone and PM2.5 in the planetary boundary layer (PBL). The NO and NO2 concentrations in the experimental field were much lower than that in the urban area of Shanghai and demonstrated totally different vertical distribution patterns from that of ozone and PM2.5. This indicates that aged air masses of different sources were transported to the experimental field at different heights. Results derived from the GAMs showed that the aggregate impact of the selected variables for the vertical variations can explain 94.3% of the variance in ozone and 94.5% in PM2.5. Air temperature, relative humidity and atmospheric pressure had the strongest effects on the variations of ozone and PM2.5. As for the horizontal variations, the GAMs can explain 56.3% of the variance in ozone and 57.6% in PM2.5. The strongest effect on ozone was related to air temperature, while PM2.5 was related to relative humidity. The output of GAMs also implied that fine aerosol particles were in the stage of growth in the experimental field, which is different from ozone (aged air parcels of ozone). Geographical parameters influenced the horizontal variations of ozone and PM2.5 concentrations by changing underlying surface types. The differences of thermodynamic properties between land and sea resulted in quick changes of PBL height, air temperature and dew point over the coastal area, which was linked to the extent of vertical mixing at different locations. The results of GAMs can be used to analyze the sources and formation mechanisms of ozone and PM2.5 pollutions at a localized area.

Predicting spatio-temporal concentrations of PM2.5 using land use and meteorological data in Yangtze River Delta, China

Abstract: The prediction of PM2.5 concentrations with high spatiotemporal resolution has been suggested as a potential method for data collection to assess the health effects of exposure. This work predicted the weekly average PM2.5 concentrations in the Yangtze River Delta, China, by using a spatio-temporal model. Integrating land use data, including the areas of cultivated land, construction land, and forest land, and meteorological data, including precipitation, air pressure, relative humidity, temperature, and wind speed, we used the model to estimate the weekly average PM2.5 concentrations. We validated the estimated effects by using the cross-validated R2 and Root mean square error (RMSE); the results showed that the model performed well in capturing the spatiotemporal variability of PM2.5 concentration, with a reasonably large R2 of 0.86 and a small RMSE of 8.15 (μg/m3). In addition, the predicted values covered 94% of the observed data at the 95% confidence interval. This work provided a dataset of PM2.5 concentration predictions with a spatiotemporal resolution of 3 km × week, which would contribute to accurately assessing the potential health effects of air pollution.

Impacts of climate variability and change on drought characteristics in the Niger River Basin, West Africa

Abstract: West Africa has been afflicted by droughts since the declining rains of the 1970s. Therefore, this study examines the characteristics of drought over the Niger River Basin (NRB), investigates the influence of the drought on the river flow, and projects the impacts of future climate change on drought. A combination of observation data and regional climate simulations of past (1986–2005) and future climates (2046–2065 and 2081–2100) were analyzed. The standardized precipitation index (SPI) and standardized precipitation and evapotranspiration index (SPEI) were used to characterize drought while the standardized runoff index (SRI) was used to quantify river flow. Results of the study show that the historical pattern of drought is consistent with previous studies over the Basin and most part of West Africa. RCA4 ensemble gives realistic simulations of the climatology of the Basin in the past climate. Generally, an increase in drought intensity and frequency are projected over NRB. The coupling between SRI and drought indices was very strong (P < 0.05). The dominant peaks can be classified into three distinct drought cycles with periods 1–2, 2–4, 4–8 years. These cycles may be associated with Quasi-Biennial Oscillation (QBO) and El-Nino Southern Oscillation (ENSO). River flow was highly sensitive to precipitation in the NRB and a 1–3 month lead time was found between drought indices and SRI. Under RCP4.5, changes in the SPEI drought frequency range from 1.8 (2046–2065) to 2.4 (2081–2100) month year−1 while under RCP8.5, the change ranges from 2.2 (2046–2065) to 3.0 month year−1 (2081–2100). Niger Middle sub-basin is likely to be mostly impacted in the future while the Upper Niger was projected to be least impacted. Results of this study may guide policymakers to evolve strategies to facilitate vulnerability assessment and adaptive capacity of the basin in order to minimize the negative impacts of climate change.

Delineation of flooding risk hotspots based on digital elevation model, calculated and historical flooding extents: the case of Ouagadougou

Abstract: Delineation of flood risk hotspots can be considered as one of the first steps in an integrated methodology for urban flood risk management and mitigation. This paper presents a step-by-step methodology in a GIS-based framework for identifying flooding risk hotspots for residential buildings. This is done by overlaying a map of potentially flood-prone areas [estimated through the topographic wetness index (TWI)], a map of residential areas [extracted from a city-wide assessment of urban morphology types (UMT)], and a geo-spatial census dataset. The novelty of this paper consists in the fact that the flood-prone areas (the TWI thresholds) are identified through a maximum likelihood method (MLE) based both on inundation profiles calculated for a specific return period (TR), and on information about the extent of historical flooding in the area of interest. Furthermore, Bayesian parameter updating is employed in order to estimate the TWI threshold by employing the historical extent as prior information and the inundation map for calculating the likelihood function. For different statistics of the TWI threshold, the map of potentially flood-prone areas is overlaid with the map of residential urban morphology units in order to delineate the residential flooding risk urban hotspots. Overlaying the delineated urban hotspots with geo-spatial census datasets, the number of people affected by flooding is estimated. These kind of screening procedures are particularly useful for locations where there is a lack of detailed data or where it is difficult to perform accurate flood risk assessment. In fact, an application of the proposed procedure is demonstrated for the identification of urban flooding risk hotspots in the city of Ouagadougou, capital of Burkina Faso, a city for which the observed spatial extent of a major flood event in 2009 and a calculated inundation map for a return period of 300 years are both available.

Estimating the provincial environmental Kuznets curve in China: a geographically weighted regression approach

Abstract: This study estimates the environmental Kuznets curve (EKC) relationship at the province level in China. We apply empirical methods to test three industrial pollutants—SO2 emission, wastewater discharge, and solid waste production—in 29 Chinese provinces in 1994–2010. We use the geographically weighted regression (GWR) approach, wherein the model can be fitted at each spatial location in the data, weighting all observations by a function of distance from the regression point. Hence, considering spatial heterogeneity, the EKC relationship can be analyzed region-specifically through this approach, rather than describing the average relationship over the entire area examined. We also investigate the spatial stratified heterogeneity to verify and compare risk factors that affect regional pollution with statistical models. This study finds that the GWR model, aimed at considering spatial heterogeneity, outperforms the OLS model; it is more effective at explaining the relationships between environmental performance and economic growth in China. The results indicate a significant variation in the existence of the EKC relationship. Such spatial patterns suggest province-specific policymaking to achieve balanced growth in those provinces.

River flow modelling: comparison of performance and evaluation of uncertainty using data-driven models and conceptual hydrological model

Abstract: Hydrological and statistical models are playing an increasing role in hydrological forecasting, particularly for river basins with data of different temporal scales. In this study, statistical models, e.g. artificial neural networks, adaptive network-based fuzzy inference system, genetic programming, least squares support vector machine, multiple linear regression, were developed, based on parametric optimization methods such as particle swarm optimization (PSO), genetic algorithm (GA), and data-preprocessing techniques such as wavelet decomposition (WD) for river flow modelling using daily streamflow data from four hydrological stations for a period of 1954–2009. These models were used for 1-, 3- and 5-day streamflow forecasting and the better model was used for uncertainty evaluation using bootstrap resampling method. Meanwhile, a simple conceptual hydrological model GR4J was used to evaluate parametric uncertainty based on generalized likelihood uncertainty estimation method. Results indicated that: (1) GA and PSO did not help improve the forecast performance of the model. However, the hybrid model with WD significantly improved the forecast performance; (2) the hybrid model with WD as a data preprocessing procedure can clarify hydrological effects of water reservoirs and can capture peak high/low flow changes; (3) Forecast accuracy of data-driven models is significantly influenced by the availability of streamflow data. More human interferences from the upper to the lower East River basin can help to introduce greater uncertainty in streamflow forecasts; (4) The structure of GR4J may introduce larger parametric uncertainty at the Longchuan station than at the Boluo station in the East river basin. This study provides a theoretical background for data-driven model-based streamflow forecasting and a comprehensive view about data and parametric uncertainty in data-scarce river basins.