Capability and robustness of novel hybridized models used for drought hazard modeling in southeast Queensland, Australia.

Widespread detrimental and long-lasting droughts are having catastrophic impacts around the globe. Researchers, organizations, and policy makers need to work together to obtain precise information, enabling timely and accurate decision making to mitigate drought impacts. In this study, a spatial modeling approach based on an adaptive neuro-fuzzy inference system (ANFIS) and several metaheuristic optimizations (ANFIS-BA, ANFIS-GA, ANFIS-ICA, ANFIS-PSO) was developed to predict the spatial occurrence of drought in a region in southeastern Queensland, Australia. In this approach, data describing the distribution of eight drought-contributing factors were prepared for input into the models to serve as independent variables. Relative departures of rainfall (RDR) and relative departures of soil moisture (RDSM) were analyzed to identify locations where drought conditions have occurred. The set of locations in the study area identified as having experienced drought conditions was randomly divided into two groups, 70% were used for training and 30% for validation. The models employed these data to generate maps that predict the locations that would be expected to experience drought. The prediction accuracy of the model-produced drought maps was scrutinized with two evaluation metrics: area under the receiver operating characteristic curve (AUC) and root mean square error (RMSE). The results demonstrate that the hybridized models (ANFIS-BA (AUCmean = 83.7%, RMSEmean = 0.236), ANFIS-GA (AUCmean = 81.62%, RMSEmean = 0.247), ANFIS-ICA (AUCmean = 82.12%, RMSEmean = 0.247), and ANFIS-PSO (AUCmean = 81.42%, RMSEmean = 0.255)) yield better predictive performance than the standalone ANFIS model (AUCmean = 71.8%, RMSEmean = 0.344). Furthermore, sensitivity analyses indicated that plant-available water capacity, the percentage of soil comprised of sand, and mean annual precipitation were the most important predictors of drought hazard. The versatility of the new approach for spatial drought modeling and the capacity of ANFIS model hybridization to improve model performance suggests great potential to assist decision makers in their formulations of drought risk, recovery, and response management, and in the development of contingency plans.

Data on assessment of corrosion-scaling potential and chemical parameters of groundwater quality for industrial and agricultural sectors in the Piranshahr Watershed in the West Azerbaijan province, Iran.

The present work sets out to 1) evaluate the corrosion-scaling potential of groundwater resources for the industrial sector as well as to 2) examine groundwater chemical parameters for the agricultural sector in the Piranshahr Watershed in the West Azerbaijan province, Iran, using geostatistical analyses and the Wilcox diagram in a GIS environment. A total of 145 spring locations as representatives of groundwater potentiality were recorded by a handheld GPS device and the corrosion and scaling potential states were further scrutinized. The latter was carried out on the basis of examining the chemical parameters at each sample location including alkalinity, pH, temperature, Na+, Ca++, Mg++, TH, HCO 3 - , Co 3 - 2 , Cl - , SO 4 - 2 , Electrical conductivity, and Total dissolved solids. The corrosion and scaling potential of groundwater was then evaluated by using Langelier saturation index (LSI), Larson-Skold index (LS), Ryznar stability index (RSI), Aggressive index (AI), and Puckorius scaling index (PSI). Also, the groundwater quality state for agriculture was assessed by the Wilcox diagram on the basis of Electrical conductivity and Sodium adsorption ratio parameters. The provided data are beneficial for researchers, policymakers, and authorities for taking pragmatic actions. Also, the compiled data can be used in the context of corrosion/scaling and groundwater quality assessment can be generalized around the world.

Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and UNEEC methods.

Although estimating the uncertainty of models used for modelling nitrate contamination of groundwater is essential in groundwater management, it has been generally ignored. This issue motivates this research to explore the predictive uncertainty of machine-learning (ML) models in this field of study using two different residuals uncertainty methods: quantile regression (QR) and uncertainty estimation based on local errors and clustering (UNEEC). Prediction-interval coverage probability (PICP), the most important of the statistical measures of uncertainty, was used to evaluate uncertainty. Additionally, three state-of-the-art ML models including support vector machine (SVM), random forest (RF), and k-nearest neighbor (kNN) were selected to spatially model groundwater nitrate concentrations. The models were calibrated with nitrate concentrations from 80 wells (70% of the data) and then validated with nitrate concentrations from 34 wells (30% of the data). Both uncertainty and predictive performance criteria should be considered when comparing and selecting the best model. Results highlight that the kNN model is the best model because not only did it have the lowest uncertainty based on the PICP statistic in both the QR (0.94) and the UNEEC (in all clusters, 0.85-0.91) methods, but it also had predictive performance statistics (RMSE = 10.63, R2 = 0.71) that were relatively similar to RF (RMSE = 10.41, R2 = 0.72) and higher than SVM (RMSE = 13.28, R2 = 0.58). Determining the uncertainty of ML models used for spatially modelling groundwater-nitrate pollution enables managers to achieve better risk-based decision making and consequently increases the reliability and credibility of groundwater-nitrate predictions.