Groundwater recharge potential zonation using an ensemble of machine learning and bivariate statistical models.

Many regions in Iran are currently experience water crisis, largely driven by frequent droughts and expanding agricultural land combined with over abstraction of groundwater. Therefore, it is extremely important to identify potential groundwater recharge (GWR) zones to help in prevent water scarcity. The key objective of this research is to applying different scenarios for GWR potential mapping by means of a classifier ensemble approach, namely a combination of Maximum Entropy (ME) and Frequency Ratio (FR) models in a semi-arid mountainous, Marboreh Watershed of Iran. To consider the ensemble effect of these models, 15 input layers were generated and used in two models and then the models were combined in seven scenarios. According to marginal response curves (MRCs) and the Jackknife technique, quaternary formations (Qft1 and Qft2) of lithology, sandy-clay-loam (Sa. Cl. L) class of soil, 0-4% class of slope, and agriculture & rangeland classes of land use, offered the highest percolation potential. Results of the FR model showed that the highest weight belonged to Qft1 rocks and Sa. Cl. L textures. Seven scenarios were used for GWR potential maps by different ensembles based on basic mathematical operations. Correctly Classified Instances (CCI), and the AUC indices were applied to validate model predictions. The validation indices showed that scenarios 5 had the best performance. The combination of models by different ensemble scenarios enhances the efficiency of these models. This study serves as a basis for future investigations and provides useful information for prediction of sites with groundwater recharge potential through combination of state-of-the-art statistical and machine learning models. The proposed ensemble model reduced the machine learning and statistical models' limitations gaps and promoted the accuracy of the model where combining, especially for data-scarce areas. The results of present study can be used for the GWR potential mapping, land use planning, and groundwater development plans.

Introducing the coupled stepwise areal constraining and Mahalanobis distance: a promising MCDM-based probabilistic model for landfill site selection.

This study sets out to propose a new ensemble of probabilistic spatial modeling and multi-criteria decision-making comprised of stepwise areal constraining and Mahalanobis distance algorithms in order to assess areal suitability for landfilling. The Ardak watershed was selected as the study area due to encountering several cases of open garbage dumps and uncontrolled landfills which are one of the main sources of river water pollution in the upstream of the Ardak dam. The results revealed that the proposed algorithm successfully assists in inventory-irrespective probabilistic modeling of landfill siting which is mainly indebted to the role of areal constraining in providing training and validation samples for the Mahalanobis distance model. The latter also showed a robust pattern recognition results from which a discernible differentiation of the area was attained while the spatial dependencies between the environmental factors were taken into account. Mahalanobis distance also gave an outstanding performance in terms of goodness of fit (area under the success rate 89.367) and prediction power (area under the success rate 89.252). Based on a five-point scale classification scheme, about 2.7% and 2.6% of the study area, respectively, have high and very high suitability for landfilling, while the remaining area is shared between very low-to-moderate suitability classes. According to the current trail of literature regarding landfill site selection which mostly relies on mere areal filtering, a probabilistic model would give invaluable inferences regarding the pattern of suitability/susceptibility of the area of interest and causative role of the influential factors. Graphical Abstract.

Evaluating the influence of geo-environmental factors on gully erosion in a semi-arid region of Iran: An integrated framework.

Despite the importance of soil erosion in sustainable development goals in arid and semi-arid areas, the study of the geo-environmental conditions and factors influencing gully erosion occurrence is rarely undertaken. As effort to this challenge, the main objective of this study is to apply an integrated approach of Geographic Object-Based Image Analysis (GEOBIA) together with high-spatial resolution imagery (SPOT-5) for detecting gully erosion features at the Kashkan-Poldokhtar watershed, Iran. We also aimed to apply a Conditional Probability (CP) model for establishing the spatial relationship between gullies and the Geo-Environmental Factors (GEFs). The gully erosion inventory map prepared using GEOBIA and field surveying was randomly partitioned into two subsets: (1) part 1 that contains 70% was used in the training phase of the CP model; (2) part 2 is a validation dataset (30%) for validation of the model and to confirm its accuracy. Prediction performances of the GEOBIA and CP model were checked by overall accuracy and Receiver Operating Characteristics (ROC) curve methods, respectively. In addition, the influence of all GEFs on gully erosion was evaluated by performing a sensitivity analysis model. The validation findings illustrated that overall accuracy for GEOBIA approach and the area under the ROC curve for the CP model were 92.4% and 89.9%, respectively. Also, based on sensitivity analysis, soil texture, drainage density, and lithology represent significantly effects on the gully erosion occurrence. This study has shown that the integrated framework can be successfully used for modeling gully erosion occurrence in a data-poor environment.