Prioritization of water erosion-prone sub-watersheds using three ensemble methods in Qareaghaj catchment, southern Iran

Water-induced erosion poses severe harm to the sustainable development of land and water resources that is essential for attaining agricultural sustainability in Qareaghaj catchment of Fars Province, Iran. This study evaluates the topo-hydrological, morphometric, climatic, and environmental characteristics of Qareaghaj catchment for prioritizing the sub-watersheds that are susceptible to erosion caused by water. We tested and compared a novel ensemble multi-criteria decision-making (MCDM) model, namely the weighted aggregated sum product assessment-analytical hierarchy process (WASPAS-AHP) with prevailing benchmark ensemble MCDM models including VlseKriterijumska optimizacija I Kompromisno Resenje (VIKOR)-AHP and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)-AHP for ranking sub-watersheds and determining the most significant parameter that influences water erosion (WE) in Qareaghaj catchment. The outcome of weights using pairwise comparison matrix (PCM) of AHP reveals that normalized difference vegetation index (NDVI), mean annual rainfall (MAR), slope degree (SD), and slope length and steepness factor (LS) governs the WE in Qareaghaj catchment. The prioritization rankings of sub-watersheds obtained using the VIKOR-AHP, TOPSIS-AHP, and WASPAS-AHP models demonstrate that SW31, SW63, and SW94 had the highest priority rank with a score of 0.047, 0.69, and 0.477, respectively. The comparison of rankings from the models using Spearman's correlation coefficient tests (SCCT) and Kendall's tau correlation coefficient tests (KTCCT) revealed that WASPAS-AHP had a higher correlation with TOPSIS-AHP and VIKOR-AHP ensemble models. The outcome of MCDM models was validated based on the erosion potential method (EPM), which displayed that the VIKOR-AHP model was better for mapping the erosion susceptibility than TOPSIS-AHP and WASPAS-AHP models. Thus, the erosion susceptibility mapping based on the VIKOR-AHP ensemble model can be considered for developing new strategies and land use policies in order to control WE in Qareaghaj catchment.

Assessing, mapping, and optimizing the locations of sediment control check dams construction

Check dams are considered to be one of the most effective measures for conservation of the soil and water resources. However, identifying the most suitable sites for the installation of check dams remain quite demanding. This research investigates and compares five machine learning algorithms (MLAs) - boosted regression trees (BRT), multivariate adaptive regression spline (MARS), mixture discriminant analysis (MDA), random forest (RF), and support vector machine (SVM) - for generating check-dam site-suitability maps (CDSSMs) and assessing them in Firuzkuh County, Iran. First, the locations of 475 existing check dams were monitored, registered, and divided into calibration (70%) and testing datasets (30%) for training and validation of the models. Fourteen check-dam conditioning factors (CDCFs) were selected and checked for multicollinearity. The relative importance of the CDCFs assessed using the elastic net (ENET) algorithm. Results demonstrated that distance from river (DFR) and drainage density (DD) to be the most significant factors for mapping the suitable sites for the erection of check dams. This research revealed that all of five MLAs had excellent accuracy for predicting the check-dam site-suitability with high AUC values: RF (0.966), SVM (0.878), MARS (0.878), MDA (0.844), and BRT (0.843). The most accurate model (RF) showed that 16.95%, 35.55%, 31.08%, and 16.42% of study area comes under low, moderate, high, and very high suitability classes. The outcome achieved by this research will be helpful to sustainability planners and managers in constructing check dams at suitable sites for better conservation of soil and water resources.

Assessment of the outbreak risk, mapping and infection behavior of COVID-19: Application of the autoregressive integrated-moving average (ARIMA) and polynomial models

Infectious disease outbreaks pose a significant threat to human health worldwide. The outbreak of pandemic coronavirus disease 2019 (COVID-19) has caused a global health emergency. Thus, identification of regions with high risk for COVID-19 outbreak and analyzing the behaviour of the infection is a major priority of the governmental organizations and epidemiologists worldwide. The aims of the present study were to analyze the risk factors of coronavirus outbreak for identifying the areas having high risk of infection and to evaluate the behaviour of infection in Fars Province, Iran. A geographic information system (GIS)-based machine learning algorithm (MLA), support vector machine (SVM), was used for the assessment of the outbreak risk of COVID-19 in Fars Province, Iran whereas the daily observations of infected cases were tested in the-polynomial and the autoregressive integrated moving average (ARIMA) models to examine the patterns of virus infestation in the province and in Iran. The results of the disease outbreak in Iran were compared with the data for Iran and the world. Sixteen effective factors were selected for spatial modelling of outbreak risk. The validation outcome reveals that SVM achieved an AUC value of 0.786 (March 20), 0.799 (March 29), and 86.6 (April 10) that displays a good prediction of outbreak risk change detection. The results of the third-degree polynomial and ARIMA models in the province revealed an increasing trend with an evidence of turning, demonstrating extensive quarantines has been effective. The general trends of virus infestation in Iran and Fars Province were similar, although a more volatile growth of the infected cases is expected in the province. The results of this study might assist better programming COVID-19 disease prevention and control and gaining sorts of predictive capability would have wide-ranging benefits.

Using machine learning algorithms to map the groundwater recharge potential zones

Groundwater recharge is indispensable for the sustainable management of freshwater resources, especially in the arid regions. Here we address some of the important aspects of groundwater recharge through machine learning algorithms (MLAs). Three MLAs including, SVM, MARS, and RF were validated for higher prediction accuracies in generating groundwater recharge potential maps (GRPMs). Accordingly, soil permeability samples were prepared and are arbitrarily grouped into training (70%) and validation (30%) samples. The GRPMs are generated using sixteen effective factors, such as elevation (denoted using a digital elevation model; DEM), aspect, slope angle, TWI (topographic wetness index), fault density, MRVBF (multiresolution index of valley bottom flatness), rainfall, lithology, land use, drainage density, distance from rivers, distance from faults, annual ETP (evapo-transpiration), minimum temperature, maximum temperature, and rainfall 24-hr. Subsequently, the VI (variables importance) is assessed based on the LASSO algorithm. The GRPMs of three MLAs were validated using the ROC-AUC (receiver operating characteristic-area under curve) and various techniques including true positive rate (TPR), false positive rate (FPR), F-measures, fallout, sensitivity, specificity, true skill statistics (TSS), and corrected classified instances (CCI). Based on the validation, the RF algorithm performed better (AUC = 0.987) than the SVM (AUC = 0.963) and the MARS algorithm (AUC = 0.962). Furthermore, the accuracy of these MLAs are included in excellent class, based on the ROC curve threshold. Our case study shows that the GRPMs are potential guidelines for decision-makers in drafting policies related to the sustainable management of the groundwater resources.