This study proposes an artificial intelligence approach to assess watershed morphometry in the Makran subduction zones of South Iran and Pakistan. The approach integrates machine learning algorithms, including artificial neural networks (ANN), support vector regression (SVR), and multivariate linear regression (MLR), on a single platform. The study area was analyzed by extracting watersheds from a Digital Elevation Model (DEM) and calculating eight morphometric indices. The morphometric parameters were normalized using fuzzy membership functions to improve accuracy. The performance of the machine learning algorithms is evaluated by mean squared error (MSE), mean absolute error (MAE), and correlation coefficient (R2) between the output of the method and the actual dataset. The ANN model demonstrated high accuracy with an R2 value of 0.974, MSE of 4.14 × 10−6, and MAE of 0.0015. The results of the machine learning algorithms were compared to the tectonic characteristics of the area, indicating the potential for utilizing the ANN algorithm in similar investigations. This approach offers a novel way to assess watershed morphometry using ML techniques, which may have advantages over other approaches.
A water supply is vital for preserving usual human living standards, industrial development, and agricultural growth. Scarce water supplies and unplanned urbanization are the primary impediments to results in dry environments. Locating suitable sites for artificial groundwater recharge (AGR) could be a strategic priority for countries to recharge groundwater. Recent advances in machine learning (ML) techniques provide valuable tools for producing an AGR site suitability map (AGRSSM). This research developed an ML algorithm to identify the most appropriate location for AGR in Iranshahr, one of the major districts in the East of Iran characterized by severe drought and excessive groundwater consumption. The area’s undue reliance on groundwater resources has resulted in aquifer depletion and socioeconomic problems. Nine digitized and georeferenced data layers have been considered for preparing the AGRSSM, including precipitation, slope, geology, unsaturated zone thickness, land use, distance from the main rivers, precipitation, water quality, and transmissivity of soil. The developed AGRSSM was trained and validated using 1000 randomly selected points across the study area with an accuracy of 97%. By comparing the results of the proposed sites with those of other methods, it was discovered that the artificial intelligence method could accurately determine artificial recharge sites. In summary, this study uses a novel approach to identify optimal AGR sites using machine learning algorithms. Our findings have practical implications for policymakers and water resource managers looking to address the problem of groundwater depletion in Iranshahr and other regions facing similar challenges. Future research in this area could explore the applicability of our approach to other regions and examine the potential economic benefits of using AGR to recharge groundwater.
In recent decades, there has been a growing emphasis on assessing aquifer vulnerability. Given the availability of spatial data and the GIS advantages, mapping the groundwater vulnerability has become a common tool for protecting and managing groundwater resources. Here, we applied the GIS indexing and an overlay method to explore a combination of the potential contamination factors needed to assess groundwater vulnerability in the Mosha aquifer. The data from a borehole data logger and chemical analysis of spring water show groundwater responses to the surface contaminating sources. To assess the aquifer vulnerability, the potential contaminating sources were classified into three groups, namely (1) geological characteristics such as lithology and structural geology features; (2) the infrastructures induced by human activities such as roads, water wells, and pit latrines; and (3) land use. By considering these components, the risk maps were produced. Our findings indicate that the aquifer is very responsive to the anthropogenic contaminants that may leak into the aquifer from urbanized areas. Additionally, roads and pit latrines can significantly release pollutants into the environment that may eventually leak into the aquifer and contaminate the underlying groundwater resources.
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