Groundwater
imad ali; maryam bayati khatibi; sadra karimzadeh
Abstract
This study aimed to delineate groundwater recharge zones using a combination of analytical hierarchy process (AHP), fuzzy-AHP, and frequency ratio (FR) models. Additionally, it aimed to compare the effectiveness of these models in groundwater recharge potential zone mapping. To achieve these objectives, ...
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This study aimed to delineate groundwater recharge zones using a combination of analytical hierarchy process (AHP), fuzzy-AHP, and frequency ratio (FR) models. Additionally, it aimed to compare the effectiveness of these models in groundwater recharge potential zone mapping. To achieve these objectives, nine groundwater influencing factors were considered, including geology, soil types, lineament density, elevation, slope, topographic wetness index, drainage density, land use land cover, and rainfall. Thematic maps for all these factors were generated using satellite and conventional data in the ArcGIS environment. Weight was assigned to each thematic layer based on its significance to recharge. All thematic layers were combined using AHP model-l (WLC), AHP model-ll (Weighted sum), fuzzy-AHP overlay, and FR-based model using ArcGIS. The findings revealed that 15% and 39% of the study area have high recharge potentials according to AHP-based model-l and model-ll, respectively. The FAHP model demarcated 43% of the area as high recharge zones while the FR model demarcated 42% of the area as high recharge zones. The majority of high groundwater recharge areas were found in the central part of the study area, while the southern part was demarcated as a moderate recharge zone. The eastern and western parts were demarcated as low recharge potentials zones. To validate the accuracy of these models, the study used receiver operating characteristic (ROC) validation curves. The ROC curves revealed that AHP model-ll had the highest accuracy (AUC=89%) followed by the FAHP model (AUC=88%), AHP model-l (AUC=84%), and FR (AUC=81%)
Geomorphology
maryam bayatikhatibi; maryam bayati khatibi; imad ali; sadra kharimzadeh
Abstract
The Quetta sub-basin is a part of the Pishin River basin, situated in the southwestern region of Pakistan. This study aimed to determine the spatial distribution of annual soil erosion through the utilization of the Revised Universal Soil Loss Equation (RUSLE) model. To accomplish this, numerous data ...
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The Quetta sub-basin is a part of the Pishin River basin, situated in the southwestern region of Pakistan. This study aimed to determine the spatial distribution of annual soil erosion through the utilization of the Revised Universal Soil Loss Equation (RUSLE) model. To accomplish this, numerous data mining techniques were employed, along with machine learning algorithms, to produce thematic layers (K, R, LS, C, and P) that served as input parameters for the RUSLE model. According to the resultant model, soil erosion in the study area ranged from 0.00 to 866 tons per hectare per year. The estimated values for rainfall-runoff erosivity (R), soil. erodibility (K), topography (LS), and cover management (C), factors ranged from 147 to 191 (MJ.mm.ha⁻¹.h⁻¹year⁻¹), 0.0229 to 0.0259 (t.ha.MJ⁻¹mm⁻¹), 0.002 to 360.77, and 0.001 to 1, respectively. The statistics revealed that 58% of the land in the study area experiences a very low degree of soil erosion, at an erosion rate less than 13.58 t/ha/year. About 24% of the study area faces low erosion, with an erosion rate spanning from 13.58-44.16 t/ha/year. 13% of the area is demarcated as moderate soil erosion severity, at an erosion rate ranging from 44.16-81.53.14 t/ha/year. On the other hand, 5% of the study area experienced high to very high soil erosion, with an erosion rate of 81.53-866.34 t/ha/year.
