Acta Geophysica, Journal Year: 2023, Volume and Issue: 72(2), P. 1033 - 1054
Published: March 16, 2023
Language: Английский
Environmental Research, Journal Year: 2023, Volume and Issue: 228, P. 115832 - 115832
Published: April 11, 2023
Language: Английский
Citations
125
Geocarto International, Journal Year: 2024, Volume and Issue: 39(1)
Published: Jan. 1, 2024
The present research is conducted in the southern region of Khyber Pakhtunkhwa, Pakistan, to identify groundwater potential zones (GWPZ). We used three models including Weight Evidence (WOE), Frequency Ratio (FR), and Information Value (IV) with twelve parameters (elevation, slope, aspect, curvature, drainage network, LULC, precipitation, geology, Lineament, NDVI, road, soil texture, that have been prepared integrated into ArcGIS 10.8. reliability applied models' results was validated using Area Under Receiver Operating Characteristics (AUROC). GWPZ were reclassified five classes, i.e. very low, medium, high, high zone. area occupied by mentioned classes WOE are low (10.14%), (19.58%), medium (26.75%), (27.10%), (16.40%), while FR (20.93%), (32.38%), (18.92%), (13.13%), (14.61%) IV (14.41%), (17.17%), (29.01%), (25.85%), High (13.53%). Success Rate Curve WOE, FR, 0.86, 0.91, 0.87, Predicted values 0.89, 0.93, 0.90, respectively. revealed all statistical performed well delineate GWPZ. However, use technique strongly encouraged evaluate GWPZ, its findings especially useful for managing resources urban planning. Our approaches assessing mapping can be any similar scenarios recommended as a helpful tool policymakers manage groundwater.
Language: Английский
Citations
22
Process Safety and Environmental Protection, Journal Year: 2025, Volume and Issue: unknown, P. 106958 - 106958
Published: Feb. 1, 2025
Language: Английский
Citations
2
Applied Water Science, Journal Year: 2022, Volume and Issue: 12(4)
Published: March 9, 2022
Abstract Increased consumption of water resource due to rapid growth population has certainly reduced the groundwater storage beneath earth which leads certain challenges human being in recent time. For optimal management this vital resource, exploration potential zone (GWPZ) become essential. We have applied Analytical Hierarchy Process (AHP), Frequency Ratio (FR) and two machine learning techniques specifically Random Forest (RF) Naïve Bayes (NB) here delineate GWPZ Gandheswari River Basin Chota Nagpur Plateau, India. To achieve goal study, twelve factors that determine occurrence been selected for inter-thematic correlations overlaid with location wells. These include elevation, drainage density, slope, lithology, geomorphology, topographical wetness index (TWI), distance from river, rainfall, lineament Normalized Difference Vegetation Index (NDVI), soil, Land use cover (LULC). A total 170 points including 85 well site non-well randomly allocated into parts: training testing at share 70:30. The implemented methods significantly provided five GWPZs Very Good (VG), (G), Moderate (M), Poor (P) (VP) high acceptable accuracy. study also finds rainfall elevation greater importance shaping than LULC, NDVI, etc. Model performance tested receiver operator characteristics (ROC), Accuracy (ACC), Kappa Coefficient, MAE, RMSE, etc., methods. Area under curve (AUC) ROC revealed accuracy level AHP, FR, RF NB is 78.8%, 81%, 85.3% 85.5, respectively. coupled AHP FR unveil effective delineation area said river basin by genetically offers low primary porosity lithological constrains. Therefore, can be helpful watershed identifying appropriate wells future.
Language: Английский
Citations
59
Geomatics Natural Hazards and Risk, Journal Year: 2023, Volume and Issue: 14(1)
Published: June 1, 2023
Groundwater is a crucial natural resource that varies in quality and quantity across Khyber Pakhtunkhwa (KPK), Pakistan. Increased population urbanization place enormous demands on groundwater supplies, reducing both their quantity. This research aimed to delineate the potential zone Kohat region, Pakistan by integrating twelve thematic layers. In current research, Potential Zone (GWPZ) were created implementing Weight of Evidence (WOE), Frequency Ratio (FR), Information Value (IV) models region. this study, we used Sentinel-2 satellite data utilized generate an inventory map using machine learning algorithms Google Earth Engine (GEE). Furthermore, validation was done with field survey ground data. The divided into training (80%) testing (20%) datasets. WOE, FR, IV are applied assess relationship between factors GWPZ Finally, results Area Under Curve (AUC) technique for 88%, 91%, 89%. final can aid better future planning exploration, management, supply water
Language: Английский
Citations
37
Water, Journal Year: 2023, Volume and Issue: 15(9), P. 1750 - 1750
Published: May 2, 2023
Developing precise soft computing methods for groundwater management, which includes quality and quantity, is crucial improving water resources planning management. In the past 20 years, significant progress has been made in management using hybrid machine learning (ML) models as artificial intelligence (AI). Although various review articles have reported advances this field, existing literature must cover ML. This article aims to understand current state-of-the-art ML used achievements domain. It most cited employed from 2009 2022. summarises reviewed papers, highlighting their strengths weaknesses, performance criteria employed, highly identified. worth noting that accuracy was significantly enhanced, resulting a substantial improvement demonstrating robust outcome. Additionally, outlines recommendations future research directions enhance of including prediction related knowledge.
Language: Английский
Citations
33
Heliyon, Journal Year: 2024, Volume and Issue: 10(2), P. e24308 - e24308
Published: Jan. 1, 2024
Assessing groundwater potential for sustainable resource management is critically important. In addressing this concern, study aims to advance the field by developing an innovative approach Groundwater zone (GWPZ) mapping using advanced techniques, such as FuzzyAHP, FuzzyDEMATEL, and Logistic regression (LR) models. GWPZ was carried out integrating various primary factors, hydrologic, soil permeability, morphometric, terrain distribution, anthropogenic influences, incorporating twenty-seven individual criteria multi-criteria decision models along with a hybrid Subarnarekha River basin, India, in Google earth engine (GEE). The predictive capability of model evaluated Multi-Collinearity test (VIF <10.0), followed applying random forest model, considering weighted impact five factors. classification showed that 21.97 % (4256.3 km
Language: Английский
Citations
15
Water, Journal Year: 2024, Volume and Issue: 16(2), P. 194 - 194
Published: Jan. 5, 2024
Searching for new sources of water is becoming one the most important aspects scientific research, especially in areas prone to drought, like Saudi Arabia. The study aim was delineate groundwater potential zones within Oasis Al-Ahsa, Arabia’s eastern region, and identify optimum factors that control availability zones. This achieved through examining effect ten environmental variables on recharge, namely: slope, topographic wetness index (TWI), land cover (LC), elevation, lineament density (Ld), drainage (Dd), rainfall, geology, soil texture. were prepared from a variety data sources, including spatial (i.e., DEM Landsat-8 image), addition other complementing appropriate parameters extraction. Two weighted overlay methods used, namely simple additive weight (SAW) as well factor (OIF) order categorize optimal set computing GWP identifying its maps obtained validated comparison with locations existing wells at findings have assured cogency SAW map, where it found nearly 45–48% resultant characterized “moderate” class, whereas around 21–37% entire area classified “high” class. texture parameter determined being influencing mapping followed by “geology” parameter; however, “lineament density” (Ld) least factor. Furthermore, OIF method has facilitated identification combination delineating (GWP) zones, which included “Ld”, “land cover”, “TWI”. methodology can serve model similar regions, supporting sustainable resource management locally globally.
Language: Английский
Citations
10
Water, Journal Year: 2024, Volume and Issue: 16(4), P. 566 - 566
Published: Feb. 14, 2024
Groundwater management in the Jinan Spring basin is hampered by its complex topography, overexploitation, and excessive urbanisation. This has led to springs drying up during dry seasons a decrease discharge recent years. GIS AHP were employed delineate groundwater potential zones using eight thematic layers: slope, geology, lineament density, topographic wetness index (TWI), rainfall, soil, drainage land use/land cover (LULC). The model’s accuracy was assessed comparing findings observation well data. We found that 74% of observations matched projected zoning. Further validation utilising receiver operating characteristic (ROC) curve gave an AUC 0.736. According study, 67.31% good GWPZ, 5.60% very one, 27.07% medium, 0.03% low. Heavy rains throughout rainy season raise water levels. Dry weather lowers study’s conclusions will protect from climate change. Integrating hydrogeological data, GIS, remote sensing, approaches maximises data use, improves zone delineation, promotes sustainable resource decision making. integrated method can help use planners, hydrologists, policymakers find optimal locations for supply projects, establish techniques, reduce risks.
Language: Английский
Citations
10
Advances in Space Research, Journal Year: 2024, Volume and Issue: unknown
Published: Nov. 1, 2024
Language: Английский
Citations
10