Unleashing the Untapped Potential: Groundwater Exploration in a Watershed Environment of North‐East India Using MCDAAHP Techniques DOI Open Access
Debashree Borah,

Ashok Kumar Bora

Hydrological Processes, Год журнала: 2025, Номер 39(1)

Опубликована: Янв. 1, 2025

ABSTRACT The contemporary era is marked by the faster exploitation of groundwater resources due to combined effects burgeoning population and rapid industrialisation. This study tries delineate potential zones (GWPZs) in a fragile agriculturally dominant watershed North‐East India using GIS‐based multi‐criteria decision analysis (MCDA) approach Analytical Hierarchy Process (AHP) technique. has undertaken 10 influencing factors: geomorphology, geology, land use/land cover (LU/LC), drainage density, rainfall, soil texture, slope, lineament topographic wetness index (TWI) normalised difference water (NDWI). Suitable weights for parameters are assigned according their relative importance association with storage based on pairwise comparison matrix (PCM). Four GWPZs respective coverages namely poor (3.39%), moderate (24.98%), good (33.36%) excellent (38.27%) categories found. central southern parts area covering portion Udalguri, Sonitpur Darrang districts Assam have porous geological settings floodplains, indicating high potentiality. In contrast, northern part hard rugged terrain lacks storage. Incorporating socio‐economic aspect, particularly number villages or without access suitable groundwater, significantly enhances study's utility. outcome cross‐verified well data obtained from Central Groundwater Board (CGWB) field which validated receiver operating characteristics (ROC) curve resulting an accuracy 72.9%. Hence, this inquiry implications both regional global significance will assist stakeholders authorities creating roadmap sustainable effective use.

Язык: Английский

Integration of hydrogeological data, GIS and AHP techniques applied to delineate groundwater potential zones in sandstone, limestone and shales rocks of the Damoh district, (MP) central India DOI
Kanak N. Moharir,

Chaitanya B. Pande,

Vinay Kumar Gautam

и другие.

Environmental Research, Год журнала: 2023, Номер 228, С. 115832 - 115832

Опубликована: Апрель 11, 2023

Язык: Английский

Процитировано

125

Delineation of Groundwater Potential Zones (GWPZs) in a Semi-Arid Basin through Remote Sensing, GIS, and AHP Approaches DOI Open Access
José Luis Uc Castillo, Diego Armando Martínez Cruz, José Alfredo Ramos‐Leal

и другие.

Water, Год журнала: 2022, Номер 14(13), С. 2138 - 2138

Опубликована: Июль 5, 2022

Groundwater occurrence in semi-arid regions is variable space and time due to climate patterns, terrain features, aquifer properties. Thus, accurate delineation of Potential Zones (GWPZs) essential for sustainable water resources management these environments. The present research aims delineate assess GWPZs a basin San Luis Potosi (SLP), Mexico, through the integration Remote Sensing (RS), Geographic Information System (GIS), Analytic Hierarchy Process (AHP). Seven thematic layers (geology, lineament density, land use cover, topographic wetness index (TWI), rainfall, drainage slope) were generated raster format. After AHP procedure rank assignment, integrated using calculator obtain map. results indicated that 68.21% area classified as low groundwater potential, whereas 26.30% moderate. Validation was done by assessing residence data from 15 wells distributed study area. Furthermore, Receiver Operating Characteristics (ROC) curve obtained, indicating satisfactory accuracy prediction (AUC = 0.677). This provides valuable information decision-makers regarding conservation resources.

Язык: Английский

Процитировано

81

Applications of machine learning to water resources management: A review of present status and future opportunities DOI Creative Commons
Ashraf Ahmed,

Sakina Sayed,

Antoifi Abdoulhalik

и другие.

Journal of Cleaner Production, Год журнала: 2024, Номер 441, С. 140715 - 140715

Опубликована: Янв. 11, 2024

Water is the most valuable natural resource on earth that plays a critical role in socio-economic development of humans worldwide. used for various purposes, including, but not limited to, drinking, recreation, irrigation, and hydropower production. The expected population growth at global scale, coupled with predicted climate change-induced impacts, warrants need proactive effective management water resources. Over recent decades, machine learning tools have been widely applied to resources management-related fields often shown promising results. Despite publication several review articles applications water-related fields, this paper presents first time comprehensive techniques management, focusing achievements. study examines potential advanced improve decision support systems sectors within realm which includes groundwater streamflow forecasting, distribution systems, quality wastewater treatment, demand consumption, marine energy, drainage flood defence. This provides an overview state-of-the-art approaches industry how they can be ensure supply sustainability, quality, drought mitigation. covers related studies provide snapshot industry. Overall, LSTM networks proven exhibit reliable performance, outperforming ANN models, traditional established physics-based models. Hybrid ML exhibited great forecasting accuracy across all showing superior computational power over ANNs architectures. In addition purely data-driven physical-based hybrid models also developed prediction performance. These efforts further demonstrate Machine powerful practical tool management. It insights, predictions, optimisation capabilities help enhance sustainable use development, healthy ecosystems human existence.

Язык: Английский

Процитировано

63

Developing a novel tool for assessing the groundwater incorporating water quality index and machine learning approach DOI Creative Commons
Abdul Majed Sajib, Mir Talas Mahammad Diganta, Azizur Rahman

и другие.

Groundwater for Sustainable Development, Год журнала: 2023, Номер 23, С. 101049 - 101049

Опубликована: Ноя. 1, 2023

Groundwater plays a pivotal role as global source of drinking water. To meet sustainable development goals, it is crucial to consistently monitor and manage groundwater quality. Despite its significance, there are currently no specific tools available for assessing trace/heavy metal contamination in groundwater. Addressing this gap, our research introduces an innovative approach: the Quality Index (GWQI) model, developed tested Savar sub-district Bangladesh. The GWQI model integrates ten water quality indicators, including six heavy metals, collected from 38 sampling sites study area. enhance precision assessment, employed established machine learning (ML) techniques, evaluating model's performance based on factors such uncertainty, sensitivity, reliability. A major advancement incorporation metals into framework index model. best authors knowledge, marks first initiative develop encompassing heavy/trace elements. Findings assessment revealed that area ranged 'good' 'fair,' indicating most indicators met standard limits set by Bangladesh government World Health Organization. In predicting scores, artificial neural networks (ANN) outperformed other ML models. Performance metrics, root mean square error (RMSE), (MSE), absolute (MAE) training (RMSE = 0.361; MSE 0.131; MAE 0.262), testing 0.001; 0.00; 0.001), prediction evaluation statistics (PBIAS 0.000), demonstrated superior effectiveness ANN. Moreover, exhibited high sensitivity (R2 1.0) low uncertainty (less than 2%) rating These results affirm reliability novel monitoring management, especially regarding metals.

Язык: Английский

Процитировано

61

Hydrogeochemical assessment of groundwater quality for drinking and irrigation applying groundwater quality index (GWQI) and irrigation water quality index (IWQI) DOI
Arijit Ghosh, Biswajit Bera

Groundwater for Sustainable Development, Год журнала: 2023, Номер 22, С. 100958 - 100958

Опубликована: Май 11, 2023

Язык: Английский

Процитировано

36

Assessing groundwater artificial recharge suitability in the Mi River basin using GIS, RS, and FAHP: a comprehensive analysis with seasonal variations DOI Creative Commons
Qilong Song, Yuyu Liu,

Zhongjie Wang

и другие.

Applied Water Science, Год журнала: 2025, Номер 15(2)

Опубликована: Янв. 29, 2025

Язык: Английский

Процитировано

2

A systematic review and meta-analysis of groundwater level forecasting with machine learning techniques: Current status and future directions DOI
José Luis Uc Castillo, Ana Elizabeth Marín Celestino, Diego Armando Martínez Cruz

и другие.

Environmental Modelling & Software, Год журнала: 2023, Номер 168, С. 105788 - 105788

Опубликована: Авг. 2, 2023

Язык: Английский

Процитировано

20

Landform classification and geomorphological mapping of the Chota Nagpur Plateau, India DOI Creative Commons
Arijit Ghosh, Biswajit Bera

Quaternary Science Advances, Год журнала: 2023, Номер 10, С. 100082 - 100082

Опубликована: Март 22, 2023

Geomorphological map plays a key role to illustrate landscape evolutionary history along with the guidelines of sustainable landuse planning. The Chota Nagpur Plateau is situated in eastern part Indian subcontinent and it storehouse valuable rocks minerals Precambrian origin. Classification geomorphological units highly required for planning natural hazards management. So, principal objective this scientific study classify region into micro by applying automated semi-automated methods. Digital elevation model (DEM) data satellite imageries (from United States Geological Survey) have been used improve precision map. mapping techniques such as terrain attributes classification, Topographical Position Index (TPI) Slope (SPI) applied extract major or features. TPI values show that maximum area comes under valley bottom stream (35.26%) followed high ridge (24.55%) whereas minimum coverage found open slope zone (0.02%). Local ridges mid-slope lie 8.77% 4.94% respectively. result has verified through field verification help GPS data. This high-accuracy should be regional These also give very good results classification well landforms evolution.

Язык: Английский

Процитировано

19

Application of bagging and boosting ensemble machine learning techniques for groundwater potential mapping in a drought-prone agriculture region of eastern India DOI Creative Commons

Krishnagopal Halder,

Amit Kumar Srivastava,

Anitabha Ghosh

и другие.

Environmental Sciences Europe, Год журнала: 2024, Номер 36(1)

Опубликована: Сен. 2, 2024

Groundwater is a primary source of drinking water for billions worldwide. It plays crucial role in irrigation, domestic, and industrial uses, significantly contributes to drought resilience various regions. However, excessive groundwater discharge has left many areas vulnerable potable shortages. Therefore, assessing potential zones (GWPZ) essential implementing sustainable management practices ensure the availability present future generations. This study aims delineate with high Bankura district West Bengal using four machine learning methods: Random Forest (RF), Adaptive Boosting (AdaBoost), Extreme Gradient (XGBoost), Voting Ensemble (VE). The models used 161 data points, comprising 70% training dataset, identify significant correlations between presence absence region. Among methods, (RF) (XGBoost) proved be most effective mapping potential, suggesting their applicability other regions similar hydrogeological conditions. performance metrics RF are very good precision 0.919, recall 0.971, F1-score 0.944, accuracy 0.943. indicates strong capability accurately predict minimal false positives negatives. (AdaBoost) demonstrated comparable across all (precision: recall: F1-score: accuracy: 0.943), highlighting its effectiveness predicting accurately; whereas, outperformed slightly, higher values metrics: (0.944), (0.971), (0.958), (0.957), more refined model performance. (VE) approach also showed enhanced performance, mirroring XGBoost's 0.958, 0.957). that combining strengths individual leads better predictions. potentiality zoning varied significantly, low accounting 41.81% at 24.35%. uncertainty predictions ranged from 0.0 0.75 area, reflecting variability need targeted strategies. In summary, this highlights critical managing resources effectively advanced techniques. findings provide foundation practices, ensuring use conservation beyond.

Язык: Английский

Процитировано

7

Mapping Prospective Areas of Water Resources and Monitoring Land Use/Land Cover Changes in an Arid Region Using Remote Sensing and GIS Techniques DOI Open Access
Tong Sun, Wuqun Cheng, Mohamed Abdelkareem

и другие.

Water, Год журнала: 2022, Номер 14(15), С. 2435 - 2435

Опубликована: Авг. 6, 2022

Groundwater is a vital water resource for economic, agricultural, and domestic purposes in arid regions. To reduce scarcity regions, recently, remote sensing GIS techniques have been successfully applied to predict areas with prospective resources. Thus, this study attempted spatially reveal groundwater potential zones (GWPZs) conduct change detection on the desert fringes of Wadi Asyuti, defunct tributary Egypt’s Nile basin eastern Sahara. Eleven influential factors generated from imagery, geological, hydrological, climatic conditions were combined after giving weight each factor through GIS-based Analytical Hierarchy Process (AHP) coupled weighted overlay technique (WOT). The results revealed six distinctive scores ranging very low (10.59%) excellent (3.03%). Thirty-three productive wells, Interferometry Synthetic Aperture Radar (InSAR) coherence (CCD), land use map derived Sentinel-2, delineated flooding zone Landsat-8 data used validate zones. GWPZs indicated that 48% collected wells can be classified as consistent excellent. Normalized Difference Vegetation Index (NDVI) image classification multi-temporal Landsat series Sentinel-2 along InSAR CCD Sentinel-1 images dramatic changes use/land cover (LU/LC) terms agricultural other anthropogenic activities structurally downstream area, which most promising area future developments. Overall, integration radar multispectral has ability provide valuable information about resources tested model technique, such extremely significant guidance planners decision makers sustainable development.

Язык: Английский

Процитировано

27