Application of geospatial and machine learning algorithms to predict (under certain limitations) the quality of groundwater used for irrigation purposes DOI Creative Commons
Hemant Raheja, Arun Goel, Mahesh Pal

et al.

Water Science & Technology Water Supply, Journal Year: 2024, Volume and Issue: 24(11), P. 3724 - 3743

Published: Nov. 1, 2024

ABSTRACT The main objective of the present study is to evaluate groundwater quality for irrigation purposes in central-western part Haryana state (India). For this, 272 samples were collected during pre- and post-monsoon periods 2022. Several indices, including SAR, PI, Na%, KR, magnesium adsorption ratio (MAR), IWQI derived. results KR values indicate that generally suitable irrigation. On other hand, PI MAR exceeded established limits, primarily showing issues related salinity content groundwater. Furthermore, according classification, 47.06 25% total fell under ‘severe restriction irrigation’ category pre-monsoon periods, respectively. Spatial variation maps water western portion area unsuitable both periods. Three ML algorithms, namely RF, SVM, XGBoost integrated validated predict IWQI. revealed with random search achieves best prediction performances. approaches this have been confirmed be cost-effective feasible quality, using hydrochemical parameters as input variables, highly beneficial resource planning management.

Language: Английский

Interpreting optimised data-driven solution with explainable artificial intelligence (XAI) for water quality assessment for better decision-making in pollution management DOI
Javed Mallick, Saeed Alqadhi, Hoang Thi Hang

et al.

Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: 31(30), P. 42948 - 42969

Published: June 17, 2024

Language: Английский

Citations

5
Groundwater Quality Prediction Using Proximal Hyperspectral Sensing, GIS, and Machine Learning Algorithms DOI
Hemant Raheja, Arun Goel, Mahesh Pal

et al.

Water Air & Soil Pollution, Journal Year: 2025, Volume and Issue: 236(6)

Published: April 24, 2025

Language: Английский

Citations

0
GROUNDWATER POTENTIAL ASSESSMENT IN GIA LAI PROVINCE (VIETNAM) USING MACHINE LEARNING, REMOTE SENSING AND GIS DOI Open Access
Huu Duy Nguyen,

Van Trong Giang,

Quang-Hai TRUONG

et al.

Geographia Technica, Journal Year: 2024, Volume and Issue: 19(2/2024), P. 13 - 32

Published: May 15, 2024

Population growth, urbanization and rapid industrial development increase the demand for water resources.Groundwater is an important resource in sustainable socio-economic development.The identification of regions with probability existence groundwater necessary helping decision makers to propose effective strategies management this resource.The objective study construct maps potential groundwater, based on machine learning algorithms, namely deep neural networks (DNNs), XGBoost (XGB), CatBoost (CB), Gia Lai province Vietnam.In study, 12 conditioning factors, elevation, aspect, curvature, slope, soil type, river density, distance road, land use/land cover (LULC), Normalized Difference Vegetation Index (NDVI), Normal Built-up (NDBI), Water (NDWI), rainfall were used, along 181 inventory points, models.The proposed models evaluated using receiver operating characteristic (ROC) curve, area under curve (AUC), root-mean-square error (RMSE), mean absolute (MAE).The results showed that predictions most accurate XGB model; CB came second, DNN was performed least well.About 4,990 km² found be category very low potential; 3,045 category; 2,426 classified as moderate, 2,665 high, 2,007 high.The methodology used creating maps.This approach, can provide valuable information factors influencing assist decisionmakers or developers managing resources sustainably.It also supports territory, including tourism.This other geographic a small change input data.

Language: Английский

Citations

0
Predicting Water Purity by Riding the Ensemble Waves with Gradient Boosting Classification Technique DOI

Ruchika Bhuria,

Kanwarpartap Singh Gill,

Deepak Upadhyay

et al.

Published: July 10, 2024

Language: Английский

Citations

0
Assessment of groundwater quality and its vulnerability for safe drinking purpose DOI Creative Commons
Hemant Raheja, Arun Goel, Mahesh Pal

et al.

Journal of Hydroinformatics, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 9, 2024

ABSTRACT Groundwater is a main resource of drinking water in several parts India. Its degradation poses significant risk to availability and human health, highlighting the importance regularly evaluating groundwater quality these regions. Thus, aim this study examine map its vulnerability for purposes using EWQI, PIG, GOD methods. The area found be generally alkaline nature. More than 20% samples exceeded desirable limit TH. Correlations major ions revealed that were distributed areas silicate weathering dolomite dissolution. EWQI values vary from 33.74 62.22, with an average value 41.54. spatial distribution diagrams hydrochemical parameters represent poor southern southern-western areas. PIG ranged 0.49 0.84, 0.59. Moreover, method indicates part region has moderate demonstrates level factor calculation vulnerability.

Language: Английский

Citations

0
Application of geospatial and machine learning algorithms to predict (under certain limitations) the quality of groundwater used for irrigation purposes DOI Creative Commons
Hemant Raheja, Arun Goel, Mahesh Pal

et al.

Water Science & Technology Water Supply, Journal Year: 2024, Volume and Issue: 24(11), P. 3724 - 3743

Published: Nov. 1, 2024

ABSTRACT The main objective of the present study is to evaluate groundwater quality for irrigation purposes in central-western part Haryana state (India). For this, 272 samples were collected during pre- and post-monsoon periods 2022. Several indices, including SAR, PI, Na%, KR, magnesium adsorption ratio (MAR), IWQI derived. results KR values indicate that generally suitable irrigation. On other hand, PI MAR exceeded established limits, primarily showing issues related salinity content groundwater. Furthermore, according classification, 47.06 25% total fell under ‘severe restriction irrigation’ category pre-monsoon periods, respectively. Spatial variation maps water western portion area unsuitable both periods. Three ML algorithms, namely RF, SVM, XGBoost integrated validated predict IWQI. revealed with random search achieves best prediction performances. approaches this have been confirmed be cost-effective feasible quality, using hydrochemical parameters as input variables, highly beneficial resource planning management.

Language: Английский

Citations

0