Predicting groundwater recharge potential zones using geospatial technique

Akter, Aysha; Uddin, Abir Md Humam; Wahid, Khalid Ben; Ahmed, Shoukat

doi:10.1007/s40899-020-00384-w

Cited by 20 publications

(8 citation statements)

References 40 publications

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“…The climate in this basin is sub-tropical monsoonal, with two distinct seasons: monsoon (June to September) and dry season (October to December) (October to May). The average annual precipitation in this basin is over 1900 mm (Akter et al, 2019), with over 80% of overall annual precipitation taking place during the monsoon season.…”

Section: Study Areamentioning

confidence: 99%

Groundwater potentiality mapping using ensemble machine learning algorithms for sustainable groundwater management

Sarkar

Talukdar

Rahman

et al. 2021

FEBE

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PurposeThe present study aims to construct ensemble machine learning (EML) algorithms for groundwater potentiality mapping (GPM) in the Teesta River basin of Bangladesh, including random forest (RF) and random subspace (RSS).Design/methodology/approachThe RF and RSS models have been implemented for integrating 14 selected groundwater condition parametres with groundwater inventories for generating GPMs. The GPM were then validated using the empirical and bionormal receiver operating characteristics (ROC) curve.FindingsThe very high (831–1200 km2) and high groundwater potential areas (521–680 km2) were predicted using EML algorithms. The RSS (AUC-0.892) model outperformed RF model based on ROC's area under curve (AUC).Originality/valueTwo new EML models have been constructed for GPM. These findings will aid in proposing sustainable water resource management plans.

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Section: Study Areamentioning

confidence: 99%

Groundwater potentiality mapping using ensemble machine learning algorithms for sustainable groundwater management

Sarkar

Talukdar

Rahman

et al. 2021

FEBE

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“…Drainage density is the ratio of all the streams over the area to the total area [65]. It indicates the drainage capacity and measures the drainage over a particular watershed [103]. A higher drainage density region indicates a well-distributed water flow area with multiple streams contributing to the flow and recharge and vice versa.…”

Section: Drainage Densitymentioning

confidence: 99%

Delineating Groundwater Recharge Potential through Remote Sensing and Geographical Information Systems

Maqsoom

Aslam

Khalid

et al. 2022

Water

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Owing to the extensive global dependency on groundwater and associated increasing water demand, the global groundwater level is declining rapidly. In the case of Islamabad, Pakistan, the groundwater level has lowered five times over the past five years due to extensive pumping by various departments and residents to meet the local water requirements. To address this, water reservoirs and sources need to be delineated, and potential recharge zones are highlighted to assess the recharge potential. Therefore, the current study utilizes an integrated approach based on remote sensing (RS) and GIS using the influence factor (IF) technique to delineate potential groundwater recharge zones in Islamabad, Pakistan. Soil map of Pakistan, Landsat 8TM satellite data, digital elevation model (ASTER DEM), and local geological map were used in the study for the preparation of thematic maps of 15 key contributing factors considered in this study. To generate a combined groundwater recharge map, rate and weightage values were assigned to each factor representing their mutual influence and recharge capabilities. To analyze the final combined recharge map, five different assessment analogies were used in the study: poor, low, medium, high, and best. The final recharge potential map for Islamabad classifies 15% (136.8 km2) of the region as the “best” zone for extracting groundwater. Furthermore, high, medium, low, and poor ranks were assigned to 21%, 24%, 27%, and 13% of the region with respective areas of 191.52 km2, 218.88 km2, 246.24 km2, and 118.56 km2. Overall, this research outlines the best to least favorable zones in Islamabad regarding groundwater recharge potentials. This can help the authorities devise mitigation strategies and preserve the natural terrain in the regions with the best groundwater recharge potential. This is aligned with the aims of the interior ministry of Pakistan for constructing small reservoirs and ponds in the existing natural streams and installing recharging wells to maintain the groundwater level in cities. Other countries can expand upon and adapt this study to delineate local groundwater recharge potentials.

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“…The drainage of the study area was also extracted from the DEM using the hydrology tool in ArcMap 10.2, and the drainage density was calculated and plotted using the line density tool. By using the Line module in PCI Geomatica v8 on the Landsat scene, the lineament map of the area was automatically extracted [32][33][34], from which the line density tool in ArcGIS 10.2 was again used to calculate and make the lineament density. The LULC was extracted from the Landsat 8 scene following a supervised classification with the Classification tool in ArcMap 10.2 (https:// appli edsci ences.…”

Section: Preparation Of Thematic Mapsmentioning

confidence: 99%

Remote sensing and geographic information system (GIS)-based high-resolution mapping of potential groundwater recharge zones on the hard rock terrains of the Cameroon volcanic line (CVL)

et al. 2022

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Groundwater is the major water reserve in the present context of global warming-related droughts that appear to be more intense in hard rock terrains. The use of Geographical Information Systems (GIS) and Remote Sensing (RS) technologies are increasingly beneficial to groundwater research, by allowing for low cost and larger-scale high-resolution mapping compared to conventional hydrogeological exploration methods. This study aimed at developing a high-resolution map of potential groundwater recharge (GWRpot) zones for the drought-stricken Banka hard rock terrain, straddling the Cameroon Volcanic Line (CVL). Shuttle Radar Tomography Mission (SRTM)-30m and Landsat 8 satellite images constituted the main data source that was ground-truthed through field mapping and used to produce various thematic GIS layers: geology slope, aspect, land use & land cover, drainage density and lineament density of spatial resolution 16m x 16m. The layers were each attributed a fixed score and weight to groundwater recharge, computed using Multi-Influencing Factor (MIF) and Analytical Hierarchy Process of Multi-Criteria Decision Analysis (AHP-MCDA) techniques. Lastly, a Weighted Overlay Analysis was done using the layers to produce the GWRpot zones for the study area. The resulting map shows that 60% of the study area, covering the south, west-northwest and the north-northeast portions of the map have moderate to very high recharge potentials. This result is particularly useful for groundwater targeting in the area and demonstrates the effectiveness of the method in hard rock terrains where traditional methods have been less efficient in properly delineating groundwater recharge zones. Article highlights Groundwater is generally the safest and most reliable source of water in water scarce environments, and forms when surface water goes into the ground. More areas where water can enter the ground means more chances of getting enough groundwater. Conventional ways of knowing these areas are time consuming and costly, whereas RS and GIS-based methods are less costly and use lesser time. We used the RS, GIS, high-resolution field mapping and statistical methods of blending several factors to produce the potential groundwater recharge zones on a part of the hardrocks of the CVL, that shows 60% of the study area, covering the south, west-northwest and the north-northeast portions of the area have moderate to very high recharge potentials.

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Predicting groundwater recharge potential zones using geospatial technique

Cited by 20 publications

References 40 publications

Groundwater potentiality mapping using ensemble machine learning algorithms for sustainable groundwater management

Groundwater potentiality mapping using ensemble machine learning algorithms for sustainable groundwater management

Delineating Groundwater Recharge Potential through Remote Sensing and Geographical Information Systems

Remote sensing and geographic information system (GIS)-based high-resolution mapping of potential groundwater recharge zones on the hard rock terrains of the Cameroon volcanic line (CVL)

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