Drainage morphometry and groundwater potential mapping: application of geoinformatics with frequency ratio and influencing factor approaches

Doke, Arjun; Pardeshi, Sudhakar D.; Das, Sumit

doi:10.1007/s12665-020-09137-6

Cited by 18 publications

(6 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, neither fault density nor distance to faults were ranked as having any importance by other ML methods (Classification and Regression Tree/CART or Boosted Regression Tree/BRT) they used. Other more recent studies have, in general, obtained results that have also shown that fault density is not an important indicator of groundwater potential, whereas the results for distance from faults varies from moderate importance to insignificant (e.g., [7,23,55,59]. In summary, although geological-structural elements such as faulting have an intuitive appeal as important controls on groundwater occurrence [15], since the presence and/or density of faults influence recharge processes [59] and groundwater migration patterns, ML based studies find they are of moderate or low utility as indicators of groundwater.…”

Section: Gcfs Of Low Importancementioning

confidence: 94%

See 1 more Smart Citation

Assessing Groundwater Potential in a Mid-Mountain Dryland Area of North-Central Chile through Geospatial Mapping

Deformes,

Núñez,

Fairley

et al. 2023

Water

View full text Add to dashboard Cite

This study utilized the Random Forest (RF) algorithm to assess groundwater potential (GWP) in the mid-mountain region of the Coquimbo region, north-central Chile. A comprehensive evaluation of twenty-one factors, primarily derived from Digital Elevation Models (DEM) and satellite data, was conducted against a database of 3822 groundwater discharge points. The majority of them consisted of shallow wells with relatively low yields. The main objective was to develop a groundwater potential (GWP) map for the study area. Among the factors considered, six variables, including two anthropogenic factors (distance to roads and presence of agricultural communities) and four natural factors (slope, elevation, concavity, and ruggedness index), were identified as the most influential indicators of GWP. The RF approach demonstrated excellent performance, achieving an Area Under the Curve (AUC) value of 0.95, sensitivity of 0.88, specificity of 0.86, and kappa coefficient of 0.74 in the test set. The majority of the study area exhibited low GWP, while only 14% of the area demonstrated high or very high GWP. In addition to providing valuable guidance for future hydrogeological investigations in the region, the GWP map serves as a valuable tool for identifying the areas that are most vulnerable to water shortages. This is particularly significant, as the region has been severely affected by extended drought, making water supply a critical concern.

show abstract

Section: Gcfs Of Low Importancementioning

confidence: 94%

“…The key output of the RF model is the groundwater potential map for the study area, presented in Figure 11. ence and/or density of faults influence recharge processes [59] and groundwater migration patterns, ML based studies find they are of moderate or low utility as indicators of groundwater.…”

Section: Groundwater Potential Mapmentioning

confidence: 99%

Assessing Groundwater Potential in a Mid-Mountain Dryland Area of North-Central Chile through Geospatial Mapping

Deformes,

Núñez,

Fairley

et al. 2023

Water

View full text Add to dashboard Cite

show abstract

“…They then performed a sensitivity analysis to determine the impact of hydrologic and geological factors on their variations. The widely used multi-criteria-based decision support techniques for GWP mapping include AHP [ 22 , [26] , [27] , [28] ], Frequency ratio (FR) [ 29 ], Logistic regression ( ) [ 30 ], Fuzzy set [ 31 ], Quick unbiased efficient statistical tree (QUEST) [ 32 ], Weighted linear combination (WLC) [ 33 ], Evidential belief function (EBF) [ 34 ], Multi-influencing factor ( ) [ 35 ], Shannon's entropy [ 36 ], TOPSIS [ 37 ], Dempster-Shafer model [ 38 ], Bayesian network model [ 39 ] etc. Causal relationships based on Fuzzy decision-making trial and evaluation laboratory (FDEMATEL) approaches have also been applied for soil erosion, flood, and landslide susceptibility mapping [ 40 , 41 ].…”

Section: Introductionmentioning

confidence: 99%

Mapping groundwater potential zone in the subarnarekha basin, India, using a novel hybrid multi-criteria approach in Google earth Engine

Singha,

Swain,

Pradhan

et al. 2024

Heliyon

View full text Add to dashboard Cite

“…Several geo-environmental factors (climate, hydrology, geology, soil, topography, drainage patterns) and several tools GIS (Geographical Information System) based on remote sensing have been utilized for the assessment of the groundwater potential zones (Bhunia 2020). Different multicriteria decision analysis (MCDA) strategies have been utilized by numerous researchers to identify groundwater potentiality such as frequency ratio (FR) (Guru et al 2017;Doke et al 2020), numerical modeling and decision tree (DT) (Lee and Lee 2015), and GIS-based Dempster-Shafer (DS) model (Mogaji et al 2015). Logistic regression (LR) (Pourtaghi and Pourghasemi 2014;Nguyen et al 2020), multivariate adaptive regression spline model (Zabihi et al 2016), certainty factor (CF) (Razandi et al 2015), random forest (RF) model (Naghibi et al 2020;Chen et al 2020), weight of evidence (WOE) (ChorbaniNejad et al 2017), and artificial neural network (ANN) (Lee et al 2018).…”

Section: Introductionmentioning

confidence: 99%

GIS-based comparative assessment of groundwater potential zone using MIF and AHP techniques in Cooch Behar district, West Bengal

Senapati

Das

2022

Appl Water Sci

View full text Add to dashboard Cite

Global warming, change in climate and rapid growth of population have put a huge strain on the world's groundwater resources. The use of excess groundwater has created a drop in the water table. Increased use of groundwater for agriculture in agro-based areas increases the demand for groundwater. The current study has been conducted in Cooch Behar, an agro-based remote district of North Bengal in the Indian state of West Bengal, where the use of high groundwater in agriculture has reduced groundwater levels. Comparative assessment of Multi-Influencing Factor (MIF) and Analytical Hierarchy Process (AHP) techniques has been used to create a perspective mapping of groundwater potential zone for the research region. Land use and land cover (LULC), rainfall, soil texture, geomorphology, lithology, drainage density, Normalized Differential Vegetation Index (NDVI), and Topographic Wetness Index (TWI) have been used to create the map. The MIF and AHP techniques have been used for Groundwater Potential Zones (GPZ) mapping. Output GPZ has been classified into 5 classes, i.e., very poor, poor, moderate, good, and excellent. MIF technique shows that the groundwater potential classes are covering 9% (285 km2), 21% (67 km2), 30% (944 km2), 27% (837 km2) and 13% (410 km2) of the study area, respectively, whereas the output GPZ from AHP technique shows that the groundwater potential classes are covering 8% (271 km2), 15% (508 km2), 24% (813 km2), 33% (1118 km2) and 20% (677 km2), respectively. Finally, the maps have been verified using groundwater fluctuation data through Receivers Operating Characteristic curve (ROC). The MIF technique's Area Under Curve (AUC) score is 76.5%, while the AHP technique reveals 88.9% accuracy. Both techniques for assessing and monitoring GPZ have been accurate and reasonable. Thus, this type of research is reliable for a more appropriate framework for swiftly analyzing groundwater recharge and directing the location of artificial recharge structures and other groundwater management operations of agricultural-based areas.

show abstract

Drainage morphometry and groundwater potential mapping: application of geoinformatics with frequency ratio and influencing factor approaches

Cited by 18 publications

References 73 publications

Assessing Groundwater Potential in a Mid-Mountain Dryland Area of North-Central Chile through Geospatial Mapping

Assessing Groundwater Potential in a Mid-Mountain Dryland Area of North-Central Chile through Geospatial Mapping

Mapping groundwater potential zone in the subarnarekha basin, India, using a novel hybrid multi-criteria approach in Google earth Engine

GIS-based comparative assessment of groundwater potential zone using MIF and AHP techniques in Cooch Behar district, West Bengal

Contact Info

Product

Resources

About