Groundwater Augmentation through the Site Selection of Floodwater Spreading Using a Data Mining Approach (Case study: Mashhad Plain, Iran)

Naghibi, Seyed Amir; Vafakhah, Mehdi; Hashemi, Hossein; Pradhan, Biswajeet; Alavi, Seyed Jalil

doi:10.3390/w10101405

Cited by 30 publications

(11 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lower soil moisture, together with a higher infiltration rate around termite mounds, is likely to enhance the recharge rate of underlying groundwater reservoirs and consequently present a more viable potential source of groundwater compared to surrounding areas. Places with high rates of infiltration have been suggested as suitable for groundwater augmentation through artificial recharge [71].…”

Section: Resultsmentioning

confidence: 99%

Aquifer Potential Assessment in Termites Manifested Locales Using Geo-Electrical and Surface Hydraulic Measurement Parameters

Ahmed

Pradhan

Mansor

et al. 2019

Sensors

Self Cite

View full text Add to dashboard Cite

In some parts of tropical Africa, termite mound locations are traditionally used to site groundwater structures mainly in the form of hand-dug wells with high success rates. However, the scientific rationale behind the use of mounds as prospective sites for locating groundwater structures has not been thoroughly investigated. In this paper, locations and structural features of termite mounds were mapped with the aim of determining the aquifer potential beneath termite mounds and comparing the same with adjacent areas, 10 m away. Soil and species sampling, field surveys and laboratory analyses to obtain data on physical, hydraulic and geo-electrical parameters from termite mounds and adjacent control areas followed. The physical and hydraulic measurements demonstrated relatively higher infiltration rates and lower soil water content on mound soils compared with the surrounding areas. To assess the aquifer potential, vertical electrical soundings were conducted on 28 termite mounds sites and adjacent control areas. Three (3) important parameters were assessed to compute potential weights for each Vertical Electrical Sounding (VES) point: Depth to bedrock, aquifer layer resistivity and fresh/fractured bedrock resistivity. These weights were then compared between those of termite mound sites and those from control areas. The result revealed that about 43% of mound sites have greater aquifer potential compared to the surrounding areas, whereas 28.5% of mounds have equal and lower potentials compared with the surrounding areas. The study concludes that termite mounds locations are suitable spots for groundwater prospecting owing to the deeper regolith layer beneath them which suggests that termites either have the ability to locate places with a deeper weathering horizon or are themselves agents of biological weathering. Further studies to check how representative our study area is of other areas with similar termite activities are recommended.

show abstract

Section: Resultsmentioning

confidence: 99%

Aquifer Potential Assessment in Termites Manifested Locales Using Geo-Electrical and Surface Hydraulic Measurement Parameters

Ahmed

Pradhan

Mansor

et al. 2019

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…The performance of the logistic model tree, deep boosting, boosted regression trees, and k-nearest neighbors in the investigation of GW potential was assessed by utilizing the receiver operating characteristic (ROC) curve, accuracy, kappa, sensitivity, and specificity [8,12,[80][81][82]. To quantify the prediction accuracy, the area under the ROC curve (AUC) was computed.…”

Section: Validation Of the Algorithmsmentioning

confidence: 99%

Application of Advanced Machine Learning Algorithms to Assess Groundwater Potential Using Remote Sensing-Derived Data

et al. 2020

Self Cite

View full text Add to dashboard Cite

Groundwater (GW) is being uncontrollably exploited in various parts of the world resulting from huge needs for water supply as an outcome of population growth and industrialization. Bearing in mind the importance of GW potential assessment in reaching sustainability, this study seeks to use remote sensing (RS)-derived driving factors as an input of the advanced machine learning algorithms (MLAs), comprising deep boosting and logistic model trees to evaluate their efficiency. To do so, their results are compared with three benchmark MLAs such as boosted regression trees, k-nearest neighbors, and random forest. For this purpose, we firstly assembled different topographical, hydrological, RS-based, and lithological driving factors such as altitude, slope degree, aspect, slope length, plan curvature, profile curvature, relative slope position, distance from rivers, river density, topographic wetness index, land use/land cover (LULC), normalized difference vegetation index (NDVI), distance from lineament, lineament density, and lithology. The GW spring indicator was divided into two classes for training (434 springs) and validation (186 springs) with a proportion of 70:30. The training dataset of the springs accompanied by the driving factors were incorporated into the MLAs and the outputs were validated by different indices such as accuracy, kappa, receiver operating characteristics (ROC) curve, specificity, and sensitivity. Based upon the area under the ROC curve, the logistic model tree (87.813%) generated similar performance to deep boosting (87.807%), followed by boosted regression trees (87.397%), random forest (86.466%), and k-nearest neighbors (76.708%) MLAs. The findings confirm the great performance of the logistic model tree and deep boosting algorithms in modelling GW potential. Thus, their application can be suggested for other areas to obtain an insight about GW-related barriers toward sustainability. Further, the outcome based on the logistic model tree algorithm depicts the high impact of the RS-based factor, such as NDVI with 100 relative influence, as well as high influence of the distance from river, altitude, and RSP variables with 46.07, 43.47, and 37.20 relative influence, respectively, on GW potential.

show abstract

“…where max and min are the largest and smallest values of cells in a rectangular neighborhood of nine adjacent elevation values [37]. Slope aspect is also important for its effect on evaporation, soil moisture, and vegetation growth that may improve or inhibit infiltration [32,33]. Surface curvature affects runoff and infiltration as well.…”

Section: Factors Influencing Groundwater In Karst Regions (Figkrs)mentioning

confidence: 99%

Assessment of Ensemble Models for Groundwater Potential Modeling and Prediction in a Karst Watershed

Farzin

Avand

Ahmadzadeh

et al. 2021

Water

View full text Add to dashboard Cite

Due to numerous droughts in recent years, the amount of surface water in arid and semi-arid regions has decreased significantly, so reliance on groundwater to meet local and regional demands has increased. The Kabgian watershed is a karst watershed in southwestern Iran that provides a significant proportion of drinking and agriculture water supplies in the area. This study identified areas with karst groundwater potential using a combination of machine learning and statistical models, including entropy-SVM-LN, entropy-SVM-SG, and entropy-SVM-RBF. To do this, 384 karst springs were identified and mapped. Sixteen factors that are related to karst potential were identified from a review of the literature, and these were compiled for the study area. The 384 locations were randomly separated into two categories for training (269 location) and validation (115 location) datasets to be used in the modeling process. The ROC curve was used to evaluate the modeling results. The models used, in general, were good at determining the location of karst groundwater potential. The evaluation showed that the E-SVM-RBF model had an area under the curve of 0.92, indicating that it was most accurate estimator of groundwater potential among the ensemble models. Evaluation of the relative importance of each of the 16 factors revealed that land use, a vector ruggedness measure, curvature, and topography roughness index were the most important explainers of the presence of karst groundwater in the study area. It was also found that the factors affecting the presence of karst springs are significantly different from non-karst springs.

show abstract

Groundwater Augmentation through the Site Selection of Floodwater Spreading Using a Data Mining Approach (Case study: Mashhad Plain, Iran)

Cited by 30 publications

References 62 publications

Aquifer Potential Assessment in Termites Manifested Locales Using Geo-Electrical and Surface Hydraulic Measurement Parameters

Aquifer Potential Assessment in Termites Manifested Locales Using Geo-Electrical and Surface Hydraulic Measurement Parameters

Application of Advanced Machine Learning Algorithms to Assess Groundwater Potential Using Remote Sensing-Derived Data

Assessment of Ensemble Models for Groundwater Potential Modeling and Prediction in a Karst Watershed

Contact Info

Product

Resources

About