Application of Mathematical Models in Defining Optimal Groundwater Yield

Hadžić, Emina; Lazović, Nerma; Mulaomerović-Šeta, Ajla

doi:10.1016/j.proenv.2015.04.016

Cited by 4 publications

(1 citation statement)

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“…GIS technologies have been used for quantitative analysis of spatial distributions in environmental, geological, and hydrological studies [17][18][19]. One limitation of data-based analysis of groundwater is insufficient availability of data for analysis [20]; groundwater yield varies with hydrological conditions and recharge sources, which have been measured in a limited number of groundwater wells [21]. Therefore, using various models to predict groundwater yield accurately and identifying the optimal model for water resource evaluation in a given region are essential to effective water resource management.…”

Section: Introductionmentioning

confidence: 99%

Groundwater Potential Mapping Using Remote Sensing and GIS-Based Machine Learning Techniques

et al. 2020

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Adequate groundwater development for the rural population is essential because groundwater is an important source of drinking water and agricultural water. In this study, ensemble models of decision tree-based machine learning algorithms were used with geographic information system (GIS) to map and test groundwater yield potential in Yangpyeong-gun, South Korea. Groundwater control factors derived from remote sensing data were used for mapping, including nine topographic factors, two hydrological factors, forest type, soil material, land use, and two geological factors. A total of 53 well locations with both specific capacity (SPC) data and transmissivity (T) data were selected and randomly divided into two classes for model training (70%) and testing (30%). First, the frequency ratio (FR) was calculated for SPC and T, and then the boosted classification tree (BCT) method of the machine learning model was applied. In addition, an ensemble model, FR-BCT, was applied to generate and compare groundwater potential maps. Model performance was evaluated using the receiver operating characteristic (ROC) method. To test the model, the area under the ROC curve was calculated; the curve for the predicted dataset of SPC showed values of 80.48% and 87.75% for the BCT and FR-BCT models, respectively. The accuracy rates from T were 72.27% and 81.49% for the BCT and FR-BCT models, respectively. Both the BCT and FR-BCT models measured the contributions of individual groundwater control factors, which showed that soil was the most influential factor. The machine learning techniques used in this study showed effective modeling of groundwater potential in areas where data are relatively scarce. The results of this study may be used for sustainable development of groundwater resources by identifying areas of high groundwater potential.

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Section: Introductionmentioning

confidence: 99%

Groundwater Potential Mapping Using Remote Sensing and GIS-Based Machine Learning Techniques

et al. 2020

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Self-Learning Random Forests Model for Mapping Groundwater Yield in Data-Scarce Areas

2018

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Integrated approach for groundwater potential exploration in Abbay River Basin, East Africa

Yoshe

2024

Sustain. Water Resour. Manag.

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This paper presents and examines groundwater potential zones with the help of remote sensing and GIS methods for controlling and investigating the geospatial data of each parameter. Groundwater is a very important source for water supply and others, considering its availability, quality, cost, and time-effectiveness to develop. It is virtually everywhere and yet variable in quantity. Because of several conditions, such as rapid population growth, urbanization, industrialization, and agricultural development, groundwater sources are under severe threat. Climate change plays an important role in the quality and quantity of groundwater potential. In addition, climate change severely affects parameters that influence groundwater recharge. Unreliable exploitation and poor quality of surface water resources tend to increase the decline in groundwater levels. Hence, it is necessary to identify groundwater potential zones that can be used to optimize and monitor groundwater resources. This study was conducted in the Abbay River Basin and identifies the location of groundwater potential for developing new supplies that could be used for a range of purposes in the study area, where groundwater serves as the main source for agricultural purposes rather than surface water. Seven selected parameters—lineament density, precipitation, geology, drainage density, land use, slope, and soil data—were collected, processed, resampled, projected, and reclassified for hydrological analysis. For the generation of groundwater zones, weightage was calculated using an analytical hierarchy method, reclassified, ranked, and overlaid with GIS. The obtained results of weightage were lineament density (37%), precipitation (30%), geology (14%), drainage density (7%), land use land cover (5%), slope (4%), and soil (3%). The consistency ratio estimated for this study was 0.089, which was acceptable for further analysis. Based on the integration of all thematic layers and the generated groundwater potential zones, the map was reclassified into five different classes, namely very good, good, moderate, poor, and very poor. The results of this study reveal that 1295.33 km2 of the study area can be considered very poor, 58,913.1 km2 is poor, 131,323 km2 is moderate, 18,557 km2 is good, and 311.5 km2 is very good. Any groundwater management project performed in the better regions would offer the greatest value. A similar study would be valuable before planning any water resource development activity, as this would save the expense of comprehensive field investigations. This study also demonstrates the importance of remote sensing and GIS techniques in mapping groundwater potential at the basin scale and suggests that similar methods could be applied across other river basins.

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Application of Mathematical Models in Defining Optimal Groundwater Yield

Cited by 4 publications

References 1 publication

Groundwater Potential Mapping Using Remote Sensing and GIS-Based Machine Learning Techniques

Groundwater Potential Mapping Using Remote Sensing and GIS-Based Machine Learning Techniques

Self-Learning Random Forests Model for Mapping Groundwater Yield in Data-Scarce Areas

Integrated approach for groundwater potential exploration in Abbay River Basin, East Africa

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