Machine learning and GIS-RS-based algorithms for mapping the groundwater potentiality in the Bundelkhand region, India

Kumar, Mukesh; Singh, Pitam; Singh, Pratibha

doi:10.1016/j.ecoinf.2023.101980

Cited by 28 publications

(8 citation statements)

References 68 publications

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“…The water crisis throughout semi-arid and dry areas, like North Africa, has been worsened by the increasingly severe effects of climate change. The problem has been aggravated by inadequate management plans and strategies, as well as a lack of knowledge about water resources [Kumar et al, 2023]. This research tried to enhance the current knowledge on groundwater potential in the UOER basin, which could help improve water resource management.…”

Section: Discussionmentioning

confidence: 99%

“…Sources of data used in the study ArcGIS before establishing the lineament density layer (km/km 2 ) in the same way as the DD map, employing the Line Density Tool.Curvature was created by calculating the second derivative of the DEM surface in ArcGIS[Kumar et al, 2023;Moore et al, 1991]. Positives values of curvature mean a convex land surface, while negative values mean a convex land surface…”

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confidence: 99%

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Groundwater Potential Assessment in the Upper Oum Er-Rbia Basin, Northern Morocco

Midaoui,

El-Hamdouny,

Elaloui

et al. 2024

Ecol. Eng. Environ. Technol.

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This study aimed to assess the groundwater potential zones (GWPZ) in northern Morocco's Upper Oum Er-Rbia Basin (UOER). In such a semi-arid context, groundwater resources are crucial to sustaining essential human activities, but they are under stress due to increased overuse and climate change. This investigation utilized remote sensing in a GIS framework along with a multi-criteria decision analysis (MCDA) technique using the analytic hierarchy process (AHP) for the first time in this region. Ten thematic layers were created, representing the most significant parameters, which were then weighted and overlaid. The output map shows five levels of potential: very low, low, medium, high, and very high, covering 12%, 19%, 20%, 27%, and 22% of the basin area, respectively. Comparing the assessment results to the borehole yield, the AUC-ROC curve showed a value of 84.5%, which testifies to the excellent performance of the methodology used. Of the 10 criteria used, lithology was shown to be the most significant factor, followed by LULC, slope, and geomorphology. The study results offer an extensive insight into the hydrogeological potential of the UOER basin. These findings are essential for decision-makers and encourage the efficient utilization of groundwater resources, thus supporting broader objectives of sustainable development.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

Groundwater Potential Assessment in the Upper Oum Er-Rbia Basin, Northern Morocco

Midaoui,

El-Hamdouny,

Elaloui

et al. 2024

Ecol. Eng. Environ. Technol.

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“…By continuously adapting to technological advancements and environmental changes, these spectral bands enhance the understanding of the earth's surface and empower policymakers and conservationists to make informed decisions aimed at long-term sustainability. Each application, whether focused on agriculture, water bodies, urban features, or geothermal monitoring, contributes to a holistic approach to environmental stewardship, underlining the critical integration of advanced remote sensing technology with strategic land management practices [25,79].…”

Section: Figure 7 Feature Importance Of Rf Modelmentioning

confidence: 99%

“…Machine Learning (ML) methods such as Support Vector Machine (SVM) and Random Forest (RF) algorithms have been adopted to develop models efficiently in geothermal land use [24,25]. Xie et al (2021) employed a Support Vector Machine (SVM) in their study to assess and monitor land use alterations on the Crozon Peninsula in Brittany, France.…”

Section: Introductionmentioning

confidence: 99%

Towards Improving Sustainable Water Management in Geothermal Fields: SVM and RF Land Use Monitoring

Utama,

Indriani,

Hermana

et al. 2024

J. Hum. Earth Future

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The management and monitoring of land use in geothermal fields are crucial for the sustainable utilization of water resources, as well as for striking a balance between the production of renewable energy and the preservation of the environment. This study primarily compared Support Vector Machine (SVM) and Random Forest (RF) machine learning methods, using satellite imagery from Landsat 8 and Sentinel 2 between 2021 and 2023, to monitor land use in the Patuha geothermal area. The objective is to improve sustainable water management practices by accurately categorizing different land cover types. This comparative analysis assessed the efficacy of these techniques in upholding water sustainability in geothermal regions. This study examined the application of SVM and RF machine learning techniques, with particular emphasis on parameter refinement and model assessment, to enhance land use classification accuracy. By employing Kernlab and e1071 for algorithm comparison, the research sought to produce a precise Land Use Model Map, which underscores the significance of advanced analytical techniques in environmental management. This approach was of utmost importance in improving land use monitoring and reinforcing sustainable practices. The comparative evaluation of SVM and RF methods for land use classification demonstrates the superiority of RF in terms of accuracy, stability, and precision, particularly in intricate urban settings, hence establishing it as the preferred model for tasks demanding high reliability. The application of SVM and RF for monitoring land use in geothermal areas is in alignment with Sustainable Development Goals (SDGs) 6 and 15, as it fosters sustainable water management and the conservation of ecosystems. Doi: 10.28991/HEF-2024-05-02-06 Full Text: PDF

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“…Remote sensing images (RSI) have numerous applications in surveillance and intelligence decision-making systems such as agriculture, urban planning, rescue missions, and transportation systems. Research work has followed suit and demonstrated what automated analytics can uncover for the geographic mapping of resources [1], crop harvest analysis [2], emergency rescue [3], and terrestrial and naval traffic monitoring [4]. Automating aerial analytics requires localization and identification of objects in the frame.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Domain Adaptation With Debiased Contrastive Learning and Support-Set Guided Pseudolabeling for Remote Sensing Images

Biswas,

Tešić

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The variability in different altitudes, geographical variances, and weather conditions across datasets degrade stateof-the-art (SOTA) DNN object detection performance. Unsupervised and Semi-supervised domain adaptation (DA) have been decent solutions to bridge the gap between two different distributions of datasets. The state-of-the-art pseudo-labeling process is susceptible to background noise, hindering the optimal performance in target datasets. The existing contrastive DA methods overlook the bias effect introduced from the false negative (FN) target samples, which misleads the complete learning process. This paper proposes DCLDA (support-guided debiased contrastive learning for domain adaptation) to properly label the unlabeled target dataset and remove the bias toward target detection. We introduce (i) A support-set curated approach to generate high-quality pseudo-labels from the target dataset proposals, (ii) a reduced distribution gap across different datasets using domain alignment on local, global, and instance-aware features for remote sensing datasets, and (iii) novel debiased contrastive loss function, that makes the model more robust for the variable appearance of a particular class over images and domains. The proposed debiased contrastive learning pivots on class probabilities to address the challenge of false negatives in the unsupervised framework. Our model outperforms the compared SOTA models with a minimum gain of +3.9%, +3.2%, +12.7%, and +2.1% of mAP for DIOR, DOTA, Visdrone, and UAVDT datasets, respectively.

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Machine learning and GIS-RS-based algorithms for mapping the groundwater potentiality in the Bundelkhand region, India

Cited by 28 publications

References 68 publications

Groundwater Potential Assessment in the Upper Oum Er-Rbia Basin, Northern Morocco

Groundwater Potential Assessment in the Upper Oum Er-Rbia Basin, Northern Morocco

Towards Improving Sustainable Water Management in Geothermal Fields: SVM and RF Land Use Monitoring

Unsupervised Domain Adaptation With Debiased Contrastive Learning and Support-Set Guided Pseudolabeling for Remote Sensing Images

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