The use of hybrid machine learning models for improving the GALDIT model for coastal aquifer vulnerability mapping

Bordbar, Mojgan; Khosravi, Khabat; Murgulet, Dorina; Tsai, Frank T.-C.; Golkarian, Ali

doi:10.1007/s12665-022-10534-2

Cited by 12 publications

(1 citation statement)

References 66 publications

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“…The replenishment of groundwater, which potentially leads to an increase in groundwater levels within coastal aquifers, can occur through mechanisms like precipitation-induced infiltration. In this study, the chosen indicators for assessing sea water intrusion susceptibility encompassed topography, type of Quaternary sedimentary rock, groundwater level, and precipitation [90][91][92][93].…”

Section: Assessment Indicatorsmentioning

confidence: 99%

Geo-Environment Vulnerability Assessment of Multiple Geohazards Using VWT-AHP: A Case Study of the Pearl River Delta, China

Huang,

Wu,

et al. 2023

Remote Sensing

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Geohazards pose significant risks to communities and infrastructure, emphasizing the need for accurate susceptibility assessments to guide land-use planning and hazard management. This study presents a comprehensive method that combines Variable Weight Theory (VWT) with Analytic Hierarchy Process (AHP) to assess geo-environment vulnerability based on susceptibility to various geohazards. The method was applied to the Pearl River Delta in China, resulting in the classification of areas into high vulnerability (5961.85 km2), medium vulnerability (19,227.93 km2), low vulnerability (14,892.02 km2), and stable areas (1616.19 km2). The findings demonstrate improved accuracy and reliability compared to using AHP alone. ROC curve analysis confirms the enhanced performance of the integrated method, highlighting its effectiveness in discerning susceptibility levels and making informed decisions in hazard preparedness and risk reduction. Additionally, this study assessed the risks posed by geohazards to critical infrastructures, roads, and artificial surfaces, while discussing prevention strategies. However, this study acknowledges certain limitations, including the subjective determination of its judgment matrix and data constraints. Future research could explore the integration of alternative methods to enhance the objectivity of factor weighting. In practical applications, this study contributes to the understanding of geo-environment vulnerability assessments, providing insight into the intricate interplay among geological processes, human activities, and disaster resilience.

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Section: Assessment Indicatorsmentioning

confidence: 99%

Geo-Environment Vulnerability Assessment of Multiple Geohazards Using VWT-AHP: A Case Study of the Pearl River Delta, China

Huang,

Wu,

et al. 2023

Remote Sensing

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Assessment of alluvial aquifer intrinsic vulnerability by a generic DRASTIC model: a discussion on data adequacy and pragmatic results

Taheri,

Missimer,

Maleki

et al. 2023

Environ Dev Sustain

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DRASTIC is a model that is commonly used to assess vulnerability to groundwater contamination at the landscape scale. When sparse data are available to populate the layers of the model, it can be difficult to ascertain the true usefulness of the model produced map. In this research an alluvial aquifer, the Sahneh aquifer in Kermanshah province of western Iran, was mapped using the generic DRASTIC model. The data available for populating the model layers were generally sparse. The model was validated using a nitrate concentration map constructed from well water measurements within the DRASTIC map area. A Receiver Operating Curve (ROC) analysis was conducted by placing 500 random points in the DRASTIC generated map compared to the nitrate concentration map. The area under the curve was compared and yielded a value of 0.72 or 72% concordance, which means it has good validity. This investigation demonstrates that a generic DRASTIC model can yield acceptable results without modification or increasing its complexity. If the ROC analysis had yielded a value <50%, then the DRASTIC would have been considered to not be useful. A common mistake in the use of DRASTIC is to modify the method to greatly increase its complexity, which may actually decrease, not increase the resultant model usefulness.

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Comparative study for coastal aquifer vulnerability assessment using deep learning and metaheuristic algorithms

Bordbar,

Heggy,

Jun

et al. 2024

Environ Sci Pollut Res

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The use of hybrid machine learning models for improving the GALDIT model for coastal aquifer vulnerability mapping

Cited by 12 publications

References 66 publications

Geo-Environment Vulnerability Assessment of Multiple Geohazards Using VWT-AHP: A Case Study of the Pearl River Delta, China

Geo-Environment Vulnerability Assessment of Multiple Geohazards Using VWT-AHP: A Case Study of the Pearl River Delta, China

Assessment of alluvial aquifer intrinsic vulnerability by a generic DRASTIC model: a discussion on data adequacy and pragmatic results

Comparative study for coastal aquifer vulnerability assessment using deep learning and metaheuristic algorithms

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