Groundwater Stability Assessment with Geospatial Modeling Using GIS: A Case Study of Illizi Town, Algeria

Kouadri, Saber; Kateb, Samir

doi:10.1007/978-3-030-76081-6_6

Cited by 1 publication

(1 citation statement)

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“…An ANN model's architectural framework is made up of three fundamental layers: input, hidden, and output. Using complex mathematical functions and knowledge of the different underlying patterns associated with a dataset, the multiple layers can join to process and create useful responses as a function of the incoming data (Kouadri et al 2021;Omeka et al 2023). Errors may occur during a normal data input process; however, ANNs may mitigate for this flaw due to their high accuracy in quantitative analysis.…”

Section: Modeling Of Artificial Neural Networkmentioning

confidence: 99%

Application of GIS and feedforward back-propagated ANN models for predicting the ecological and health risk of potentially toxic elements in soils in Northwestern Nigeria

Omang

Omeka

Asinya

et al. 2023

Preprint

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Potentially toxic elements occur naturally in most geologic materials. However, recent anthropogenic disturbances such as ore mining have contributed significantly to their enrichment in soils. Their occurrence in soil may portend a myriad of related risks to the environment and biota. Most traditional soil quality evaluation methods involve comparing the background values of the elements to the established guideline values, which is often time-consuming and fraught with computational mistakes. As a result, in order to conduct a comprehensive and unbiased evaluation of soil quality and its effects on the ecosystem and human health, this research combined geochemical, numerical, and GIS data for a composite health risk zonation of the full study region. Furthermore, the multilayer perceptron artificial neural network was used to forecast the most important toxic components influencing soil quality (MLP-NN). Geochemical, statistical, and quantitative soil pollution evaluation (pollution index and ecological risk index) showed that apart from mining, the spread and association of trace elements and oxides occur as a consequence of surface environmental conditions (e.g., leaching, weathering, and organo-metallic complexation). The hazard quotients (HQs) and hazard index (HI) of all PTEs were greater than one. This indicates that residents (particularly children) are more susceptible to risks from toxic components ingestion than skin exposure and inhalation. Ingestion of As and Cr resulted in higher cancer risks and lifetime cancer risk levels (> 1.0E 04), with risk levels increasing towards the northeastern, western, and southeastern directions of the study area. The low modeling errors observed from the sum of square errors, relative errors, and coefficient of determination confirmed the efficiency of the MLP-NN in pollution load prediction. Based on the sensitivity analysis, Hg, Sr, Zn, Ba, As, and Zr showed the greatest influence on soil quality. Focus on remediation should therefore be placed on the removal of these elements from the soil

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Section: Modeling Of Artificial Neural Networkmentioning

confidence: 99%