Predicting Groundwater Level Using Climate Change Scenarios in the Southern Part of Mali

Diancoumba, Oumou; Toure, Adama; Keita, Souleymane; Konare, Sériba; Mounir, Zakari Mahamadou; Bokar, Hamadoun

doi:10.4236/ajcc.2023.121002

Cited by 2 publications

(1 citation statement)

References 29 publications

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“…[21] and Sun et al [49], climatic circumstances impact GWL changes, which is consistent with the spearman correlation data. Furthermore, it should be noted that varied climatic circumstances impact GWL changes differently [50], as seen in Figure 5, where each of the meteorological factors acts differently.…”

Section: Spearman Correlation Heatmap Analysis Of the Datasetmentioning

confidence: 99%

Climate change impact assessment on groundwater level changes: A study of hybrid model techniques

Afrifa

Zhang

Xin

et al. 2023

IET Signal Processing

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One of the most important sources of water supply is groundwater. However, the groundwater level (GWL) is significantly impacted by the global climate change. Therefore, under these more severe climate change conditions, the accurate and simple forecast of farmland GWL is a crucial component of agricultural water management. A hybrid model (HM) of Bayesian random forest (BRF), Bayesian support vector machine (BSVM), and Bayesian artificial neural network (BANN) is built in this study. The HM is made up of a Bayesian model averaging (BMA) and three machine learning models: random forest (RF), support vector machine (SVM), and artificial neural network. These three HMs are employed to help automate logical inference and decision‐making in business intelligence for groundwater management. For this purpose, data on 8 separate climatic factors that impact GWL changes in the study area were acquired. Nine distinct farming communities' GWL change data were utilised as the dependent variables for each model fit (community data). The effectiveness of the HM techniques was assessed using the evaluation metrics of mean absolute error (MAE), coefficient of determination (R2), mean absolute percent error (MAPE), and root mean square error (RMSE). The model fit in Suhum had the greatest performance with the highest accuracy (R2 varied from 0.9051 to 0.9679) and the lowest error scores (RMSE ranged from 0.0653 to 0.0727, and MAE ranged from 0.0121 to 0.0541), according to the models' evaluation results. The BRF delivered the greatest results when compared to the two independent HMs, the BSVM and BANN. Future GWL and climatic variable data may be trained using the trained HM techniques to determine the effects of climate change. Farmers, businesses, and civil society organisations might benefit from continuous monitoring of GWL data and education on climate change to help control and prevent excessive deteriorations of global climate change on GWL.

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Section: Spearman Correlation Heatmap Analysis Of the Datasetmentioning

confidence: 99%