Spatial Prediction of Groundwater Withdrawal Potential Using Shallow, Hybrid, and Deep Learning Algorithms in the Toudgha Oasis, Southeast Morocco

Abstract

Water availability is a key factor in territorial sustainable development. Moreover, groundwater constitutes the survival element of human life and ecosystems in arid oasis areas. Therefore, groundwater potential (GWP) identification represents a crucial step for its management and sustainable development. This study aimed to map the GWP using ten algorithms, i.e., shallow models comprising: multilayer perceptron, k-nearest neighbor, decision tree, and support vector machine algorithms; hybrid models comprising: voting, random forest, adaptive boosting, gradient boosting (GraB), and extreme gradient boosting; and the deep learning neural network. The GWP inventory map was prepared using 884 binary data, with “1” indicating a high GWP and “0” indicating an extremely low GWP. Twenty-three GWP-influencing factors have been classified into numerical data using the frequency ration method. Afterwards, they were selected based on their importance and multi-collinearity tests. The predicted GWP maps show that, on average, only 11% of the total area was predicted as a very high GWP zone and 17% and 51% were estimated as low and very low GWP zones, respectively. The performance analyses demonstrate that the applied algorithms have satisfied the validation standards for both training and validation tests with an average area under curve of 0.89 for the receiver operating characteristic. Furthermore, the models’ prioritization has selected the GraB model as the outperforming algorithm for GWP mapping. This study provides decision support tools for sustainable development in an oasis area.

Investigating machine learning and ensemble learning models in groundwater potential mapping in arid region: case study from Tan-Tan water-scarce region, Morocco

“…RF-LR-DT-ANN ensemble model demonstrated stable and suitable prediction results in mountainous karstic region (High Atlas Mountains of Beni Mellal) (Namous et al, 2021). Additionally, other studies in Toudgha arid oasis at southeast Morocco reveal the relative effectiveness of boosted models e.g., Gradient Boosting and the bagged RF to model groundwater withdrawal (Ouali et al, 2023).…”

“…In Morocco, a few studies of GWPM were undertaken using machine learning and ensemble learning algorithms (Namous et al, 2021;Ouali et al, 2023). RF-LR-DT-ANN ensemble model demonstrated stable and suitable prediction results in mountainous karstic region (High Atlas Mountains of Beni Mellal) (Namous et al, 2021).…”

Groundwater Level Trend Analysis and Prediction in the Upper Crocodile (West) Basin, South Africa

“…GB algorithm was successfully used in Iran for predicting monthly GWL (Sharafati et al, 2020a), In Slovenia, GB outperformed linear regression, random forest, decision tree algorithms for predicting GWL. Again in a Moroccan study where ten ML where compared in predicting groundwater withdrawals , GB outperformed all the algorithms (Ouali et al, 2023). In a South African study, GB also outperformed five ML algorithms among which ANNs and Support Vector Regresion (SVR), the dominantly used ML were included.…”

Groundwater Level Trend Analysis and Prediction in the Upper Crocodile Sub-Basin, South Africa

“…In Slovenia, GB outperformed linear regression, random forest and decision tree algorithms for predicting GWLs. Again, in a Moroccan study, where ten ML models were compared in predicting groundwater withdrawals, GB outperformed all the algorithms [41]. In a South African study, GB also outperformed five ML algorithms, among which ANNs and Support Vector Regression (SVR), the dominantly used ML algorithms, were included [42].…”