Convolutional neural network and long short-term memory algorithms for groundwater potential mapping in Anseong, South Korea

Hakim, Wahyu Luqmanul; Nur, Arip Syaripudin; Rezaie, Fatemeh; Panahi, Mahdi; Lee, Chang-Wook; Lee, Saro

doi:10.1016/j.ejrh.2022.100990

Cited by 27 publications

(24 citation statements)

References 73 publications

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“…Recently, numerous novel methods and algorithms related to artificial intelligence (AI) based on machine learning (ML) and deep learning (DL) have been developed, assessed, and approved in the field of GWP mapping determination; this has been conducted with respect to inventories of water withdrawal points and geological, hydrogeological, hydrological, topographic and climatic factors [11,17,[19][20][21]. On this matter, the following models were commonly used and applied in the sub-cited studies: random forest (RF), support vector machine (SVM), linear regression (LR), decision tree (DT), naive Bayes (NB), convolutional neural network (CNN), long short-term memory (LSTM) and artificial neural network (ANN) [6,9,14,[22][23][24][25]. Furthermore, a variety of methods have also been proposed to improve the efficiency and precision of the prediction models, such as optimization algorithms and ensemble models [23,26,27].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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“…Determination of groundwater potential sites and various hydrological, hydrogeological, geological, topographic, and climatic factors is a preliminary step before applying any modeling approach. Within the aforementioned studies, several commonly employed models have been utilized, these models include decision tree (DT) (Naghibi et al, 2019), random forest (RF) (Rahmati et al, 2016), support vector machine (SVM) (Anh et al, 2023), naive bayes (NB) (Pham et al, 2021), AdaBoost (AB) (Naghibi et al, 2017), long short-term memory (LSTM) (Hakim et al, 2022), convolutional neural network (CNN) (Hakim et al, 2022), and artificial neural network (ANN) (Tamiru and Wagari, 2022). Additionally, researchers have proposed diverse methodologies to enhance the efficiency and the accuracy of prediction models, including ensemble models and optimization models.…”

Section: Introductionmentioning

confidence: 99%

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

Jari,

Bachaoui,

Hajaj

et al. 2023

Front. Water

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Groundwater resource management in arid regions has a critical importance for sustaining human activities and ecological systems. Accurate mapping of groundwater potential plays a vital role in effective water resource planning. This study investigates the effectiveness of machine learning models, including Random Forest (RF), Adaboost, K-Nearest Neighbors (KNN), and Gaussian Process in groundwater potential mapping (GWPM) in the Tan-Tan arid region, Morocco. Fourteen groundwater conditional factors were considered following multicollinearity test, including topographical, hydrological, climatic, and geological factors. Additionally, point data with 174 sites indicative of groundwater occurrences were incorporated. The groundwater inventory data underwent random partitioning into training and testing datasets at three different ratios: 55/45%, 65/35%, and 75/25%. Ultimately, a comprehensive ranking of the 13 models, encompassing both individual and ensemble models, was determined using the prioritization rank technique. The results revealed that ensemble learning (EL) models, particularly RF and Adaboost (RF-Adaboost), outperformed individual models in groundwater potential mapping. Based on accuracy assessment using the validation dataset, the RF-Adaboost EL results yielded an Area Under the Receiver Operating characteristic Curve (AUROC) and Overall Accuracy (OA) of 94.02 and 94%, respectively. Ensemble models have been effectively applied to integrate 14 factors, capturing their intricate interrelationships, and thereby enhancing the accuracy and robustness of groundwater prediction in the Tan-Tan water-scarce region. Among the natural factors, the current study identified lithology, structural elements (such as faults and tectonic lineaments), and land use as significant contributors to groundwater potential. However, the critical characteristics of the study area showing a coastal position as well as a low background in groundwater prospectivity (low borehole points) are challenging in GWPM. The findings highlight the importance of the significant factors in assessing and managing groundwater resources in arid regions. Moreover, this study makes a contribution to the management of groundwater resources by demonstrating the effectiveness of ensemble learning algorithms in the groundwater potential mapping (GWPM) in arid regions.

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“…It is crucial but di cult to identify groundwater potentiality (GWP) zones and determine groundwater availability when numerous deciding factors are present (Kalantar et al, 2019;Hakim et al, 2022). The GWP has been modeled using physical, heuristic, and mathematical techniques (Victor et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

A national-level study on groundwater potentiality mapping using a hybrid machine learning models under the scenario of climate change

Sarkar

Alshehri

Shahfahad

et al. 2022

Preprint

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A severe threat to natural resources and human livelihood is groundwater scarcity. Therefore, mapping groundwater potentiality (GWP) is necessary for future resource management. In this article, a framework for conducting ensemble modeling is introduced. This framework will be used to map GWP at the national level under the climate change scenario. Thirteen elements linked to topography, geology, hydrology, and land cover, as well as six climatic indicators based on historical time series data, were used to map the GWP. To provide extremely reliable groundwater potentiality mapping, three traditional standalone machine learning techniques such as logistic model tree (LMT), logistic regression (LR), and artificial neural network (ANN) have been merged with a stacking ensemble framework. Using the empirical and binormal receiver operating characteristic curves, the GWP mapping has been validated (ROC curve). According to research, Bangladesh's major rivers run along the high GWP zones in the country's southern and central regions. Additionally, the validation using the ROC curve demonstrates that the stacking model which had all three MLAs—performed better than other models (AUC: 0.971). The study may have a substantial impact on Bangladesh's national water planning and policy, which will be made using evidence. Additionally, the suggested method might be applied to map GWP on a broader scale in additional nations as well as at the continental level.

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Convolutional neural network and long short-term memory algorithms for groundwater potential mapping in Anseong, South Korea

Cited by 27 publications

References 73 publications

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

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

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

A national-level study on groundwater potentiality mapping using a hybrid machine learning models under the scenario of climate change

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