Application of Machine Learning Techniques In Forecasting Groundwater Levels in the Grootfontein Aquifer

Kanyama, Yolanda; Ajoodha, Ritesh; Seyler, Helen; Makondo, Ndivhuwo; Tutu, Hlanganani

doi:10.1109/imitec50163.2020.9334142

Cited by 13 publications

(16 citation statements)

References 26 publications

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“…Due to their potential for being less time-consuming and their capacity to produce relevant findings, machine learning models are interestingly becoming alternatives to processbased models (Kanyama et al, 2020). A large volume of literature is available on the applicability of machine learning algorithms to forecast groundwater level (GWL) in different regions of the world (Tao et al, 2022).…”

Section: Machine Learning Case Studies Of Groundwater Level Predictio...mentioning

confidence: 99%

“…The root node, or starting node, and the leaf node, or end node, make up the DT. The DT can calculate division values by exploiting impurities produced at each node (Kanyama et al, 2020). Deep Belief Networks (DBN) is a type of deep learning algorithm that consists of multiple layers of hidden units (Kalu et al, 2022).…”

Section: Regressionmentioning

confidence: 99%

“…A variant of SVM is Support Vector Regression (SVR) also used for regression tasks. It predicts the value of a continuous variable by finding an optimal hyperplane that maximizes the margin while allowing a certain amount of error (Kanyama et al, 2020). Logistic Regression (LR) is a statistical regression algorithm commonly used for binary classification tasks.…”

Section: Regressionmentioning

confidence: 99%

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A Review of Geological and Climatic Variables in Groundwater Availability Prediction in Africa: Machine Learning Approaches

Touré,

Boateng,

Gidigasu

et al. 2024

Preprint

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Groundwater is crucial for Africa's potable water supply, agriculture, and economic development. However, the continent faces challenges with groundwater scarcity due to factors like population growth, climate change, and over exploitation. Over the past ten years, machine learning has been increasingly and successfully used in groundwater level prediction across the world. This review paper explores the application of machine learning techniques in predicting groundwater levels in Africa. The methodology involved downloading relevant papers, identifying and categorizing the machine learning algorithms employed, and quantifying their use. Geological and climatic variables were also identified, analyzed and categorized to measure their usage frequency. The different algorithms and input variables extracted from each paper are graphically represented in this document highlighting the most employed ones. The findings suggest that the available literature on this topic in Africa is limited compared to the rest of the world. Tree-based algorithms are commonly used in machine learning in Africa, and the most employed input variables are related to geomorphology and temperature. The study highlights the potential of machine learning in improving water resource management and decision-making in the region.

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Section: Machine Learning Case Studies Of Groundwater Level Predictio...mentioning

confidence: 99%

Section: Regressionmentioning

confidence: 99%

Section: Regressionmentioning

confidence: 99%

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A Review of Geological and Climatic Variables in Groundwater Availability Prediction in Africa: Machine Learning Approaches

Touré,

Boateng,

Gidigasu

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…The use of machine learning techniques within SADC for groundwater modelling, has seen a slow adoption [12,13]. Several studies, such as [14,[31][32][33], have explored machine learning for groundwater modelling in Southern Africa. Kombo [33] reported good skill using a hybrid K-Nearest Neighbour-Random Forest (KNN-RF) to predict groundwater level fluctuations in fractured rock aquifers in Kenya, while [31,32] demonstrated reasonable skill in predicting groundwater levels in karts aquifers in South Africa.…”

Section: Introductionmentioning

confidence: 99%

“…Several studies, such as [14,[31][32][33], have explored machine learning for groundwater modelling in Southern Africa. Kombo [33] reported good skill using a hybrid K-Nearest Neighbour-Random Forest (KNN-RF) to predict groundwater level fluctuations in fractured rock aquifers in Kenya, while [31,32] demonstrated reasonable skill in predicting groundwater levels in karts aquifers in South Africa. Nonetheless, additional testing is needed to fully understand the applicability of these models in groundwater modelling applications, and especially within the context of data-scarce aquifers [13].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Ensemble and Deep Learning Algorithms to Model Groundwater Levels in a Data-Scarce Aquifer of Southern Africa

et al. 2022

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Machine learning and deep learning have demonstrated usefulness in modelling various groundwater phenomena. However, these techniques require large amounts of data to develop reliable models. In the Southern African Development Community, groundwater datasets are generally poorly developed. Hence, the question arises as to whether machine learning can be a reliable tool to support groundwater management in the data-scarce environments of Southern Africa. This study tests two machine learning algorithms, a gradient-boosted decision tree (GBDT) and a long short-term memory neural network (LSTM-NN), to model groundwater level (GWL) changes in the Shire Valley Alluvial Aquifer. Using data from two boreholes, Ngabu (sample size = 96) and Nsanje (sample size = 45), we model two predictive scenarios: (I) predicting the change in the current month’s groundwater level, and (II) predicting the change in the following month’s groundwater level. For the Ngabu borehole, GBDT achieved R2 scores of 0.19 and 0.14, while LSTM achieved R2 scores of 0.30 and 0.30, in experiments I and II, respectively. For the Nsanje borehole, GBDT achieved R2 of −0.04 and −0.21, while LSTM achieved R2 scores of 0.03 and −0.15, in experiments I and II, respectively. The results illustrate that LSTM performs better than the GBDT model, especially regarding slightly greater time series and extreme GWL changes. However, closer inspection reveals that where datasets are relatively small (e.g., Nsanje), the GBDT model may be more efficient, considering the cost required to tune, train, and test the LSTM model. Assessing the full spectrum of results, we concluded that these small sample sizes might not be sufficient to develop generalised and reliable machine learning models.

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Application of machine learning and deep learning for predicting groundwater levels in the West Coast Aquifer System, South Africa

Igwebuike,

Ajayi,

Okolie

et al. 2024

Earth Sci Inform

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Groundwater models are valuable tools to quantify the response of groundwater level to hydrological stresses induced by climate variability and groundwater extraction. These models strive for sustainable groundwater management by balancing recharge, discharge, and natural processes, with groundwater level serving as a critical response variable. While traditional numerical models are labour-intensive, machine learning and deep learning offer a data-driven alternative, learning from historical data to predict groundwater level variations. The groundwater level in wells is typically recorded as continuous groundwater level time series data and is essential for implementing managed aquifer recharge within a particular region. Machine learning and deep learning are essential tools to generate a data-driven approach to modeling an area, and there is a need to understand if they are the most suitable tools to improve model prediction. To address this objective, the study evaluates two machine learning algorithms - Random Forest (RF) and Support Vector Machine (SVM); and two deep learning algorithms - Simple Recurrent Neural Network (SimpleRNN) and Long Short-Term Memory (LSTM) for modeling groundwater level changes in the West Coast Aquifer System of South Africa. Analysis of regression error metrics on the test dataset revealed that SVM outperformed the other models in terms of the root mean square error, whereas random forest had the best performance in terms of the MAE. In the accuracy analysis of predicted groundwater levels, SVM achieved the highest accuracy with an MAE of 0.356 m and an RMSE of 0.372 m. The study concludes that machine learning and deep learning are effective tools for improved modeling and prediction of groundwater level. Further research can incorporate more detailed geologic information of the study area for enhanced interpretation.

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Application of Machine Learning Techniques In Forecasting Groundwater Levels in the Grootfontein Aquifer

Cited by 13 publications

References 26 publications

A Review of Geological and Climatic Variables in Groundwater Availability Prediction in Africa: Machine Learning Approaches

A Review of Geological and Climatic Variables in Groundwater Availability Prediction in Africa: Machine Learning Approaches

A Comparison of Ensemble and Deep Learning Algorithms to Model Groundwater Levels in a Data-Scarce Aquifer of Southern Africa

Application of machine learning and deep learning for predicting groundwater levels in the West Coast Aquifer System, South Africa

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