A Comprehensive Review of Conventional, Machine Leaning, and Deep Learning Models for Groundwater Level (GWL) Forecasting
Groundwater level (GWL) refers to the depth of the water table or the level of water below the Earth’s surface in underground formations. It is an important factor in managing and sustaining the groundwater resources that are used for drinking water, irrigation, and other purposes. Groundwater level...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/4/2743 |
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| author | Junaid Khan Eunkyu Lee Awatef Salem Balobaid Kyungsup Kim |
| author_facet | Junaid Khan Eunkyu Lee Awatef Salem Balobaid Kyungsup Kim |
| author_sort | Junaid Khan |
| collection | DOAJ |
| description | Groundwater level (GWL) refers to the depth of the water table or the level of water below the Earth’s surface in underground formations. It is an important factor in managing and sustaining the groundwater resources that are used for drinking water, irrigation, and other purposes. Groundwater level prediction is a critical aspect of water resource management and requires accurate and efficient modelling techniques. This study reviews the most commonly used conventional numerical, machine learning, and deep learning models for predicting GWL. Significant advancements have been made in terms of prediction efficiency over the last two decades. However, while researchers have primarily focused on predicting monthly, weekly, daily, and hourly GWL, water managers and strategists require multi-year GWL simulations to take effective steps towards ensuring the sustainable supply of groundwater. In this paper, we consider a collection of state-of-the-art theories to develop and design a novel methodology and improve modelling efficiency in this field of evaluation. We examined 109 research articles published from 2008 to 2022 that investigated different modelling techniques. Finally, we concluded that machine learning and deep learning approaches are efficient for modelling GWL. Moreover, we provide possible future research directions and recommendations to enhance the accuracy of GWL prediction models and improve relevant understanding. |
| first_indexed | 2024-03-11T09:10:58Z |
| format | Article |
| id | doaj.art-5cc40b4ef223490383da7540737dd461 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T09:10:58Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-5cc40b4ef223490383da7540737dd4612023-11-16T19:00:08ZengMDPI AGApplied Sciences2076-34172023-02-01134274310.3390/app13042743A Comprehensive Review of Conventional, Machine Leaning, and Deep Learning Models for Groundwater Level (GWL) ForecastingJunaid Khan0Eunkyu Lee1Awatef Salem Balobaid2Kyungsup Kim3Department of Environmental & IT Engineering, Chungnam National University, Daejeon 34134, Republic of KoreaDepartment of Computer Engineering, Chungnam National University, Daejeon 34134, Republic of KoreaDepartment of Computer Science, College of Computer Science and Information Technology, Jazan University, Jazan 45142, Saudi ArabiaDepartment of Environmental & IT Engineering, Chungnam National University, Daejeon 34134, Republic of KoreaGroundwater level (GWL) refers to the depth of the water table or the level of water below the Earth’s surface in underground formations. It is an important factor in managing and sustaining the groundwater resources that are used for drinking water, irrigation, and other purposes. Groundwater level prediction is a critical aspect of water resource management and requires accurate and efficient modelling techniques. This study reviews the most commonly used conventional numerical, machine learning, and deep learning models for predicting GWL. Significant advancements have been made in terms of prediction efficiency over the last two decades. However, while researchers have primarily focused on predicting monthly, weekly, daily, and hourly GWL, water managers and strategists require multi-year GWL simulations to take effective steps towards ensuring the sustainable supply of groundwater. In this paper, we consider a collection of state-of-the-art theories to develop and design a novel methodology and improve modelling efficiency in this field of evaluation. We examined 109 research articles published from 2008 to 2022 that investigated different modelling techniques. Finally, we concluded that machine learning and deep learning approaches are efficient for modelling GWL. Moreover, we provide possible future research directions and recommendations to enhance the accuracy of GWL prediction models and improve relevant understanding.https://www.mdpi.com/2076-3417/13/4/2743groundwater levels (GWL)machine learningdeep learningconventional methodsforecastingwater level |
| spellingShingle | Junaid Khan Eunkyu Lee Awatef Salem Balobaid Kyungsup Kim A Comprehensive Review of Conventional, Machine Leaning, and Deep Learning Models for Groundwater Level (GWL) Forecasting Applied Sciences groundwater levels (GWL) machine learning deep learning conventional methods forecasting water level |
| title | A Comprehensive Review of Conventional, Machine Leaning, and Deep Learning Models for Groundwater Level (GWL) Forecasting |
| title_full | A Comprehensive Review of Conventional, Machine Leaning, and Deep Learning Models for Groundwater Level (GWL) Forecasting |
| title_fullStr | A Comprehensive Review of Conventional, Machine Leaning, and Deep Learning Models for Groundwater Level (GWL) Forecasting |
| title_full_unstemmed | A Comprehensive Review of Conventional, Machine Leaning, and Deep Learning Models for Groundwater Level (GWL) Forecasting |
| title_short | A Comprehensive Review of Conventional, Machine Leaning, and Deep Learning Models for Groundwater Level (GWL) Forecasting |
| title_sort | comprehensive review of conventional machine leaning and deep learning models for groundwater level gwl forecasting |
| topic | groundwater levels (GWL) machine learning deep learning conventional methods forecasting water level |
| url | https://www.mdpi.com/2076-3417/13/4/2743 |
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