Support vector machine and data assimilation framework for Groundwater Level Forecasting using GRACE satellite data

Liu, Di; Mishra, Ashok K.; Yu, Zhongbo; Lü, Haishen; Li, Yajie

doi:10.1016/j.jhydrol.2021.126929

Cited by 40 publications

(13 citation statements)

References 94 publications

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“…In addition, statistical models (SM) by [12][13][14] and mathematical models (MM) by [15][16][17] have also been used to predict GWL changes. In recent years, there has been a growing interest in applying machine learning and data-driven methodologies to groundwater modeling [18][19][20]. With the persistent threat of climate change and human influences, access to high-resolution and continuous hydrologic data is critical for projecting trends and water resource availability [19].…”

Section: Introductionmentioning

confidence: 99%

Mathematical and Machine Learning Models for Groundwater Level Changes: A Systematic Review and Bibliographic Analysis

et al. 2022

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With the effects of climate change such as increasing heat, higher rainfall, and more recurrent extreme weather events including storms and floods, a unique approach to studying the effects of climatic elements on groundwater level variations is required. These unique approaches will help people make better decisions. Researchers and stakeholders can attain these goals if they become familiar with current machine learning and mathematical model approaches to predicting groundwater level changes. However, descriptions of machine learning and mathematical model approaches for forecasting groundwater level changes are lacking. This study picked 117 papers from the Scopus scholarly database to address this knowledge gap. In a systematic review, the publications were examined using quantitative and qualitative approaches, and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was chosen as the reporting format. Machine learning and mathematical model techniques have made significant contributions to predicting groundwater level changes, according to the study. However, the domain is skewed because machine learning has been more popular in recent years, with random forest (RF) methods dominating, followed by the methods of support vector machine (SVM) and artificial neural network (ANN). Machine learning ensembles have also been found to help with aspects of computational complexity, such as performance and training times. Furthermore, compared to mathematical model techniques, machine learning approaches achieve higher accuracies, according to our research. As a result, it is advised that academics employ new machine learning techniques while also considering mathematical model approaches to predicting groundwater level changes.

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Section: Introductionmentioning

confidence: 99%

Mathematical and Machine Learning Models for Groundwater Level Changes: A Systematic Review and Bibliographic Analysis

et al. 2022

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“…Several machine learning regression learners have been applied in predicting terrestrial water storage changes, and this includes physically-based algorithms (Fisher et al, 2008), data assimilation (DA) methods (Liu et al, 2021), empirical/semi-empirical algorithms (Humphrey and Gudmundsson, 2019;Yao et al, 2015). Traditionally, physically-based algorithms rely on data from satellite and meteorological observations for TWSA learning.…”

Section: Introductionmentioning

confidence: 99%

“…Our study introduces a convolution-based support vector machine (CSVM) for the GRACE-time series reconstruction procedure. The support vector machine has been reported to bring about a steady balance between satellite-based and model-based datasets in hydrological studies (Yao et al, 2017;Liu et al, 2021), enhancing its modeling robustness and predictions capabilities even in severely non-linear system states. The goal is to attain reliable machine-learning techniques with accuracy and efficiency that can successfully reconstruct observations from the GRACE mission.…”

Section: Introductionmentioning

confidence: 99%

Reconstructing terrestrial water storage anomalies using convolution-based support vector machine

Kalu

Ndehedehe

Okwuashi

et al. 2023

Journal of Hydrology: Regional Studies

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“…It is critical to maintain sustainable groundwater usage, especially in light of urbanisation and a population that is increasingly reliant on groundwater. An essential part of agricultural water management is the accurate and simple forecast of farmland GWL [7]. The effective management of groundwater resources and the long‐term usage of GWLs are heavily reliant on accurate GWL prediction.…”

Section: Introductionmentioning

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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Support vector machine and data assimilation framework for Groundwater Level Forecasting using GRACE satellite data

Cited by 40 publications

References 94 publications

Mathematical and Machine Learning Models for Groundwater Level Changes: A Systematic Review and Bibliographic Analysis

Mathematical and Machine Learning Models for Groundwater Level Changes: A Systematic Review and Bibliographic Analysis

Reconstructing terrestrial water storage anomalies using convolution-based support vector machine

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

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