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Groundwater level as an input to monthly predicting of water level using various machine learning algorithms

algorithm; ground penetrating radar; groundwater; machine learning; resource management; water level; water resource

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Main Authors: Sapitang M., Ridwan W.M., Ahmed A.N., Fai C.M., El-Shafie A.
Other Authors: 57215211508
Format: Article
Published: Springer Science and Business Media Deutschland GmbH 2023
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spelling my.uniten.dspace-260272023-05-29T17:06:11Z Groundwater level as an input to monthly predicting of water level using various machine learning algorithms Sapitang M. Ridwan W.M. Ahmed A.N. Fai C.M. El-Shafie A. 57215211508 57218502036 57214837520 57214146115 16068189400 algorithm; ground penetrating radar; groundwater; machine learning; resource management; water level; water resource Accurate prediction of the water level will help prevent overexploiting groundwater and help control water resources. On the other hand, water level predicting is a highly dynamic and non-linear process dependent on complex factors. Therefore, developing models to predict water levels to optimize water resources management in the reservoir is essential. Thus, this work recommends various supervised machine learning algorithms for predicting water levels with groundwater level correlation. The predicting models have Linear Regression (LR), Support Vector Machines (SVM), Gaussian Processes Regression (GPR), and Neural Network (NN). This study includes four scenarios; The first scenario (SC1) uses lag 1; second scenario (SC2) uses lag 1 and lag 2; third scenario (SC3) uses lag 1, lag 2, and lag 11 and the fourth scenario (SC4) uses lag 1, lag 2, lag 11 and lag 12. These scenarios have been determined using the autocorrelation function (ACF), and these lags represent the month. The results showed that for SC1, SC2, and SC4, all model performance in GPR gave good results where the highest R equal to 0.71 in SC1, 0.78 in SC2, and 0.73 in SC4 using the Matern 5/2 GPR model. For SC3, the Stepwise LR model gave a better result with an R of 0.79. It can be concluded that Matern 5/2 of Gaussian Processes Regression Models is a�reliable model to predict water level as the method gave a high performance in each scenario (except SC3) with a relatively fastest training time. The NN model had the worst performance to the other three models since it has the highest MAE values, RMSE, and lowest value of R in almost all four scenarios of input combinations. These results obtained in this study serves as an excellent benchmark for future water level prediction using the GPR and LR with four scenarios created. � 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. Final 2023-05-29T09:06:11Z 2023-05-29T09:06:11Z 2021 Article 10.1007/s12145-021-00654-x 2-s2.0-85109300934 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109300934&doi=10.1007%2fs12145-021-00654-x&partnerID=40&md5=1279c0d304365487e982b3d1553a438f https://irepository.uniten.edu.my/handle/123456789/26027 14 3 1269 1283 Springer Science and Business Media Deutschland GmbH Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description algorithm; ground penetrating radar; groundwater; machine learning; resource management; water level; water resource
author2 57215211508
author_facet 57215211508
Sapitang M.
Ridwan W.M.
Ahmed A.N.
Fai C.M.
El-Shafie A.
format Article
author Sapitang M.
Ridwan W.M.
Ahmed A.N.
Fai C.M.
El-Shafie A.
spellingShingle Sapitang M.
Ridwan W.M.
Ahmed A.N.
Fai C.M.
El-Shafie A.
Groundwater level as an input to monthly predicting of water level using various machine learning algorithms
author_sort Sapitang M.
title Groundwater level as an input to monthly predicting of water level using various machine learning algorithms
title_short Groundwater level as an input to monthly predicting of water level using various machine learning algorithms
title_full Groundwater level as an input to monthly predicting of water level using various machine learning algorithms
title_fullStr Groundwater level as an input to monthly predicting of water level using various machine learning algorithms
title_full_unstemmed Groundwater level as an input to monthly predicting of water level using various machine learning algorithms
title_sort groundwater level as an input to monthly predicting of water level using various machine learning algorithms
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2023
_version_ 1806427699917357056
score 13.222552

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