Data driven of underground water level using artificial intelligence hybrid algorithms
Abstract As the population grows, industry and agriculture have also developed and water resources require quantitative and qualitative management. Currently, the management of water resources is essential in the exploitation and development of these resources. For this reason, it is important to st...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-06-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-35255-9 |
| _version_ | 1797789789658808320 |
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| author | Mohammadtaghi Rahimi Hossein Ebrahimi |
| author_facet | Mohammadtaghi Rahimi Hossein Ebrahimi |
| author_sort | Mohammadtaghi Rahimi |
| collection | DOAJ |
| description | Abstract As the population grows, industry and agriculture have also developed and water resources require quantitative and qualitative management. Currently, the management of water resources is essential in the exploitation and development of these resources. For this reason, it is important to study water level fluctuations to check the amount of underground water storage. It is vital to study the level of underground water in Khuzestan province with a dry climate. The methods which exist for predicting and managing water resources are used in studies according to their strengths and weaknesses and according to the conditions. In recent years, artificial intelligence has been used extensively for groundwater resources worldwide. Since artificial intelligence models have provided good results in water resources up to now, in this study, the hybrid model of three new recombined methods including FF-KNN, ABC-KNN and DL-FF-KNN-ABC-MLP has been used to predict the underground water level in Khuzestan province (Qale-Tol area). The novelty of this technique is that it first does classification by presenting the first block (combination of FF-DWKNN algorithm) and predicts with the second block (combination of ABC-MLP algorithm). The algorithm’s ability to decrease data noise will be enabled by this feature. In order to predict this key and important parameter, a part of the data related to wells 1–5 has been used to build artificial intelligence hybrid models and also to test these models, and to check this model three wells 6–8 have been used for the development of these models. After checking the results, it is clear that the statistical RMSE values of this algorithm including test, train and total data are 0.0451, 0.0597 and 0.0701, respectively. According to the results presented in the table reports, the performance accuracy of DL-FF-KNN-ABC-MLP for predicting this key parameter is very high. |
| first_indexed | 2024-03-13T01:55:39Z |
| format | Article |
| id | doaj.art-9458038cd31c4e4f873fd478d98c6dfc |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-13T01:55:39Z |
| publishDate | 2023-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-9458038cd31c4e4f873fd478d98c6dfc2023-07-02T11:15:26ZengNature PortfolioScientific Reports2045-23222023-06-0113111410.1038/s41598-023-35255-9Data driven of underground water level using artificial intelligence hybrid algorithmsMohammadtaghi Rahimi0Hossein Ebrahimi1Department of Civil Engineering, Kish international Branch, Islamic Azad UniversityDepartment of Water Science and Engineering, Shahr-e-Qods Branch, Islamic Azad UniversityAbstract As the population grows, industry and agriculture have also developed and water resources require quantitative and qualitative management. Currently, the management of water resources is essential in the exploitation and development of these resources. For this reason, it is important to study water level fluctuations to check the amount of underground water storage. It is vital to study the level of underground water in Khuzestan province with a dry climate. The methods which exist for predicting and managing water resources are used in studies according to their strengths and weaknesses and according to the conditions. In recent years, artificial intelligence has been used extensively for groundwater resources worldwide. Since artificial intelligence models have provided good results in water resources up to now, in this study, the hybrid model of three new recombined methods including FF-KNN, ABC-KNN and DL-FF-KNN-ABC-MLP has been used to predict the underground water level in Khuzestan province (Qale-Tol area). The novelty of this technique is that it first does classification by presenting the first block (combination of FF-DWKNN algorithm) and predicts with the second block (combination of ABC-MLP algorithm). The algorithm’s ability to decrease data noise will be enabled by this feature. In order to predict this key and important parameter, a part of the data related to wells 1–5 has been used to build artificial intelligence hybrid models and also to test these models, and to check this model three wells 6–8 have been used for the development of these models. After checking the results, it is clear that the statistical RMSE values of this algorithm including test, train and total data are 0.0451, 0.0597 and 0.0701, respectively. According to the results presented in the table reports, the performance accuracy of DL-FF-KNN-ABC-MLP for predicting this key parameter is very high.https://doi.org/10.1038/s41598-023-35255-9 |
| spellingShingle | Mohammadtaghi Rahimi Hossein Ebrahimi Data driven of underground water level using artificial intelligence hybrid algorithms Scientific Reports |
| title | Data driven of underground water level using artificial intelligence hybrid algorithms |
| title_full | Data driven of underground water level using artificial intelligence hybrid algorithms |
| title_fullStr | Data driven of underground water level using artificial intelligence hybrid algorithms |
| title_full_unstemmed | Data driven of underground water level using artificial intelligence hybrid algorithms |
| title_short | Data driven of underground water level using artificial intelligence hybrid algorithms |
| title_sort | data driven of underground water level using artificial intelligence hybrid algorithms |
| url | https://doi.org/10.1038/s41598-023-35255-9 |
| work_keys_str_mv | AT mohammadtaghirahimi datadrivenofundergroundwaterlevelusingartificialintelligencehybridalgorithms AT hosseinebrahimi datadrivenofundergroundwaterlevelusingartificialintelligencehybridalgorithms |