Deep convolutional neural network to predict ground water level
In contrast to the atmosphere and fresh surface water, which can only briefly store water, the natural water cycle may use groundwater as a “reservoir” that stores water for extended periods. Even though there is a considerable degree of variation and complexity in the subsurface environment, ther...
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2024
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my.iium.irep.1064042024-05-23T08:25:04Z http://irep.iium.edu.my/106404/ Deep convolutional neural network to predict ground water level Zamani, Abu Sarwar Hassan Abdalla Hashim, Aisha Gopi, Arepalli Moholkar, Kavita Rizwanullah, Mohammed Altaee, Rasool TK7885 Computer engineering In contrast to the atmosphere and fresh surface water, which can only briefly store water, the natural water cycle may use groundwater as a “reservoir” that stores water for extended periods. Even though there is a considerable degree of variation and complexity in the subsurface environment, there is a minimal availability of data from the field. Both of these challenges were faced by those who used models that were based on actual reality. Statistical modelling gradually improved the accuracy of the model’s calibration. Groundwater has become an increasingly important resource for supplying the water requirements of a rising global population. The fact that there is such a large stockpile allows it to be used once again, even during dry seasons or droughts. This article presents a deep convolutional neural network-based model for predicting groundwater levels. As part of the experimental setup, 174 satellite pictures of groundwater are included in the input data set. Images are preprocessed using the CLAHE method. The CNN, SVM, and AdaBoost methods make up the classification model. The results have shown that CNN can classify things correctly 98.5 per cent of the time. Precision and Recall rate of Deep CNN is also better for ground water image classification. Springer Nature 2024-04 Article PeerReviewed application/pdf en http://irep.iium.edu.my/106404/7/106404_Deep%20convolutional%20neural%20network%20to%20predic.pdf application/pdf en http://irep.iium.edu.my/106404/19/106404_Deep%20convolutional%20neural%20network%20to%20predict_Scopus.pdf Zamani, Abu Sarwar and Hassan Abdalla Hashim, Aisha and Gopi, Arepalli and Moholkar, Kavita and Rizwanullah, Mohammed and Altaee, Rasool (2024) Deep convolutional neural network to predict ground water level. Spatial Information Research, 32 (2). pp. 1-9. ISSN 2366-3286 E-ISSN 2366-3294 https://link.springer.com/article/10.1007/s41324-023-00537-x doi:10.1007/s41324-023-00537-x |
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TK7885 Computer engineering |
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TK7885 Computer engineering Zamani, Abu Sarwar Hassan Abdalla Hashim, Aisha Gopi, Arepalli Moholkar, Kavita Rizwanullah, Mohammed Altaee, Rasool Deep convolutional neural network to predict ground water level |
| description |
In contrast to the atmosphere and fresh surface water, which can only briefly store water, the natural water cycle may
use groundwater as a “reservoir” that stores water for extended periods. Even though there is a considerable degree of
variation and complexity in the subsurface environment, there is a minimal availability of data from the field. Both of
these challenges were faced by those who used models that were based on actual reality. Statistical modelling gradually
improved the accuracy of the model’s calibration. Groundwater has become an increasingly important resource for supplying the water requirements of a rising global population. The fact that there is such a large stockpile allows it to be used
once again, even during dry seasons or droughts. This article presents a deep convolutional neural network-based model
for predicting groundwater levels. As part of the experimental setup, 174 satellite pictures of groundwater are included in
the input data set. Images are preprocessed using the CLAHE method. The CNN, SVM, and AdaBoost methods make up
the classification model. The results have shown that CNN can classify things correctly 98.5 per cent of the time. Precision and Recall rate of Deep CNN is also better for ground water image classification. |
| format |
Article |
| author |
Zamani, Abu Sarwar Hassan Abdalla Hashim, Aisha Gopi, Arepalli Moholkar, Kavita Rizwanullah, Mohammed Altaee, Rasool |
| author_facet |
Zamani, Abu Sarwar Hassan Abdalla Hashim, Aisha Gopi, Arepalli Moholkar, Kavita Rizwanullah, Mohammed Altaee, Rasool |
| author_sort |
Zamani, Abu Sarwar |
| title |
Deep convolutional neural network to predict ground water level |
| title_short |
Deep convolutional neural network to predict ground water level |
| title_full |
Deep convolutional neural network to predict ground water level |
| title_fullStr |
Deep convolutional neural network to predict ground water level |
| title_full_unstemmed |
Deep convolutional neural network to predict ground water level |
| title_sort |
deep convolutional neural network to predict ground water level |
| publisher |
Springer Nature |
| publishDate |
2024 |
| url |
http://irep.iium.edu.my/106404/7/106404_Deep%20convolutional%20neural%20network%20to%20predic.pdf http://irep.iium.edu.my/106404/19/106404_Deep%20convolutional%20neural%20network%20to%20predict_Scopus.pdf http://irep.iium.edu.my/106404/ https://link.springer.com/article/10.1007/s41324-023-00537-x |
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13.211869 |