Linear and stratified sampling-based deep learning models for improving the river streamflow forecasting to mitigate flooding disaster
algorithm; flooding; forecasting method; machine learning; river flow; sampling; streamflow; Tigris River
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Springer Science and Business Media B.V.
2023
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my.uniten.dspace-268692023-05-29T17:37:23Z Linear and stratified sampling-based deep learning models for improving the river streamflow forecasting to mitigate flooding disaster Afan H.A. Yafouz A. Birima A.H. Ahmed A.N. Kisi O. Chaplot B. El-Shafie A. 56436626600 57221981418 23466519000 57214837520 6507051085 57201316781 16068189400 algorithm; flooding; forecasting method; machine learning; river flow; sampling; streamflow; Tigris River Due to the need to reduce the flooding disaster, river streamflow prediction is required to be enhanced by the aid of deep learning algorithms. To achieve accurate model of streamflow prediction, it is important to provide suitable data sets to train the predictive models. Thus, this research has investigated two sampling approaches by using deep learning algorithms. These sampling approaches are linear and stratified selection in deep learning algorithms. This investigation has been performed on the Tigris River data set in terms of 2 scenarios. The first scenario: implementation of 12 different linear and stratified sampling selection in deep learning models. This scenario is trained and tested as much as a number of months per year�12�months. The second scenario: the complete time series is taken into consideration while performing the two approaches that are utilized in this research. Furthermore, the optimal input combination is identified via correlation analysis. To evaluate the performance of the algorithms utilized in this research, a number of metrics have been used which are Root Mean Square Error RMSE, Absolute Error AE, Relative Error RE, Relative Error Lenient REL, Relative Error Strict RES, Root Relative Squared Error RRSE, Coefficient of determination R2, Spearman rho and Kendall tau. The results have indicated that in both scenarios, stratified-deep learning (SDL) improves the accuracy by about 7.96�94.6 with respect to several assessment criteria. Thus, finally, it is worth mentioning that SDL outperforms Linear-deep learning (LDL) in monthly streamflow modelling. � 2022, The Author(s), under exclusive licence to Springer Nature B.V. Final 2023-05-29T09:37:23Z 2023-05-29T09:37:23Z 2022 Article 10.1007/s11069-022-05237-7 2-s2.0-85124715274 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124715274&doi=10.1007%2fs11069-022-05237-7&partnerID=40&md5=7afd748351e4646639fffc00cc9414b6 https://irepository.uniten.edu.my/handle/123456789/26869 112 2 1527 1545 Springer Science and Business Media B.V. Scopus |
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| description |
algorithm; flooding; forecasting method; machine learning; river flow; sampling; streamflow; Tigris River |
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56436626600 |
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56436626600 Afan H.A. Yafouz A. Birima A.H. Ahmed A.N. Kisi O. Chaplot B. El-Shafie A. |
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Article |
| author |
Afan H.A. Yafouz A. Birima A.H. Ahmed A.N. Kisi O. Chaplot B. El-Shafie A. |
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Afan H.A. Yafouz A. Birima A.H. Ahmed A.N. Kisi O. Chaplot B. El-Shafie A. Linear and stratified sampling-based deep learning models for improving the river streamflow forecasting to mitigate flooding disaster |
| author_sort |
Afan H.A. |
| title |
Linear and stratified sampling-based deep learning models for improving the river streamflow forecasting to mitigate flooding disaster |
| title_short |
Linear and stratified sampling-based deep learning models for improving the river streamflow forecasting to mitigate flooding disaster |
| title_full |
Linear and stratified sampling-based deep learning models for improving the river streamflow forecasting to mitigate flooding disaster |
| title_fullStr |
Linear and stratified sampling-based deep learning models for improving the river streamflow forecasting to mitigate flooding disaster |
| title_full_unstemmed |
Linear and stratified sampling-based deep learning models for improving the river streamflow forecasting to mitigate flooding disaster |
| title_sort |
linear and stratified sampling-based deep learning models for improving the river streamflow forecasting to mitigate flooding disaster |
| publisher |
Springer Science and Business Media B.V. |
| publishDate |
2023 |
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1806427897598050304 |
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13.214268 |