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Novel Time Series Bagging Based Hybrid Models for Predicting Historical Water Levels in the Mekong Delta Region, Vietnam

Nguyen Thanh Hoan1, Nguyen Van Dung1, Ho Le Thu1, Hoa Thuy Quynh1, Nadhir Al-Ansari2,*, Tran Van Phong3, Phan Trong Trinh3, Dam Duc Nguyen4, Hiep Van Le4, Hanh Bich Thi Nguyen4, Mahdis Amiri5, Indra Prakash6, Binh Thai Pham4,*

1 Institute of Geography, Vietnam Academy of Science and Technology, Hanoi, 10000, Viet Nam
2 Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, Lulea, 971 87, Sweden
3 Institute of Geological Sciences, Vietnam Academy of Science and Technology (VAST), Dong Da, 10000, Hanoi, Viet Nam
4 University of Transport Technology, Thanh Xuan, Ha Noi, 10000, Viet Nam
5 Department of Watershed & Arid Zone Management, Gorgan University of Agricultural Sciences & Natural Resources, Gorgan, 4918943464, Iran
6 DDG (R) Geological Survey of India, Gandhinagar, 382010, India

* Corresponding Authors: Nadhir Al-Ansari. Email: email; Binh Thai Pham. Email: email

(This article belongs to the Special Issue: Soft Computing Techniques in Materials Science and Engineering)

Computer Modeling in Engineering & Sciences 2022, 131(3), 1431-1449. https://doi.org/10.32604/cmes.2022.018699

Abstract

Water level predictions in the river, lake and delta play an important role in flood management. Every year Mekong River delta of Vietnam is experiencing flood due to heavy monsoon rains and high tides. Land subsidence may also aggravate flooding problems in this area. Therefore, accurate predictions of water levels in this region are very important to forewarn the people and authorities for taking timely adequate remedial measures to prevent losses of life and property. There are so many methods available to predict the water levels based on historical data but nowadays Machine Learning (ML) methods are considered the best tool for accurate prediction. In this study, we have used surface water level data of 18 water level measurement stations of the Mekong River delta from 2000 to 2018 to build novel time-series Bagging based hybrid ML models namely: Bagging (RF), Bagging (SOM) and Bagging (M5P) to predict historical water levels in the study area. Performances of the Bagging-based hybrid models were compared with Reduced Error Pruning Trees (REPT), which is a benchmark ML model. The data of 19 years period was divided into 70:30 ratio for the modeling. The data of the period 1/2000 to 5/2013 (which is about 70% of total data) was used for the training and for the period 5/2013 to 12/2018 (which is about 30% of total data) was used for testing (validating) the models. Performance of the models was evaluated using standard statistical measures: Coefficient of Determination (R2), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Results show that the performance of all the developed models is good (R2 > 0.9) for the prediction of water levels in the study area. However, the Bagging-based hybrid models are slightly better than another model such as REPT. Thus, these Bagging-based hybrid time series models can be used for predicting water levels at Mekong data.

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APA Style
Hoan, N.T., Dung, N.V., Thu, H.L., Quynh, H.T., Al-Ansari, N. et al. (2022). Novel time series bagging based hybrid models for predicting historical water levels in the mekong delta region, vietnam. Computer Modeling in Engineering & Sciences, 131(3), 1431-1449. https://doi.org/10.32604/cmes.2022.018699
Vancouver Style
Hoan NT, Dung NV, Thu HL, Quynh HT, Al-Ansari N, Phong TV, et al. Novel time series bagging based hybrid models for predicting historical water levels in the mekong delta region, vietnam. Comput Model Eng Sci. 2022;131(3):1431-1449 https://doi.org/10.32604/cmes.2022.018699
IEEE Style
N.T. Hoan et al., "Novel Time Series Bagging Based Hybrid Models for Predicting Historical Water Levels in the Mekong Delta Region, Vietnam," Comput. Model. Eng. Sci., vol. 131, no. 3, pp. 1431-1449. 2022. https://doi.org/10.32604/cmes.2022.018699



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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