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Article

Stochastic Modeling for Estimating Real-Time Inundation Depths at Roadside IoT Sensors Using the ANN-Derived Model

1
Department of Civil and Disaster Prevention Engineering, National United University, Miaoli 36063, Taiwan
2
National Center for High-Performance Computing, Hsinchu 30076, Taiwan
3
Department of Civil Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editors: Fi-John Chang, Li-Chiu Chang and Jui-Fa Chen
Water 2021, 13(21), 3128; https://doi.org/10.3390/w13213128
Received: 22 September 2021 / Revised: 22 October 2021 / Accepted: 25 October 2021 / Published: 5 November 2021
This paper aims to develop a stochastic model (SM_EID_IOT) for estimating the inundation depths and associated 95% confidence intervals at the specific locations of the roadside water-level gauges, i.e., Internet of Things (IoT) sensors under the observed water levels/rainfalls and the precipitation forecasts given. The proposed SM_EID_IOT model is an ANN-derived one, a modified artificial neural network model (i.e., the ANN_GA-SA_MTF) in which the associated ANN weights are calibrated via a modified genetic algorithm with a variety of transfer functions considered. To enhance the reliability and accuracy of the proposed SM_EID_IOT model in the estimations of the inundation depths at the IoT sensors, a great number of the rainfall induced flood events as the training and validation datasets are simulated by the 2D hydraulic dynamic (SOBEK) model with the simulated rain fields via the stochastic generation model for the short-term gridded rainstorms. According to the results of model demonstration, Nankon catchment, located in northern Taiwan, the proposed SM_EID_IOT model can estimate the inundation depths at the various lead times with high reliability in capturing the validation datasets. Moreover, through the integrated real-time error correction method integrated with the proposed SM_EID_IOT model, the resulting corrected inundation-depth estimates exhibit a good agreement with the validated ones in time under an acceptable bias. View Full-Text
Keywords: ANN; roadside IoT sensors; simulations of the gridded rainstorms; 2D inundation simulation and real-time error correction ANN; roadside IoT sensors; simulations of the gridded rainstorms; 2D inundation simulation and real-time error correction
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MDPI and ACS Style

Wu, S.-J.; Hsu, C.-T.; Chang, C.-H. Stochastic Modeling for Estimating Real-Time Inundation Depths at Roadside IoT Sensors Using the ANN-Derived Model. Water 2021, 13, 3128. https://doi.org/10.3390/w13213128

AMA Style

Wu S-J, Hsu C-T, Chang C-H. Stochastic Modeling for Estimating Real-Time Inundation Depths at Roadside IoT Sensors Using the ANN-Derived Model. Water. 2021; 13(21):3128. https://doi.org/10.3390/w13213128

Chicago/Turabian Style

Wu, Shiang-Jen, Chih-Tsu Hsu, and Che-Hao Chang. 2021. "Stochastic Modeling for Estimating Real-Time Inundation Depths at Roadside IoT Sensors Using the ANN-Derived Model" Water 13, no. 21: 3128. https://doi.org/10.3390/w13213128

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