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

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...

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Autores principales:	Shiang-Jen Wu, Chih-Tsu Hsu, Che-Hao Chang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	ANN roadside IoT sensors simulations of the gridded rainstorms 2D inundation simulation and real-time error correction Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500
Acceso en línea:	https://doaj.org/article/46e8c0e8c657495388a3a28f87a9f9b2
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spelling	oai:doaj.org-article:46e8c0e8c657495388a3a28f87a9f9b22021-11-11T19:58:13ZStochastic Modeling for Estimating Real-Time Inundation Depths at Roadside IoT Sensors Using the ANN-Derived Model10.3390/w132131282073-4441https://doaj.org/article/46e8c0e8c657495388a3a28f87a9f9b22021-11-01T00:00:00Zhttps://www.mdpi.com/2073-4441/13/21/3128https://doaj.org/toc/2073-4441This 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.Shiang-Jen WuChih-Tsu HsuChe-Hao ChangMDPI AGarticleANNroadside IoT sensorssimulations of the gridded rainstorms2D inundation simulation and real-time error correctionHydraulic engineeringTC1-978Water supply for domestic and industrial purposesTD201-500ENWater, Vol 13, Iss 3128, p 3128 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	ANN roadside IoT sensors simulations of the gridded rainstorms 2D inundation simulation and real-time error correction Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500
spellingShingle	ANN roadside IoT sensors simulations of the gridded rainstorms 2D inundation simulation and real-time error correction Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500 Shiang-Jen Wu Chih-Tsu Hsu Che-Hao Chang Stochastic Modeling for Estimating Real-Time Inundation Depths at Roadside IoT Sensors Using the ANN-Derived Model
description	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.
format	article
author	Shiang-Jen Wu Chih-Tsu Hsu Che-Hao Chang
author_facet	Shiang-Jen Wu Chih-Tsu Hsu Che-Hao Chang
author_sort	Shiang-Jen Wu
title	Stochastic Modeling for Estimating Real-Time Inundation Depths at Roadside IoT Sensors Using the ANN-Derived Model
title_short	Stochastic Modeling for Estimating Real-Time Inundation Depths at Roadside IoT Sensors Using the ANN-Derived Model
title_full	Stochastic Modeling for Estimating Real-Time Inundation Depths at Roadside IoT Sensors Using the ANN-Derived Model
title_fullStr	Stochastic Modeling for Estimating Real-Time Inundation Depths at Roadside IoT Sensors Using the ANN-Derived Model
title_full_unstemmed	Stochastic Modeling for Estimating Real-Time Inundation Depths at Roadside IoT Sensors Using the ANN-Derived Model
title_sort	stochastic modeling for estimating real-time inundation depths at roadside iot sensors using the ann-derived model
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/46e8c0e8c657495388a3a28f87a9f9b2
work_keys_str_mv	AT shiangjenwu stochasticmodelingforestimatingrealtimeinundationdepthsatroadsideiotsensorsusingtheannderivedmodel AT chihtsuhsu stochasticmodelingforestimatingrealtimeinundationdepthsatroadsideiotsensorsusingtheannderivedmodel AT chehaochang stochasticmodelingforestimatingrealtimeinundationdepthsatroadsideiotsensorsusingtheannderivedmodel
_version_	1718431343488008192

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

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