Data-driven modeling for river flood forecasting based on a piecewise linear ARX system identification

Hadid, Baya; Duviella, E.; Lecœuche, S.

doi:10.1016/j.jprocont.2019.12.007

Cited by 42 publications

(20 citation statements)

References 46 publications

(45 reference statements)

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“…Khac‐Tien Nguyen and Hock‐Chye Chua (2012) developed an adaptive network‐based fuzzy inference system prediction of water level for a 1–5 day time window. Rainfall‐runoff modeling of rivers has also been used for forecasting the river flood with piecewise affine systems, which is identified based on a combined unsupervised clustering‐linear regression technique (Hadid, Duviella, & Lecoeuche, 2020). These studies show that data‐driven methods hold a strong potential for complementing physics‐based models to enhance urban flood prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

A hybrid deep learning model for predictive flood warning and situation awareness using channel network sensors data

Dong

Farahmand

et al. 2020

Computer aided Civil Eng

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The objective of this study is to create and test a hybrid deep learning (DL) model, FastGRNN‐FCN (fast, accurate, stable and tiny gated recurrent neural network‐fully convolutional network), for urban flood prediction and situation awareness using channel network sensors data. The study used Harris County, Texas, as the testbed, and obtained channel sensor data from three historical flood events (e.g., 2016 Tax Day Flood, 2016 Memorial Day Flood, and 2017 Hurricane Harvey Flood) for training and validating the hybrid DL model. The flood data are divided into a multivariate time series and used as the model input. Each input comprises nine variables, including information of the studied channel sensor and its predecessor and successor sensors in the channel network. Precision‐recall curve and F‐measure are used to identify the optimal set of model parameters. The optimal model with a weight of 1 and a critical threshold of 0.59 are obtained through 100 iterations based on examining different weights and thresholds. The test accuracy and F‐measure eventually reach 97.8% and 0.792, respectively. The model is then tested in predicting the 2019 Imelda Flood in Houston and the results show an excellent match with the empirical flood. The results show that the model enables accurate prediction of the spatial–temporal flood propagation and recession and provides emergency response officials with a predictive flood warning tool for prioritizing the flood response and resource allocation strategies.

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Section: Literature Reviewmentioning

confidence: 99%

A hybrid deep learning model for predictive flood warning and situation awareness using channel network sensors data

Dong

Farahmand

et al. 2020

Computer aided Civil Eng

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“…The experimental results indicates the reliability of visual sensing approach. In 2020, Hadid et al [33] presents an approach for stream level prediction for a river using hybrid model and Dempster-Shafer algorithm for PWARX (Piecewise Auto-Regressive eXogeneous) model.…”

Section: Flood Management Systemmentioning

confidence: 99%

Energy efficient IoT-based cloud framework for early flood prediction

Kaur

Sood

2021

Nat Hazards

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Flood is a recurrent and crucial natural phenomenon affecting almost the entire planet. It is a critical problem that causes crop destruction, destruction to the population, loss of infrastructure, and demolition of several public utilities. An effective way to deal with this is to alert the community from incoming inundation and provide ample time to evacuate and protect property. In this article, we suggest an IoT-based energy efficient flood prediction and forecasting system. IoT sensor nodes are constrained in terms of battery and memory, so the fog layer uses an energy-saving approach based on data heterogeneity to preserve the system's power consumption. cloud storage is used for efficient storage. The environmental conditions such as temperature, humidity, rainfall, and water body parameters, i.e. water flow and water level, are being investigated for India's Kerala region to calibrate the flood phases. PCA (Principal Component Analysis) approach is used at the fog layer for attribute dimensionality reduction. To forecast the flood, the ANN (Artificial Neural Network) algorithm is used, and the simulation technique of Holt Winter is used to forecast the future flood. Data is obtained from the Indian government meteorological database and experimental assessment is carried out. The findings showed the feasibility of the proposed architecture.

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“…After resampling and extracting the RMS feature from the original healthy signal, the AR technique models and extracts the state-space equation from the RMS signal under normal conditions. The state-space definition of a healthy signal based on the AR technique is represented using the following equations [39]:…”

Section: Combination Of Arxu and Mbr For Signal Approximationmentioning

confidence: 99%

“…To improve the robustness and performance against uncertainties, an ARXU is recommended. The mathematical definition of ARXU is represented as the following equations [39,40]:…”

Section: Combination Of Arxu and Mbr For Signal Approximationmentioning

confidence: 99%

SVM-Based Bearing Anomaly Identification with Self-Tuning Network-Fuzzy Robust Proportional Multi Integral and Smart Autoregressive Model

TayebiHaghighi

Koo

2021

Applied Sciences

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In this paper, the combination of an indirect self-tuning observer, smart signal modeling, and machine learning-based classification is proposed for rolling element bearing (REB) anomaly identification. The proposed scheme has three main stages. In the first stage, the original signal is resampled, and the root mean square (RMS) signal is extracted from it. In the second stage, the normal resampled RMS signal is approximated using the AutoRegressive with eXternal Uncertainty (ARXU) technique. Moreover, the nonlinearity of the bearing signal is solved using the combination of the ARXU and the machine learning-based regression, which is called AMRXU. After signal modeling by AMRXU, the RMS resampled signal is estimated using a combination of the proportional multi-integral (PMI) technique, the variable structure (VS) Lyapunov technique, and a self-tuning network-fuzzy system (SNFS). Finally, in the third stage, the difference between the original signal and the estimated one is calculated to generate the residual signal. A machine learning-based classification technique is utilized to classify the residual signal. The Case Western Reserve University (CWRU) dataset is used to evaluate anomaly identification performance of the proposed scheme. Regarding the experimental results, the average accuracy for REB crack identification is 98.65%, 97.7%, 97.35%, and 97.67%, respectively, when the motor torque loads are 0-hp, 1-hp, 2-hp, and 3-hp.

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Data-driven modeling for river flood forecasting based on a piecewise linear ARX system identification

Cited by 42 publications

References 46 publications

A hybrid deep learning model for predictive flood warning and situation awareness using channel network sensors data

A hybrid deep learning model for predictive flood warning and situation awareness using channel network sensors data

Energy efficient IoT-based cloud framework for early flood prediction

SVM-Based Bearing Anomaly Identification with Self-Tuning Network-Fuzzy Robust Proportional Multi Integral and Smart Autoregressive Model

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