Convolutional Neural Network Coupled with a Transfer-Learning Approach for Time-Series Flood Predictions

Kimura, Nobuaki; Yoshinaga, Ikuo; Sekijima, Kenji; Azechi, Issaku; Baba, Daichi

doi:10.3390/w12010096

Cited by 78 publications

(50 citation statements)

References 24 publications

(26 reference statements)

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“…CNN is a leading network architecture in deep learning techniques and has had extremely successful applications in image pattern recognition and classification [56][57][58][59]. It has only recently received significant attention in water-related time series modeling in terms of ground water level prediction [60], precipitation estimation [61,62], and flood forecasting [63,64].…”

Section: Introductionmentioning

confidence: 99%

Emulation of a Process-Based Salinity Generator for the Sacramento–San Joaquin Delta of California via Deep Learning

He¹,

Zhong²,

Sandhu³

et al. 2020

Water

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Salinity management is a subject of particular interest in estuarine environments because of the underlying biological significance of salinity and its variations in time and space. The foremost step in such management practices is understanding the spatial and temporal variations of salinity and the principal drivers of these variations. This has traditionally been achieved with the assistance of empirical or process-based models, but these can be computationally expensive for complex environmental systems. Model emulation based on data-driven methods offers a viable alternative to traditional modeling in terms of computational efficiency and improving accuracy by recognizing patterns and processes that are overlooked or underrepresented (or overrepresented) by traditional models. This paper presents a case study of emulating a process-based boundary salinity generator via deep learning for the Sacramento–San Joaquin Delta (Delta), an estuarine environment with significant economic, ecological, and social value on the Pacific coast of northern California, United States. Specifically, the study proposes a range of neural network models: (a) multilayer perceptron, (b) long short-term memory network, and (c) convolutional neural network-based models in estimating the downstream boundary salinity of the Delta on a daily time-step. These neural network models are trained and validated using half of the dataset from water year 1991 to 2002. They are then evaluated for performance in the remaining record period from water year 2003 to 2014 against the process-based boundary salinity generation model across different ranges of salinity in different types of water years. The results indicate that deep learning neural networks provide competitive or superior results compared with the process-based model, particularly when the output of the latter are incorporated as an input to the former. The improvements are generally more noticeable during extreme (i.e., wet, dry, and critical) years rather than in near-normal (i.e., above-normal and below-normal) years and during low and medium ranges of salinity rather than high range salinity. Overall, this study indicates that deep learning approaches have the potential to supplement the current practices in estimating salinity at the downstream boundary and other locations across the Delta, and thus guide real-time operations and long-term planning activities in the Delta.

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Section: Introductionmentioning

confidence: 99%

Emulation of a Process-Based Salinity Generator for the Sacramento–San Joaquin Delta of California via Deep Learning

He¹,

Zhong²,

Sandhu³

et al. 2020

Water

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“…As a result, current transfer learning solutions for intrusion detection still need to be updated [ 36 ]. A new-generation labeled dataset of an in-vehicle network proposed by Kang et al [ 43 ], which is more suitable for applying transfer learning models because, for time series classification, deep transfer learning approach shows the better performances than other TML or DL models [ 44 , 45 , 46 ]. This paper has improved the existing transfer learning model for detecting various complex types of cyber-attacks in CAN bus protocol.…”

Section: Background and Related Workmentioning

confidence: 99%

Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks

Mehedi

Anwar

Rahman

et al. 2021

Sensors

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The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security.

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“…The results show that with the increase in the lead time, neural networks showed superior performance in prediction than autoregressive models. Wu et al [13] used the convolutional neural network (CNN) [14] model for flood routing prediction, which greatly improved the prediction efficiency without reducing the accuracy.…”

Section: Introductionmentioning

confidence: 99%

Reservoir inflow predicting model based on machine learning algorithm via multi‐model fusion: A case study of Jinshuitan river basin

Zhang¹,

Wang²,

Lin³

et al. 2021

IET Cyber-Syst and Robotics

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Flood prevention and disaster mitigation have a great impact on people's lives and properties, and so it is urgent to realise high-accuracy inflow predictions for flood early warning. To this end, a prediction model based on a machine learning algorithm via a multimodel combination method is proposed to predict the inflow of Jinshuitan reservoir. Firstly, a data formatting scheme called the 'hydrological regime profile' is designed for input data. The whole data set is partitioned into a low-flow subset and a high-flow subset. Considering the high dimensions of the complex input data, convolutional neural networks (CNN), EXtreme gradient Boosting model (XGBoost) and a partial least squares model (PLS) are used. In the CNN and XGBoost models, a special loss function weighted on inflow is designed to improve the performance on high-inflow predictions. Finally, a multi-model combination method is proposed to improve the prediction performance. Compared with XGBoost, CNN and PLS, the root mean square error of the combined model is reduced by 41.64%, 72.29% and 3.41%, respectively. As a consequence, the combined model is able to predict the inflows with higher accuracy compared to the single models.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Convolutional Neural Network Coupled with a Transfer-Learning Approach for Time-Series Flood Predictions

Cited by 78 publications

References 24 publications

Emulation of a Process-Based Salinity Generator for the Sacramento–San Joaquin Delta of California via Deep Learning

Emulation of a Process-Based Salinity Generator for the Sacramento–San Joaquin Delta of California via Deep Learning

Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks

Reservoir inflow predicting model based on machine learning algorithm via multi‐model fusion: A case study of Jinshuitan river basin

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