Interpretable spatio-temporal attention LSTM model for flood forecasting

Ding, Yaogen; Zhu, Yuemin; Feng, Jun; Zhang, Pengcheng; Cheng, Zirun

doi:10.1016/j.neucom.2020.04.110

Cited by 280 publications

(112 citation statements)

References 27 publications

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“…These gates help in improving the efficiency by utilizing their decision power that which information need to be discarded and which new information should add to the cell state. Detailed working can be seen in [37]. Here it is important to consider that LSTM has the ability to preserve information only from the past because the input it receive is always from the previous neurons [38] known as forward direction, which may leads to low prediction as it have no information about future.…”

Section: A Proposed Hybrid DL Enabled Architecturementioning

confidence: 99%

A Dynamic Robust DL-Based Model for Android Malware Detection

2021

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The dramatic increase in Android-based smart devices has brought technological revolution to improve the overall quality of life and thus making it worth a billion-dollar market. Despite the huge hype surrounding Android market, the prevalent and potentially sophisticated malicious mobile malware has become a serious threat to the popular Android platform and an ideal target for varied cyber adversaries. Conversely, multivector malware efficient and timely detection is extremely challenging because it usually hides itself under legitimate third party software's and having the capability to be easily crafted on any executable file extension. To better streamline this complex issue of paramount concern, the authors propose a highly proficient hybrid deep learning (DL)-enabled intelligent multi-vector malware detection mechanism. The devised approach leverages Convolutional Neural Networks and Bidirectional Long Short-Term Memory (BiLSTM) to efficiently identify persistent malware. The proposed technique has been thoroughly evaluated with publicly available datasets, standard performance metrics, and state-of-the-art hybrid DL-driven architectures and benchmark DL algorithms. Besides, the proposed framework has been cross-validated and shows out performance both in terms of time efficiency and detection accuracy.

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Section: A Proposed Hybrid DL Enabled Architecturementioning

confidence: 99%

A Dynamic Robust DL-Based Model for Android Malware Detection

2021

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“…16 Ma et al 17 combined the Grid concept and LSTM (G-LSTM) for the forecasting of fuel cell degradation. More than that, the latest researches applied LSTM to more hot areas of prediction, for example, electricity price forecasting, 18 flood forecasting, 19 wind speed forecasting, 20 air pollution forecasting, 21 voltages forecasting, 22 demand forecasting, 23 photovoltaic power forecasting, 24 and so forth. 25,26 The LSTM was wielded to alleviate the vanished gradient in a multi-layer network architecture.…”

Section: Overview Of Studies On Carbon Price Forecasting and Other mentioning

confidence: 99%

“…Additionally, the values of A MAE , A MAPE , A RMSE , and A R2 are closer to 0, the smaller amelioration the proposed model renders. The definition of, A MAE , A MAPE , A RMSE , and A R2 is described as Equations (16)(17)(18)(19). where the subscript 1 and 2 denote the statistic index of the proposed model and other benchmark models.…”

Section: Performance Evaluation Criteriamentioning

confidence: 99%

An ensemble‐driven long short‐term memory model based on mode decomposition for carbon price forecasting of all eight carbon trading pilots in China

Sun

2020

Energy Science & Engineering

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Global climate change, which was triggered by the excessive emissions of greenhouse gases, has become an obstacle in sustainable development. Among all greenhouse gases, carbon dioxide is the most prominent and is artificially controllable. China ranks as the largest emitter of carbon dioxide worldwide and is a pioneer in economic growth. 1 As the international community is advocating in reducing carbon emissions, Chinese carbon emissions and its corresponding

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“…LSTM solves the vanishing gradient problem of the original recurrent neural network (RNN). It has strong time-series data processing capabilities and is widely used in time-series data modeling [43][44][45].…”

Section: Model Integrationmentioning

confidence: 99%

Integrated Deep Neural Networks-Based Complex System for Urban Water Management

et al. 2020

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Although the management and planning of water resources are extremely significant to human development, the complexity of implementation is unimaginable. To achieve this, the high-precision water consumption prediction is actually the key component of urban water optimization management system. Water consumption is usually affected by many factors, such as weather, economy, and water prices. If these impact factors are directly combined to predict water consumption, the weight of each perspective on the water consumption will be ignored, which will be greatly detrimental to the prediction accuracy. Therefore, this paper proposes a deep neural network-based complex system for urban water management. The essence of it is to formulate a water consumption prediction model with the aid of principal component analysis (PCA) and the integrated deep neural network, which is abbreviated as UWM-Id. The PCA classifies the factors affecting water consumption in the original data into three categories according to their correlation and inputs them into the neural network model. The results in the previous step are assigned weights and integrated into the form of fully connected layer. Finally, analyzing the sensitivity of the proposed UWM-Id and comparing its performance with a series of commonly used baseline methods for data mining, a large number of experiments have proved that UWM-Id has good performance and can be used for urban water management system.

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Interpretable spatio-temporal attention LSTM model for flood forecasting

Cited by 280 publications

References 27 publications

A Dynamic Robust DL-Based Model for Android Malware Detection

A Dynamic Robust DL-Based Model for Android Malware Detection

An ensemble‐driven long short‐term memory model based on mode decomposition for carbon price forecasting of all eight carbon trading pilots in China

Integrated Deep Neural Networks-Based Complex System for Urban Water Management

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