Deep Learning with a Long Short-Term Memory Networks Approach for Rainfall-Runoff Simulation

Hu, Caihong; Wu, Qiang; Li, Hui; Jian, Shengqi; Li, Nan; Lou, Zhengzheng

doi:10.3390/w10111543

Cited by 416 publications

(220 citation statements)

References 29 publications

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“…Therefore, it is interesting to explore UKF for modeling and lowering the uncertainty appeared in RNN-driven flood forecasts.Machine-learning techniques have developed fast during the last few decades, and they have been adopted as data-driven methods to model hydrological systems [11,25,26]. For instance, the back-propagation neural network (BPNN), the radial basis function (RBF), the support vector machine, the quantile regression neural network (QRNN), the recurrent neural network (RNN), the long-short term memory (LSTM) and the non-linear auto-regressive with exogenous inputs neural network (NARX) have been widely applied to modeling hydrologic and meteorological time series [27][28][29][30][31][32][33][34][35][36][37][38]. A number of recent studies indicate that ensemble artificial neural network can improve the probabilistic forecast skill for hydrological events [39][40][41].…”

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confidence: 99%

“…The Kendall tau coefficient analysis[38,52] is employed to extract the highest correlation of lag-times between model input and output values. According to the highest correlation coefficients, lag-times between the inflow of the TGR and flow/rainfall at various gauge stations are set as 6 h (TGR), 48 h (XJB reservoir), 48 h (F 1 ), 48 h (F 2 ), 42 h (F 3 ), 42 h (F 4 ), 24 h (F 5 ), 18 h (F 6 ), 18 h (F 7 ), 12 h (F 8 ), 42 h (Rainfall-I) and12 h (Rainfal-II)[36]. To reduce the adverse effect of the distinct scales of input data on model performance, all 12 input variables (one autoregressive variable plus 11 exogenous variables) were transformed into the same scale during data preprocessing.Water 2020, 12, x FOR PEER REVIEW 7 of Study area and the statistical characteristics of reservoir inflows.…”

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confidence: 99%

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Improving the Reliability of Probabilistic Multi-Step-Ahead Flood Forecasting by Fusing Unscented Kalman Filter with Recurrent Neural Network

Zhou

Guo

et al. 2020

Water

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It is fundamentally challenging to quantify the uncertainty of data-driven flood forecasting. This study introduces a general framework for probabilistic flood forecasting conditional on point forecasts. We adopt an unscented Kalman filter (UKF) post-processing technique to model the point forecasts made by a recurrent neural network and their corresponding observations. The methodology is tested by using a long-term 6-h timescale inflow series of the Three Gorges Reservoir in China. The main merits of the proposed approach lie in: first, overcoming the under-prediction phenomena in data-driven flood forecasting; second, alleviating the uncertainty encountered in data-driven flood forecasting. Two commonly used artificial neural networks, a recurrent and a static neural network, were used to make the point forecasts. Then the UKF approach driven by the point forecasts demonstrated its competency in increasing the reliability of probabilistic flood forecasts significantly, where predictive distributions encountered in multi-step-ahead flood forecasts were effectively reduced to small ranges. The results demonstrated that the UKF plus recurrent neural network approach could suitably extract the complex non-linear dependence structure between the model’s outputs and observed inflows and overcome the systematic error so that model reliability as well as forecast accuracy for future horizons could be significantly improved.

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confidence: 99%

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confidence: 99%

Improving the Reliability of Probabilistic Multi-Step-Ahead Flood Forecasting by Fusing Unscented Kalman Filter with Recurrent Neural Network

Zhou

Guo

et al. 2020

Water

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“…Structure of the DL models: a) CNN model. b) LSTM model.The second DL model is based on the Long Short-Term Memory (LSTM) layer[32,33] and it is also implemented in Matlab and using the DL toolbox. It consists of two LSTM layers with 140 hidden nodes per layer.…”

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confidence: 99%

Deep Learning Models for Denoising ECG Signals

Arsene

Hankins

Yin

2019

2019 27th European Signal Processing Conference (EUSIPCO)

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Effective and powerful methods for denoising real electrocardiogram (ECG) signals are important for wearable sensors and devices. Deep Learning (DL) models have been used extensively in image processing and other domains with great success but only very recently have been used in processing ECG signals. This paper presents several DL models namely Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Restricted Boltzmann Machine (RBM) together with the more conventional filtering methods (low pass filtering, high pass filtering, Notch filtering) and the standard wavelet-based technique for denoising EEG signals. These methods are trained, tested and evaluated on different synthetic and real ECG datasets taken from the MIT PhysioNet database and for different simulation conditions (i.e. various lengths of the ECG signals, single or multiple records). The resultsshow the CNN model is a performant model that can be used for off-line denoising ECG applications where it is satisfactory to train on a clean part of an ECG signal from an ECG record, and then to test on the same ECG signal, which would have some high level of noise added to it. However, for real-time applications or near-real time applications, this task becomes more cumbersome, as the clean part of an ECG signal is very probable to be very limited in size. Therefore the solution put forth in this work is to train a CNN model on 1 second ECG noisy artificial multiple heartbeat data (i.e. ECG at effort), which was generated in a first instance based on few sequences of real signal heartbeat ECG data (i.e. ECG at rest).Afterwards it would be possible to use the trained CNN model in real life situations to denoise the ECG signal. This corresponds also to reality, where usually the human is put at rest and the ECG is recorded and then the same human is asked to do some physical exercises and the ECG is recorded at effort. The quality of results is assessed visually but also by using the Root Mean Squared (RMS) and the Signal to Noise Ratio (SNR) measures. All CNN models used an NVIDIA TITAN V Graphical Processing Unit (GPU) with 12 GB RAM, which reduces drastically the computational times. Finally, as an element of novelty, the paper presents also a Design of Experiment (DoE) study which intends to determine the optimal structure of a CNN model, which type of study has not been seen in the literature before.

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“…It has been widely used to analyze the time series in many applications like natural language processing, speech recognition, handwriting recognition, sentiment analysis as well as in disease diagnosis. Previous studies have shown that the LSTM model outperformed the conceptual and physical-based models for simulating the rainfall-runoff process [15], and is more stable than an ANN model in different lead-time modeling [16]. The LSTM model has an advantage over other ML approaches in capturing the time-series dynamics of discharges and reducing the time consumption and memory storage [17].…”

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confidence: 99%

Flash Flood Forecasting Based on Long Short-Term Memory Networks

Song

Ding

et al. 2019

Water

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Flash floods occur frequently and distribute widely in mountainous areas because of complex geographic and geomorphic conditions and various climate types. Effective flash flood forecasting with useful lead times remains a challenge due to its high burstiness and short response time. Recently, machine learning has led to substantial changes across many areas of study. In hydrology, the advent of novel machine learning methods has started to encourage novel applications or substantially improve old ones. This study aims to establish a discharge forecasting model based on Long Short-Term Memory (LSTM) networks for flash flood forecasting in mountainous catchments. The proposed LSTM flood forecasting (LSTM-FF) model is composed of T multivariate single-step LSTM networks and takes spatial and temporal dynamics information of observed and forecast rainfall and early discharge as inputs. The case study in Anhe revealed that the proposed models can effectively predict flash floods, especially the qualified rates (the ratio of the number of qualified events to the total number of flood events) of large flood events are above 94.7% at 1-5 h lead time and range from 84.2% to 89.5% at 6-10 h lead-time. For the large flood simulation, the small flood events can help the LSTM-FF model to explore a better rainfall-runoff relationship. The impact analysis of weights in the LSTM network structures shows that the discharge input plays a more obvious role in the 1-h LSTM network and the effect decreases with the lead-time. Meanwhile, in the adjacent lead-time, the LSTM networks explored a similar relationship between input and output. The study provides a new approach for flash flood forecasting and the highly accurate forecast contributes to prepare for and mitigate disasters.Water 2020, 12, 109 2 of 15 concluded no model could make reliable flash flood forecasts in spite of the plausible results of physically-based distributed hydrological models. Many studies showed that distributed hydrological models have advantages over lumped hydrological models and data-driven models, but they are computationally inefficient and need high-resolution sophisticated input data (e.g., DEM, land-use and soil maps, and soil characteristics) [5]. Hence, their applicability is limited in mountainous catchments. The expected benefits of using high-resolution distributed models might be masked by the increasing uncertainties at small scales [6]. Lumped hydrological models for flash flood forecasting are limited by their coarse resolution and inadequate description of rainfall spatial distribution, which has a great impact on the catchment response [2]. In addition, physically-based hydrological models depend heavily on their boundary conditions, which are often poorly defined [7]. It is difficult to describe flash flood generation and propagation by a deterministic approach due to the complexity of its processes. Numerous studies indicate the gap of physically-based hydrological models in short-term flood prediction [8].With the advancements in sys...

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Deep Learning with a Long Short-Term Memory Networks Approach for Rainfall-Runoff Simulation

Cited by 416 publications

References 29 publications

Improving the Reliability of Probabilistic Multi-Step-Ahead Flood Forecasting by Fusing Unscented Kalman Filter with Recurrent Neural Network

Improving the Reliability of Probabilistic Multi-Step-Ahead Flood Forecasting by Fusing Unscented Kalman Filter with Recurrent Neural Network

Deep Learning Models for Denoising ECG Signals

Flash Flood Forecasting Based on Long Short-Term Memory Networks

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