Long Short-Term Memory (LSTM) Based Model for Flood Forecasting in Xiangjiang River

Liu, Yizhuang; Yang, Yue; Chin, Ren Jie; Wang, Chucai; Wang, Changshun

doi:10.1007/s12205-023-2469-7

Cited by 5 publications

(4 citation statements)

References 26 publications

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“…Therefore, after undergoing lag time preprocessing, it is evident that as the prediction horizon extends from 6 h to 12 h, and further to 24 h, the R-value of each predictive model decreases by approximately 0.1 less than that of models without lag time preprocessing. [49,53,55]. However, a salient discovery of this study is the significant enhancement in prediction accuracy and model performance observed after applying lag time preprocessing to spatiotemporal data, even when operating under identical forecast durations.…”

Section: Discussion Of Resultsmentioning

confidence: 85%

Flood Forecasting Using Hybrid LSTM and GRU Models with Lag Time Preprocessing

Zhang,

Zhou,

Van Griensven Thé

et al. 2023

Water

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Climate change and urbanization have increased the frequency of floods worldwide, resulting in substantial casualties and property loss. Accurate flood forecasting can offer governments early warnings about impending flood disasters, giving them a chance to evacuate and save lives. Deep learning is used in flood forecasting to improve the timeliness and accuracy of flood water level predictions. While various deep learning models similar to Long Short-Term Memory (LSTM) have achieved notable results, they have complex structures with low computational efficiency, and often lack generalizability and stability. This study applies a spatiotemporal Attention Gated Recurrent Unit (STA-GRU) model for flood prediction to increase the models’ computing efficiency. Another salient feature of our methodology is the incorporation of lag time during data preprocessing before the training of the model. Notably, for 12-h forecasting, the STA-GRU model’s R-squared (R2) value increased from 0.8125 to 0.9215. Concurrently, the model manifested reduced root mean squared error (RMSE) and mean absolute error (MAE) metrics. For a more extended 24-h forecasting, the R2 value of the STA-GRU model improved from 0.6181 to 0.7283, accompanied by diminishing RMSE and MAE values. Seven typical deep learning models—the LSTM, the Convolutional Neural Networks LSTM (CNNLSTM), the Convolutional LSTM (ConvLSTM), the spatiotemporal Attention Long Short-Term Memory (STA-LSTM), the GRU, the Convolutional Neural Networks GRU (CNNGRU), and the STA-GRU—are compared for water level prediction. Comparative analysis delineated that the use of the STA-GRU model and the application of the lag time pre-processing method significantly improved the reliability and accuracy of flood forecasting.

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Section: Discussion Of Resultsmentioning

confidence: 85%

Flood Forecasting Using Hybrid LSTM and GRU Models with Lag Time Preprocessing

Zhang,

Zhou,

Van Griensven Thé

et al. 2023

Water

View full text Add to dashboard Cite

show abstract

“…In addition, Le et al [50] reported a strong performance (with Nash-Sutcliffe efficiency (NSE > 87) from LSTM in flood forecasting in Da River basin in Vietnam. Likewise, the outperformance of LSTM with NSE > 0.98 and RMSE of <0.2 m in forecasting flooding was reported in the Xiangjiang River [51,52]. Moreover, Fang et al [33] reported that the local spatial sequential long short-term memory neural network (LSS-LSTM) method performed satisfactory prediction in terms of accuracy (93.75%) and area under the receiver operating characteristic (ROC) curve (0.965).…”

Section: Flood Susceptibility Mapsmentioning

confidence: 96%

Integrating Satellite Images and Machine Learning for Flood Prediction and Susceptibility Mapping for the Case of Amibara, Awash Basin, Ethiopia

Wedajo,

Lemma,

Fufa

et al. 2024

Remote Sensing

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Flood is one of the most destructive natural hazards affecting the environment and the socioeconomic system of the world. The effects are higher in the developing countries due to their higher vulnerability to disaster and limited coping capacity. The Awash basin is one of the flood-prone basins in Ethiopia where the frequency and severity of flooding has been increasing. Amibara district is one of the flood-affected areas in the Awash basin. To minimize the effects of flooding, reliable and up-to-date information on flooding is highly required. However, flood monitoring and forecasting systems are lacking in most basins of Ethiopia including the Awash basin. Therefore, this study aimed to (i) identify important flood causative factors, (ii) evaluate the performance of random forest (RF), linear regression, support vector machine (SVM), and long short-term memory (LSTM) machine learning models for flood prediction and susceptibility mapping in the Amibara area. For developing flood prediction and susceptibility modeling, nine causative factors were considered, namely elevation, slope, aspect, curvature, topographic wetness index, soil texture, rainfall, land use/land cover, and curve number. The Pearson correlation coefficient and information gain ratio (InGR) techniques were used to evaluate the relative importance of the factors. The machine learning models were trained and tested using 400 historic flood points collected from the 10 September 2020 Sentinel 2 image, during which a flood event occurred in the area. Multiple metrics, namely precession, recall, F1-score, accuracy, and receiver operating characteristics (area under curve), were used to evaluate the performance of the models. The results showed that all the factors considered in this study were important; elevation, rainfall, topographic wetness index, aspect, and slope were more important while land use/land cover, curve number, curvature, and soil texture were less important. Furthermore, the results showed that random forest outperformed in predicting and mapping flooding for the study area whereas the linear regression model showed the next best performance to RF. However, SVM performed poorly in flood prediction and susceptibility mapping. The integration of satellite and field datasets coupled with state-of-the-art-machine learning models are novel approaches and thus improved the accuracy of flood prediction and susceptibility mapping. Such methodology improves the state-of-the-art knowledge in this field and fills the gaps of traditional flood mapping techniques. Thus, the results of the study can provide crucial information for informed decision-making in the processes of designing flood control strategies and risk management.

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“…Water 2024, 16, 625 2 of 17 enhance the precision of forecasting. Hydrodynamic modeling can usually simulate the hydrodynamics of an entire basin, even in areas where there are no measuring devices, with a satisfactory level of accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…It can bypass some of the intrinsic factor restrictions of mechanism models to achieve reduced modeling costs and improved prediction accuracy, speed, and span [7]. A long short-term memory (LSTM) neural network was introduced into flood forecast prediction, which realizes the capture of nonlinear and periodic relationships in long-time-series hydrodynamic data by coupling with data dimensionality reduction methods, and the computational time consumption of the single-step forecast was controlled at the second or minute level, which is a great performance enhancement compared with traditional mechanism modeling [15,16]. A convolutional neural network (CNN) was used in conjunction with LSTM to construct a CNN-LSTM neural network to address the deficiency of LSTM in spatial feature extraction [17].…”

Section: Introductionmentioning

confidence: 99%

A Deep U-Net-ConvLSTM Framework with Hydrodynamic Model for Basin-Scale Hydrodynamic Prediction

Li,

Zhang,

Zhang

et al. 2024

Water

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Traditional hydrodynamic models face the significant challenge of balancing the demands of long prediction spans and precise boundary conditions, large computational areas, and low computational costs when attempting to rapidly and accurately predict the nonlinear spatial and temporal characteristics of fluids at the basin scale. To tackle this obstacle, this study constructed a novel deep learning framework with a hydrodynamic model for the rapid spatiotemporal prediction of hydrodynamics at the basin scale, named U-Net-ConvLSTM. A validated high-fidelity hydrodynamic mechanistic model was utilized to build a 20-year hydrodynamic indicator dataset of the middle and lower reaches of the Han River for the training and validation of U-Net-ConvLSTM. The findings indicate that the R2 value of the model surpassed 0.99 when comparing the single-step prediction results with the target values. Additionally, the required computing time fell by 62.08% compared with the hydrodynamic model. The ablation tests demonstrate that the U-Net-ConvLSTM framework outperforms other frameworks in terms of accuracy for basin-scale hydrodynamic prediction. In the multi-step-ahead prediction scenarios, the prediction interval increased from 1 day to 5 days, while consistently maintaining an R2 value above 0.7, which demonstrates the effectiveness of the model in the missing boundary conditions scenario. In summary, the U-Net-ConvLSTM framework is capable of making precise spatiotemporal predictions in hydrodynamics, which may be considered a high-performance computational solution for predicting hydrodynamics at the basin scale.

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Long Short-Term Memory (LSTM) Based Model for Flood Forecasting in Xiangjiang River

Cited by 5 publications

References 26 publications

Flood Forecasting Using Hybrid LSTM and GRU Models with Lag Time Preprocessing

Flood Forecasting Using Hybrid LSTM and GRU Models with Lag Time Preprocessing

Integrating Satellite Images and Machine Learning for Flood Prediction and Susceptibility Mapping for the Case of Amibara, Awash Basin, Ethiopia

A Deep U-Net-ConvLSTM Framework with Hydrodynamic Model for Basin-Scale Hydrodynamic Prediction

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