Typhoon Intensity Forecasting Based on LSTM Using the Rolling Forecast Method

Yuan, Shijin; Wang, Cheng; Mu, Bin; Zhou, Feifan; Duan, Wansuo

doi:10.3390/a14030083

Cited by 35 publications

(18 citation statements)

References 28 publications

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“…Due to artificial control and natural factors, such as sluices, pumps, rainfall, and tides, the variations in river water levels are highly uncertain [29]. Therefore, it is necessary to consider the rolling forecast method [30][31][32][33]. In this study, the river section water levels were forecast by the rolling forecast method, as shown in Figure 3.…”

Section: Real-time Rolling Forecast Methods and Implementationmentioning

confidence: 99%

Short Term Real-Time Rolling Forecast of Urban River Water Levels Based on LSTM: A Case Study in Fuzhou City, China

Liu

Wang

Feng

et al. 2021

IJERPH

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Water level management is an important part of urban water system management. In flood season, the river should be controlled to ensure the ecological and landscape water level. In non-flood season, the water level should be lowered to ensure smooth drainage. In urban areas, the response of the river water level to rainfall and artificial regulation is relatively rapid and strong. Therefore, building a mathematical model to forecast the short-term trend of urban river water levels can provide a scientific basis for decision makers and is of great significance for the management of urban water systems. With a focus on the high uncertainty of urban river water level prediction, a real-time rolling forecast method for the short-term water levels of urban internal rivers and external rivers was constructed, based on long short-term memory (LSTM). Fuzhou City, China was used as the research area, and the forecast performance of LSTM was analyzed. The results confirm the feasibility of LSTM in real-time rolling forecasting of water levels. The absolute errors at different times in each forecast were compared, and the various characteristics and causes of the errors in the forecast process were analyzed. The forecast performance of LSTM under different rolling intervals and different forecast periods was compared, and the recommended values are provided as a reference for the construction of local operational forecast systems.

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Section: Real-time Rolling Forecast Methods and Implementationmentioning

confidence: 99%

Short Term Real-Time Rolling Forecast of Urban River Water Levels Based on LSTM: A Case Study in Fuzhou City, China

Liu

Wang

Feng

et al. 2021

IJERPH

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“…BPESN performs the best among all the contrasts in view of the data distribution, median, and mean, indicating that the prediction ability of the BPESN model is the best. for i in mappingNum: (2) for j in enhanceNum: (3) enter data into the mapping layer and initialize it W ej , β ej and get the matrix Z; (4) initialize the ESN of the reinforced layer, collect the calculation result matrix H; (5) each ESN is pruned and optimized, and the RMSE after optimization is calculated and record the number of pruning C; (6) if RMSE < thresholdRMSE or C < thresholdPrunNum: (7) Add unpruned ESN units into the reinforced layer for further pruning; (8) else: (9) Calculation of the same mapping layer different enhancement layer ESN pruning optimized performance index; (10) end (11) end (12) the optimized parameters and predicted output are calculated. ALGORITHM 1: BPESN with pruning optimization.…”

Section: Prediction Of Air Quality Monitoring Datamentioning

confidence: 99%

“…e stock data prediction can foresee the capital flows trend [2]. e precipitation [3], water bloom [4], and typhoon intensity are also predicted for natural environment protection and disaster prevention [5]. e trend forecast of air pollutants provides strong support for the decisionmaking of relevant departments in the future [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Broad Echo State Network with Reservoir Pruning for Nonstationary Time Series Prediction

Liu

Bai

Wang

et al. 2022

Computational Intelligence and Neuroscience

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The nonstationary time series is generated in various natural and man-made systems, of which the prediction is vital for advanced control and management. The neural networks have been explored in the time series prediction, but the problem remains in modeling the data’s nonstationary and nonlinear features. Referring to the time series feature and network property, a novel network is designed with dynamic optimization of the model structure. Firstly, the echo state network (ESN) is introduced into the broad learning system (BLS). The broad echo state network (BESN) can increase the training efficiency with the incremental learning algorithm by removing the error backpropagation. Secondly, an optimization algorithm is proposed to reduce the redundant information in the training process of BESN units. The number of neurons in BESN with a fixed step size is pruned according to the contribution degree. Finally, the improved network is applied in the different datasets. The tests in the time series of natural and man-made systems prove that the proposed network performs better on the nonstationary time series prediction than the typical methods, including the ESN, BLS, and recurrent neural network.

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“…Guan et al (2018) pointed out that the percentage and annual average intensity of TCs that landed in East Asia and Southeast Asia have increased significantly between 1974 and 2013. Yuan et al (2021) developed a long and short-term memory (LSTM) TC intensity prediction model on the basis of artificial neural networks; the model can provide meaningful results for the prediction of TC intensity within 120 hours. Liou et al (2018) found that the winter cold front affects TC tracks and increases TC intensities.…”

Section: Introductionmentioning

confidence: 99%

Tropical Cyclone Damage Assessment using a Projection Pursuit Dynamic Cluster Model

Tu¹,

Chen²,

Zhao

et al. 2022

Preprint

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Using data from 62 tropical cyclones (TCs) that landed in Guangdong Province in China between 2000 and 2019, we calculated six indices—minimum central pressure, maximum wind speed, maximum rainstorm ratio, cumulative surface rainfall, cyclone track length and lifetime—and constructed a projection pursuit dynamic cluster (PPDC) model to assess TC damage risk. Although a single index may provide correct information on the intensity of certain types of damage, a comprehensive damage risk assessment cannot be obtained from individual indices alone. The PPDC model is a stable tool for TC damage risk assessment, especially in terms of economic loss, agricultural disaster area and disaster-affected population. Model validation improved the correlation of each of the indices. Output from the PPDC model for disaster-affected population and agricultural disaster-affected area also improved after model validation. We examined the limitations of the single indices using data from three TCs. Output from the PPDC model can closely reflect the intensity of the damage caused by the cyclones. Projection pursuit dynamic clustering is a new and objective method for typhoon damage risk assessment, and provides the scientific basis to support disaster prevention and mitigation.

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Typhoon Intensity Forecasting Based on LSTM Using the Rolling Forecast Method

Cited by 35 publications

References 28 publications

Short Term Real-Time Rolling Forecast of Urban River Water Levels Based on LSTM: A Case Study in Fuzhou City, China

Short Term Real-Time Rolling Forecast of Urban River Water Levels Based on LSTM: A Case Study in Fuzhou City, China

Broad Echo State Network with Reservoir Pruning for Nonstationary Time Series Prediction

Tropical Cyclone Damage Assessment using a Projection Pursuit Dynamic Cluster Model

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