Genetic Programming for storm surge forecasting

Storm Surge Level Prediction Based on Improved NARX Neural Network

“…From the perspective of practical application, the key time for hurricane prevention is 1 to 6 hours before hurricane landfall, and all preparations should be completed within 12 hours before hurricane landfall, so the advance time step a of the simulation is initially set to 12 in this paper. In this way, the preparation for storm surge prediction simulation is basically completed, and the prediction output can be obtained by bringing each station data set into each model, as shown in the gure (9)(10)(11).…”

“…In recent years, with the development of arti cial intelligence, more and more researchers have used data-driven models that introduce the idea of arti cial intelligence in predicting storm surges [8][9][10][11], and the prediction is accomplished by mapping input and output variables to obtain information from the collected data. Various storm parameters such as sea level height, wind, barometric pressure, and other tropical cyclone characteristics are usually used in this type of prediction method to predict storm surge levels in the future period without directly studying the natural rules that govern the storm surge mechanism.…”

“…They do not allow longterm prediction of storm surge activity, the computational process is very complicated and expensive, and the calculation costs is quite high, etc. By reducing the delay and streamlining the process of storm surge prediction, and by quickly and accurately predicting some critical data for each storm surge event, the authorities can be directed to prepare for the disaster and further reduce the damage caused by the disaster.In recent years, with the development of arti cial intelligence, more and more researchers have used data-driven models that introduce the idea of arti cial intelligence in predicting storm surges [8][9][10][11], and the prediction is accomplished by mapping input and output variables to obtain information from the collected data. Various storm parameters such as sea level height, wind, barometric pressure, and other tropical cyclone characteristics are usually used in this type of prediction method to predict storm surge levels in the future period without directly studying the natural rules that govern the storm surge mechanism.…”

“…In this paper, the prediction results are set to a total of 720 data for 30 days (Fig. [9][10][11], and a longer time span is used to analyze whether the model can accurately predict when the storm surge extremes occur and reduce the predicted values back to the normal range in time. For stations S1-3 in Texas, this paper researches the impact of Tropical Storm BETA on the region, thus selecting the measured storm surge data from September 1, 2020 GMT 0000 to September 30, 2020 GMT 2300 as the prediction output for the simulation prediction test.…”

A Review of Application of Machine Learning in Storm Surge Problems

“…The lack of large and well-structured datasets suitable for implementing ML algorithms poses a significant obstacle [31], and the performance of the models used in previous studies could be further boosted by training on more high-fidelity and massive datasets. According to Hien et al [73], frequently used input features can be categorized into three types: typhoon characteristics, local hydrodynamic parameters and local meteorological conditions.…”