Comparative study on typhoon's wind speed prediction by a neural networks model and a hydrodynamical model

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There are many models to predict natural phenomena around the world, but it is still difficult to accurately forecast the events. Many scientists, modeling professions, students, and researchers working on the tropical cyclones prediction, but they are encountered to many errors during compiling and configuring the models. Despite the increasing accuracy of weather forecasts, there is an element of uncertainty in all predictions. This paper reviews two methods used in my previous papers for predicting typhoon wind speed in the South China Sea, a dynamical model, Weather Research and Forecasting (WRF), and an Adaptive Neuro-Fuzzy Inference System (ANFIS) model. The performances of the models are calculated using statistical parameters of the root mean square error (RMSE) and Correlation Coefficient (CC), and the advantages and disadvantages of both models are represented. Regarding the statistical parameters values, the ANFIS model in comparison with the WRF model showed higher accuracy for typhoon intensity prediction because of higher CC and lower RMSE. The development of methods has represented several advanced techniques that their strengths and weaknesses have not been well-documented. In fact, a qualitative assessment and points to several ways in which the methods may be able to complement each other. The paper suggests that the scientists should improve the concepts of the models. • Investigating two different methods and their performance in predicting typhoon intensity. • Representing the strengths and weaknesses of both models. • Suggesting some solutions for future researches.




Haghroosta, T. (2019). Comparative study on typhoon’s wind speed prediction by a neural networks model and a hydrodynamical model. MethodsX, 6, 633–640.

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