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

机译：神经网络模型与水动力模型对台风风速预测的比较研究

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class="kwd-title">Methods name: Analyzing two different methods for predicting typhoon intensity to represent some outcomes that should be considered in future studies class="kwd-title">Keywords: Prediction, Wind speed, Typhoon, South China Sea, ANFIS, WRF class="head no_bottom_margin" id="abs0010title">AbstractThere 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. class="first-line-outdent" id="lis0005">
  • • 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.
    • 机译：<！-fig ft0-> <！-fig @ position =“ anchor” mode =文章f4-> <！-fig mode =“ anchred” f5-> <！-fig / graphic | fig / alternatives / graphic mode =“ anchored” m1-> class =“ kwd-title”>方法名称：分析两种不同的预测台风强度的方法以代表未来研究中应考虑的某些结果< strong class =“ kwd-title”>关键字：预测，风速，台风，南海，ANFIS，WRF class =“ head no_bottom_margin” id =“ abs0010title”>摘要有许多模型可以预测世界各地的自然现象，但仍然很难准确预测事件。许多从事热带气旋预报工作的科学家，建模专业，学生和研究人员，但是在编译和配置模型时会遇到许多错误。尽管天气预报的准确性不断提高，但是所有天气预报中都存在不确定性因素。本文回顾了我以前的论文中使用的两种预测南海台风风速的方法，一个动力学模型，天气研究和预报（WRF）和一个自适应神经模糊推理系统（ANFIS）模型。使用均方根误差（RMSE）和相关系数（CC）的统计参数计算模型的性能，并表示两种模型的优缺点。关于统计参数值，由于CC较高且RMSE较低，因此ANFIS模型与WRF模型相比显示出更高的台风强度预测精度。方法的发展代表了几种先进的技术，它们的优缺点尚未得到充分记录。实际上，定性评估指出了这些方法可能相互补充的几种方式。该论文建议科学家们应该改进模型的概念。 class =“ first-line-outdent” id =“ lis0005”> <！-list-behavior =简单前缀-word = mark-type = none max -label-size = 9->
  • •研究两种不同的方法及其在预测台风强度方面的性能。
  • •分别代表这两种方法的优缺点
  • •为将来的研究提出一些解决方案。

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