Combined Forecasting of Rainfall Based on Fuzzy Clustering and Cross Entropy

Entropy

Zhijian Wu¹  Baohui Men¹  Huanlong Liu¹  Yangsong Li¹  Wei Tian²  Rishang Long³ 

[1] Beijing Key Laboratory of Energy Safety and Clean Utilization, North China Electric Power University, Renewable Energy Institute, Beijing 102206, China;Key Laboratory of Water Cycle and Related Land Surface Process, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;State Key Laboratory of New Energy Power System, North China Electric Power University, Beijing 102206, China;

关键词: rainfall forecast;    cross entropy;    ant colony fuzzy clustering;    combined forecast;   

DOI  :  10.3390/e19120694

来源: DOAJ

【 摘 要 】

Rainfall is an essential index to measure drought, and it is dependent upon various parameters including geographical environment, air temperature and pressure. The nonlinear nature of climatic variables leads to problems such as poor accuracy and instability in traditional forecasting methods. In this paper, the combined forecasting method based on data mining technology and cross entropy is proposed to forecast the rainfall with full consideration of the time-effectiveness of historical data. In view of the flaws of the fuzzy clustering method which is easy to fall into local optimal solution and low speed of operation, the ant colony algorithm is adopted to overcome these shortcomings and, as a result, refine the model. The method for determining weights is also improved by using the cross entropy. Besides, the forecast is conducted by analyzing the weighted average rainfall based on Thiessen polygon in the Beijing–Tianjin–Hebei region. Since the predictive errors are calculated, the results show that improved ant colony fuzzy clustering can effectively select historical data and enhance the accuracy of prediction so that the damage caused by extreme weather events like droughts and floods can be greatly lessened and even kept at bay.

【 授权许可】

Unknown