Next Article in Journal
Hypothesis Tests for Bernoulli Experiments: Ordering the Sample Space by Bayes Factors and Using Adaptive Significance Levels for Decisions
Next Article in Special Issue
Information Entropy Suggests Stronger Nonlinear Associations between Hydro-Meteorological Variables and ENSO
Previous Article in Journal
Molecular Conformational Manifolds between Gas-Liquid Interface and Multiphasic
Previous Article in Special Issue
Testing the Beta-Lognormal Model in Amazonian Rainfall Fields Using the Generalized Space q-Entropy
Open AccessArticle

Combined Forecasting of Rainfall Based on Fuzzy Clustering and Cross Entropy

Beijing Key Laboratory of Energy Safety and Clean Utilization, North China Electric Power University, Renewable Energy Institute, Beijing 102206, China
State Key Laboratory of New Energy Power System, North China Electric Power University, 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
University of Chinese Academy of Sciences, Beijing 100049, China
Author to whom correspondence should be addressed.
Entropy 2017, 19(12), 694;
Received: 31 August 2017 / Revised: 5 December 2017 / Accepted: 14 December 2017 / Published: 19 December 2017
(This article belongs to the Special Issue Entropy Applications in Environmental and Water Engineering)
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. View Full-Text
Keywords: rainfall forecast; cross entropy; ant colony fuzzy clustering; combined forecast rainfall forecast; cross entropy; ant colony fuzzy clustering; combined forecast
Show Figures

Figure 1

MDPI and ACS Style

Men, B.; Long, R.; Li, Y.; Liu, H.; Tian, W.; Wu, Z. Combined Forecasting of Rainfall Based on Fuzzy Clustering and Cross Entropy. Entropy 2017, 19, 694.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop