Hasan, M., Tsegaye, T., Shi, X., Schaefer G., & Taylor, G. (2008). Model for Predicting Rainfall by Fuzzy Set Theory Using USDA Scan Data. Agricultural Water Management, 95, 1350-1360.
https://doi.org/10.1016/j.agwat.2008.07.015

has been cited by the following article:

• TITLE: Improvement of Rainfall Prediction Model by Using Fuzzy Logic

  AUTHORS: Md. Anisur Rahman

  KEYWORDS: Fuzzy Logic, Membership Function, Temperature, Wind Speed, Predicted Rainfall

  JOURNAL NAME: American Journal of Climate Change, Vol.9 No.4, December 7, 2020

  ABSTRACT: This paper presents the improvement of the fuzzy inference model for predicting rainfall. Fuzzy rule based system is used in this study to predict rainfall. Fuzzy inference is the actual procedure of mapping with a given set of input and output through a set of fuzzy systems. Two operations were performed on the fuzzy logic model; the fuzzification operation and defuzzification operation. This study is obtaining two input variables and one output variable. The input variables are temperature and wind speed at a particular time and output variable is the amount of predictable rainfall. Temperature, wind speed and rainfall have to construct eight equations for different categories and which are shows the diagram of the graph. Fuzzy levels and membership functions obtained after minimum composition of inference part of the fuzzifications done for temperature and wind speed are considered as they represent the environmental condition enhance a rainfall occurrence which is effect on agricultural production.