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Improvement of Rainfall Prediction Model by Using Fuzzy Logic

机译:采用模糊逻辑改进降雨预测模型

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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.
机译:本文提出了预测降雨的模糊推理模型的改进。基于模糊的规则系统用于本研究预测降雨。模糊推理是通过一组模糊系统使用给定的输入和输出的实际过程进行映射。在模糊逻辑模型上进行了两次操作;模糊操作和排放操作。本研究正在获取两个输入变量和一个输出变量。输入变量是特定时间的温度和风速,输出变量是可预测降雨量的量。温度,风速和降雨量必须为不同类别构建八个方程,并且显示图表的图。在温度和风速所做的模型的最小推理部分的最小构成后获得的模糊水平和隶属函数被认为是它们代表环境条件,增强了对农业生产影响的降雨量。

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