Abstract:
Rainfall is a key factor in determining agriculture demands such as paddy production and paddy
price. This research focuses implementation of data mining techniques for analyzing customer
demands in paddy price based on the rainfall variation under the long term manner. The use
of analyzing rainfall and price of paddy for ten years from 2006 to 2015 that predict customer
demands makes to check the price of paddy along with changes in rainfall. The analysis of
past ten years’ meteorological data comprising year, month and rainfall is important to predict
future state of rainfall accurately. It utilizes past weather data records on the premise that previous weather will be a repeat of the future. At the beginning K- means clustering algorithm
was used to group the homogeneous paddy price data, then most suitable cluster was selected
by correlation analysis. By using Random tree, rules are retrieved related to month, price and
rainfall. Finally long short-term memory Neural Network (LSTM) was used to forecasting rainfall and paddy price. End of the study customer’s demands in price of paddy were predicted by
forecasted result. Correlation between rainfall and paddy price in cluster 0 is –0.603511 and
Mean absolute error is 4.1 degrees. It is higher than previous data set. In future can be analyzed
temperature humidity, and etc for predict sales of paddy at same way. LSTM predicting accuracy is increased along with huge amount of data. From the result, day vice data will give more
accuracy compare with monthly data and adding more attributes for prediction will provide an
exact result.
