ARMA BASED CROP YIELD PREDICTION USING TEMPERATURE AND RAINFALL PARAMETERS WITH GROUND WATER LEVEL CLASSIFICATION
Keywords:Crop Yield Prediction, ARMA Model, KNN Classification, Neural Network. .
Now a days, wireless telecommunication networks are promising alternative for rainfall measuring instruments that complement previous monitoring devices. Because of big dataset of the rainfall and therefore the telecommunication networks data, empirical computational methods mean less adequate of actual data. So, deep learning models are proposed for the analysis of massive data and provides more accurate presentation of real measurements. This project performrainfall monitoring results from experimental measurements. The most aim of this study is supply a technique for rainfall data classification supported neural network methods supported historical rainfall data production data. Classification based on the previous years of rainfall can help farmers take necessary steps to live crop production within the coming season. Understanding and assessing future crop production can help ensure food security and reduce impacts of global climate change. During this work, ARMA (Auto Regressive Moving Average) method is used for proposed work. Past 10 years of information(data) set is taken for rainfall and ground water level for our country. The proposed work classifies the bottom water level data set records using ARIMA model to estimate the model for future test record data sets. The new model will helpf for analyzing ground water levels in past and then on find the long run levels.