Forecasting Temperature and Rainfall for Crop Cultivation with Classification of Ground Water Level

These days, wireless telecommunication networks offer a viable replacement for traditional monitoring systems and rainfall measuring gear. Empirical computing techniques are less accurate representations of real data because to the vast amounts of data connected to rainfall and telecommunication networks. In order to analyse massive data and provide real measurements more accurately, deep learning models are suggested. This project examined the outcomes of experimental measurements used to monitor rainfall. This study's main objective is to demonstrate a neural network-based classification method for rainfall data based on historical production data. Farmers can take the appropriate actions to gauge crop yield in the upcoming season with the aid of classification based on the rainfall from prior years. Understanding and evaluating crop output in the future can assist to protect food security and lessen the effects of climate change. The ARMA (Auto Regressive Moving} {Average) approach is employed in this work for the proposed work. For our country, data from the previous ten years are used to calculate rainfall and ground water levels. In the proposed study, records in the ground water level data set are classified by an ARIMA model, and what the model predicts for additional test record data sets. The brand-new design help in researching past ground water levels and forecasting levels in the upcoming.