Heat Wave Predication Correlate Data to Predict Drought

Abstract

Climate change increases the frequency and intensity of heat waves and drought both cause significant human and material losses every time. This work aims at assessing the forecast quality in predicting the evolution of drought and heat stress by using user-relevant models such as S2S and GBDT to compare the model and predict the best accuracy and then collect the data from heat wave if heat wave found in the area, then use relevant algorithm and model to predict drought. I train Machine Learning models to predict the occurrence of heat waves over any 1° by 1° geographical co-ordinate over India a month in advance, using monthly or sometimes weekly, maximum temperature data from the 6 months preceding it and correlating given data and predict the drought and to notify people to allow them to take precautions to protect their lives and lifestyle. It is found that the developed criterion is functional in providing an outlook on the impending extreme temperatures with sufficient humidity to lead time. understanding and predicting the extreme temperatures and humidity leading to heat waves and drought are of the greatest importance. Key Word: Heat Wave, Drought, S2S, RNN, India, Temperature, Humidity, IMD data.