Time series forecasting of precipitation patterns over Lucknow region using LSTM

Rainfall forecasting has assumed an important role in recent times due to uncertainities emanating from climate change as a result of environmental phenomenons like El Nino, La Nina, global warming, etc. Agriculture in India is still pretty much dependent upon rains, more so in a state like Uttar Pradesh. So it is imperative that forecasting systems are developed that can analyse the previous trends of rainfall and predict accordingly the future values of rain. The existing statistical models that forecast rain are too complex and also not cost effective. Hence we take the approach of a machine learning model, or to further specify, a deep learning model called Long Short Term Memory (LSTM) to try and predict with some accuracy. This study examines the precipitation patterns over the Lucknow region for a period of 20 years, with the dates ranging from 1st January, 2000 to 31st December, 2019. The accuracy of the LSTM model developed is judged on the basis of Mean Absolute Percentage Error (MAPE), R-square (R2) and Root Mean Squared Error (RMSE) values.