Browsing by Author "Mandal, Sudipta"

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    Efficacy of Recurrent Neural Networks in rainfall forecasting
    (Department of Computer Science & Engineering, NITS, 2022) Mandal, Sudipta
    India is an agriculture dependent country. Agriculture is the primary source of income for maximum Of Indians for survival as well as to use crops as an export material. TO achieve a good agricultural profit a proper knowledge of the trend of rainfall is needed so that crop can be saved from flood and drought situation. So predicting rainfall has been a major task in research works. Rainfall prediction provides an awareness to humans and know rainfall amount beforehand so that they can take certain precautions to save crops from rainfall. Many approaches has come into subsistence in order to predict rainfall. Statistical models, Numerical weather prediction models were used initially for prediction. The statistical techniques and numerical methods for rainfall were unable to show well performance for long term rainfall forecasting due to the dynamicity of rainfall. Due to the non-linear and dynamic behaviour, advanced computer modelling is required to predict rainfall accurately, in lieu Of unreliable statistical techniques. In recent days, Machine Learning (ML) such as Artificial Neural Network (ANN) and Deep Learning (DL) enables self-learning to design a time series, data-driven model on rainfall dataset. This work proposes a ML model Short term Data Driven Rainfall Forecasting Model (SDDRF), which uses Long Short Term Memory (LSTM)-based rainfall prediction model to forecast weather and rainfall in the region Of Shillong , a city in Meghalaya. This thesis considers rainfall prediction using LSTM and is compared with three other neural network models. The study intends to the efficacy of these architectures in rainfall prediction, and determines the best among all, for future application and analysis.