Abstract
India is an agricultural country, and rainfall is the main source of irrigation for agriculture. Prediction of rainfall is very crucial for farmers to make decisions. In this research paper, the prediction model has been developed through deep learning using historical data of 10 years of rainfall. A deep learning approach used Keras API with an artificial neural network technique to predict the daily rainfall. The prediction model has been assessed by four-loss function, i.e., MSE, MAE, Hinge, and Binary Cross-Entropy.
Highlights
Rainfall: - In India, more than 65% of the population resides in rural areas, out of which most of the people are farmers and mainly rely on agriculture
Deep Learning: - In artificial intelligence (AI), Deep Learning (DL) is a subset of machine learning based on Neural Network (NN) having a capability of learning from data
Different types of loss functions are available in Keras, such as Mean Squared Error (MSE), Binary Cross-Entropy, Mean Absolute Error (MAE), Hinge, Mean Squared Logarithmic Error (MSLE) and many more [18]
Summary
Rainfall: - In India, more than 65% of the population resides in rural areas, out of which most of the people are farmers and mainly rely on agriculture. Artificial Neural Network (ANN) is capable of predicting real-world problems and resulting in nearly the perfect solution for the given problems with the help of proper training and testing mechanism [9]. DL has been applied to different fields, including forecasting systems, computer vision, automatic speech recognition, many more. It has achieved success in improving the results for the given tasks [16]. The Keras Framework: - Keras is an excellent high-level open-source neural network API framework, written in Python. It can train and execute deep learning models using Theano, Tensor Flow, and CNTK backend [17]. Different types of loss functions are available in Keras, such as Mean Squared Error (MSE), Binary Cross-Entropy, Mean Absolute Error (MAE), Hinge, Mean Squared Logarithmic Error (MSLE) and many more [18]
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