In artificial intelligence, dividing the response variable into more than two classes and determining the original class using AI techniques is called multiclass classification.
Published in Chapter:
Multivariate Time Series Forecasting of Rainfall Using Machine Learning
Shilpa Hudnurkar (Symbiosis Institute of Technology, Symbiosis International University (Deemed), India), Vidur Sood (Symbiosis Institute of Technology, Symbiosis International University (Deemed), India), Vedansh Mishra (Symbiosis Institute of Technology, Symbiosis International University (Deemed), India), Manobhav Mehta (Symbiosis Institute of Technology, Symbiosis International University (Deemed), India), Akash Upadhyay (Symbiosis Institute of Technology, Symbiosis International University (Deemed), India), Shilpa Gite (Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International University (Deemed), India), and Neela Rayavarapu (Symbiosis International University (Deemed), India)
Copyright: © 2022
|Pages: 20
DOI: 10.4018/978-1-6684-3981-4.ch007
Abstract
Predicting rainfall is essential for assessing the impact of climatic and hydrological changes over a specific region, predicting natural disasters or day-to-day life. It is one of the most prominent, complex, and essential weather forecasting and meteorology tasks. In this chapter, long short-term memory network (LSTM), artificial neural network (ANN), and 1-dimensional convolutional neural network LSTM (1D CNN-LSTM) models are explored for predicting rainfall at multiple lead times. The daily weather parameter data of over 15 years is collected for a station in Maharashtra. Rainfall data is classified into three classes: no-rain, light rain, and moderate-to-heavy rain. The principal component analysis (PCA) helped to reduce the input feature dimension. The performance of all the networks are compared in terms of accuracy and F1 score. It is observed that LSTM predicts rainfall with consistent accuracy of 82% for 1 to 6 days lead time while the performance of 1D CNN-LSTM and ANN are comparable to LSTM.