Feature Selection and Optimization Based Deep Learning for Rainfall Prediction

DOI

10.2991/978-94-6463-110-4_17How to use a DOI?

Keywords

Rainfall prediction; time-series data; Deep Learning; Technical indicators; optimization

Abstract

Rainfall hugely impacts every aspect of human life, such as transportation, agriculture, water management, and so on. It also is a grave cause of several natural calamities, like landslides, floods, and drought, which pose a serious threat to the well-being of individuals. These concerns have necessitated the need for devising an effective technique to predict rainfall, which enables the undertaking of effective preventive measures. Several works have focused on developing efficient rainfall forecasting techniques; however, the uncertain nature of rainfall and the lack of rainfall data limit their effectiveness. This paper proposes an efficient rainfall prediction strategy using an optimized Deep Learning approach. Here, prediction is carried out using a Deep Long Short Term Memory network based on the time series data of the rainfall. Further, the prediction efficiency is enhanced by the utilization of the Circle Inspired Optimization Algorithm for the weight optimization of the Deep Long Short Term Memory. Experimental results show that the devised Circle Inspired Optimization Algorithm-Deep Long Short Term Memory reveals enhanced performance by attaining a minimal value of Relative Absolute Error at 0.023 Mean Square Error of 0.151, and Root Mean Square Error of 0.389.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - P. Vijaya
AU  - Satish Chander
AU  - Praba Palanisamy
AU  - Alycia Sebastian
AU  - Joseph Mani
PY  - 2023
DA  - 2023/01/30
TI  - Feature Selection and Optimization Based Deep Learning for Rainfall Prediction
BT  - Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022)
PB  - Atlantis Press
SP  - 235
EP  - 249
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-110-4_17
DO  - 10.2991/978-94-6463-110-4_17
ID  - Vijaya2023
ER  -