Optimization Enabled Neural Network for the Rainfall Prediction in India

被引：0

作者：

Mukkala, Ananda R. Kumar ^{[1
]}

Reddy, S. Sai Satyanarayana ^{[1
]}

Raju, P. Praveen ^{[2
]}

Mounica ^{[2
]}

Oguri, Chiranjeevi ^{[2
]}

Bhukya, Srinivasu ^{[2
]}

机构：

[1] Sreyas Inst Engn & Technol, Dept CSE, Hyderabad 500068, India

[2] Sreyas Inst Engn & Technol, Dept ECE, Hyderabad 500068, India

来源：

ADVANCES IN COMPUTING AND DATA SCIENCES (ICACDS 2022), PT II | 2022年 / 1614卷

关键词：

NARX neural network; Rainfall prediction; Spotted hyena; Indian rainfall data; RMSE; MONSOON RAINFALL; SERIES;

D O I：

10.1007/978-3-031-12641-3_2

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Rainfall prediction plays a major role in ensuring the livelihood of many people especially, for the farmers. Heavy and irregular flow of rainfall can cause flood, landslide and much other destruction. To prevent this, rainfall should be predicted in a periodic manner. As a contribution, the proposed Spotted Hyena based nonlinear autoregressive model (SH-NARX) prediction model effectively predicts the rainfall in a yearly, monthly and quarterly manner using the Indian rainfall dataset. The data is collected and trained using the NARX neural network, which is a non linear autoregressive network that is optimized using the spotted hyena optimization for rainfall prediction. The performance of the prediction model is analyzed based on RMSE and PRD that are minimal, highlighting the higher accuracy rates.

引用

页码：12 / 23

页数：12

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