Rainfall Prediction Using Gated Recurrent Unit Based on DMI and Nino3.4 Index

2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2022-07-28 DOI:10.1109/IAICT55358.2022.9887474

Huda Febrianto Nurrohman, D. C. R. Novitasari, F. Setiawan, Rochimah, Amal Taufiq, Abdulloh Hamid

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引用次数: 1

Abstract

Rainfall variability has a severe impact in Sidoarjo, Indonesia. The significant increase in extreme rainfall caused a hydrometeorological disaster and expanded the Sidoarjo mudflow. Rainfall prediction can reduce risks and anticipate hydrometeorological disasters. This study predicts rainfall based on the Dipole Mode Index (DMI) and Niño3.4 Index and several other parameters such as temperature, humidity, duration of sunshine, and wind speed. This study uses monthly time-series data to predict rainfall and compare the results of the 1D-CNN, RNN, LSTM, and GRU methods. The best prediction was made by GRU with a Mean Arctangent Absolute Percentage Error (MAAPE) value of 0.42 and R-square value of 0.79 with 32 hidden neurons, 32 batch sizes, and 0.001 learning rate. Predictions indicate that the rainfall intensity will increase from 50 mm to 200 mm per month from September 2021 to January 2022, or the rainfall intensity will increase by 30 mm per month.

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基于DMI和Nino3.4指数的门控循环单元降水预报

降雨变率对印度尼西亚Sidoarjo产生了严重影响。极端降水显著增加，造成水文气象灾害，扩大了西多阿若泥石流。降雨预报可以降低风险，预测水文气象灾害。这项研究基于偶极子模式指数(DMI)和Niño3.4指数以及其他几个参数，如温度、湿度、日照时间和风速，来预测降雨。本研究使用月时间序列数据预测降雨，并比较1D-CNN、RNN、LSTM和GRU方法的结果。在32个隐藏神经元、32个批大小、0.001学习率的情况下，GRU的平均arctan绝对百分比误差(MAAPE)为0.42,r方值为0.79，预测效果最好。预测显示，从2021年9月到2022年1月，降雨强度将从每月50毫米增加到200毫米，或降雨强度将每月增加30毫米。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)

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