Prediction of Orographic Rainfall using Regression Based Method and Artificial Neural Network

T. K. Rana, Naomi Mallik, H. Saikia, S. Chakraborty
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Abstract

The article attempts to compare the performance of regression based model and artificial neural network model for the prediction of stochastic – deterministic phenomena like orographic rain in North East Indian hills and valleys using historical thirty eight years data of rainfall over the three hill stations, Majhitar, Shillong and Silchar. Considering the randomness, nonstationary within the time series the suitable model for prediction of rainfall has been carried out. The performance of the prediction model is also calculated in terms of deviation from actual data. Result shows that for long term prediction of rainfall, artificial neural network (ANN) model performs better compared to autoregressive integrated moving average model for the prediction of orographic rainfall of North eastern India.
基于回归和人工神经网络的地形降水预测
本文利用Majhitar、Shillong和Silchar三个山地站38年的历史降水数据,比较了基于回归模型和人工神经网络模型对印度东北部丘陵和山谷地形雨等随机确定性现象的预测效果。考虑到时间序列的随机性和非平稳性,建立了适合于降雨预测的模型。预测模型的性能也根据与实际数据的偏差进行了计算。结果表明,人工神经网络(ANN)模型对印度东北部地形降水的长期预报效果优于自回归综合移动平均模型。
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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