T. K. Rana, Naomi Mallik, H. Saikia, S. Chakraborty
{"title":"基于回归和人工神经网络的地形降水预测","authors":"T. K. Rana, Naomi Mallik, H. Saikia, S. Chakraborty","doi":"10.1109/IEMENTech48150.2019.8981223","DOIUrl":null,"url":null,"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.","PeriodicalId":243805,"journal":{"name":"2019 3rd International Conference on Electronics, Materials Engineering & Nano-Technology (IEMENTech)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction of Orographic Rainfall using Regression Based Method and Artificial Neural Network\",\"authors\":\"T. K. Rana, Naomi Mallik, H. Saikia, S. Chakraborty\",\"doi\":\"10.1109/IEMENTech48150.2019.8981223\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":243805,\"journal\":{\"name\":\"2019 3rd International Conference on Electronics, Materials Engineering & Nano-Technology (IEMENTech)\",\"volume\":\"58 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 3rd International Conference on Electronics, Materials Engineering & Nano-Technology (IEMENTech)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IEMENTech48150.2019.8981223\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 3rd International Conference on Electronics, Materials Engineering & Nano-Technology (IEMENTech)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IEMENTech48150.2019.8981223","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Prediction of Orographic Rainfall using Regression Based Method and Artificial Neural Network
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.