Forecasting monsoon precipitation using artificial neural networks

被引：16

作者：

Wu, XD ^{[1
]}

Cao, HX

Flitman, A

Wei, FY

Feng, GL

机构：

[1] Monash Univ, Fac Informat Technol, Sch Business Syst, Clayton, Vic 3168, Australia

[2] Chinese Acad Meteorol Sci, Beijing 100081, Peoples R China

来源：

ADVANCES IN ATMOSPHERIC SCIENCES | 2001年 / 18卷 / 05期

关键词：

forecasting; monsoon precipitation; artificial intelligent technique; artificial neural networks;

D O I：

10.1007/BF03403515

中图分类号：

P4 [大气科学（气象学）];

学科分类号：

0706 ; 070601 ;

摘要：

This paper explores the application of Artificial Intelligent (AI) techniques for climate forecast. It presents a study on modelling the monsoon precipitation forecast by means of Artificial Neural Networks (ANNs). Using the historical data of the total amount of summer rainfall over the Delta Area of Yangtze River in China, three ANNs models have been developed to forecast the monsoon precipitation in the corresponding area one year, five-year, and ten-year forward respectively. Performances of the models have been validated using a 'new' data set that has not been exposed to the models during the processes of model development and test. The experiment results are promising, indicating that the proposed ANNs models have good quality in terms of the accuracy, stability and generalisation ability.

引用

页码：950 / 958

页数：9

共 50 条

[21] Forecasting monthly maximum 5-day precipitation using artificial neural networks with initial lags
Junaida, Sulaiman
Herdianti, Darwis
Hirose, Hideo
2013 SIXTH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID), VOL 2, 2013, : 3 - 7
[22] Artificial neural network models for forecasting monthly precipitation in Jordan
Aksoy, Hafzullah
Dahamsheh, Ahmad
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, 2009, 23 (07) : 917 - 931
[23] Artificial neural network models for forecasting monthly precipitation in Jordan
Hafzullah Aksoy
Ahmad Dahamsheh
Stochastic Environmental Research and Risk Assessment, 2009, 23 : 917 - 931
[24] Air temperature forecasting using artificial neural networks with delayed exogenous input
Jallal, Mohammed Ali
Chabaa, Samira
El Yassini, Abdessalam
Zeroual, Abdelouhab
Ibnyaich, Saida
2019 INTERNATIONAL CONFERENCE ON WIRELESS TECHNOLOGIES, EMBEDDED AND INTELLIGENT SYSTEMS (WITS), 2019,
[25] Forecasting production of clean energy using cognitive mapping and artificial neural networks
Wang, Miao
Peiyu, Ren
Information Technology Journal, 2013, 12 (20) : 5791 - 5798
[26] Most Influential Variables for Solar Radiation Forecasting Using Artificial Neural Networks
Alluhaidah, B. M.
Shehadeh, S. H.
El-Hawary, M. E.
2014 IEEE ELECTRICAL POWER AND ENERGY CONFERENCE (EPEC), 2014, : 71 - 75
[27] Solar Production Forecasting Based on Irradiance Forecasting Using Artificial Neural Networks
Ioakimidis, Christos S.
Lopez, Sergio
Genikomsakis, Konstantinos N.
Rycerski, Pawel
Simic, Dragan
39TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY (IECON 2013), 2013, : 8121 - 8126
[28] Mobile Network Traffic Forecasting Using Artificial Neural Networks
Kirmaz, Anil
Michalopoulos, Diomidis S.
Balan, Irina
Gerstacker, Wolfgang
2020 IEEE 28TH INTERNATIONAL SYMPOSIUM ON MODELING, ANALYSIS, AND SIMULATION OF COMPUTER AND TELECOMMUNICATION SYSTEMS (MASCOTS 2020), 2020, : 70 - +
[29] Forecasting Indian Stock Market Using Artificial Neural Networks
Kale, Ashutosh
Khanvilkar, Omkaar
Jivani, Hardik
Kumkar, Prathamesh
Madan, Ishan
Sarode, Tanuja
2018 FOURTH INTERNATIONAL CONFERENCE ON COMPUTING COMMUNICATION CONTROL AND AUTOMATION (ICCUBEA), 2018,
[30] FORECASTING THE SORPTION OF PHOSPHATES IN SOIL WITH ARTIFICIAL NEURAL NETWORKS
Diaconu, Ecaterina
Orbulet, Oanamari Daniela
Miron, Alexandra Raluca
Modrogan, Cristina
UNIVERSITY POLITEHNICA OF BUCHAREST SCIENTIFIC BULLETIN SERIES B-CHEMISTRY AND MATERIALS SCIENCE, 2010, 72 (03): : 175 - 182

← 1 2 3 4 5 →