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Weather Forecasting Models Using Neural Networks and Adaptive Neuro Fuzzy Inference for Two Case Studies at Huoston, Texas and Dallas States

Chelang A .Arslan and Enas Kayis
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Chelang A .Arslan: Assistant professor ,College of Engineering /Kirkuk University-Iraq
Enas Kayis: Civil Engineer , College of Engineering /Kirkuk University-Iraq

Journal of Asian Scientific Research, 2018, vol. 8, issue 1, 1-12

Abstract: Forecasting of precipitation is one of the most challenging operational tasks done by hydrologists. This operation can be described as most complicated procedure that includes multiple specialized fields of expertise. In this research a comprehensive study was employed to forecast daily precipitation depending on different weather parameters. This was done by using two different methods which are back propagation neural networks BPNN and adaptive neuro inference system ANFIS. Two case studies were selected for this operation which are Huoston, Texas and Dallas, Texas. The high performance of the applied models in forecasting the daily precipitation was concluded especially by using auxiliary weather data with the lagged day precipitation values since the BPNN and ANFIS were able to learn from continuous input data

Keywords: Forecasting; ANN; PBNN; ANFIS.; Precipitation. (search for similar items in EconPapers)
Date: 2018
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Handle: RePEc:asi:joasrj:2018:p:1-12