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

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
References: Add references at CitEc
Citations:

Downloads: (external link)
https://archive.aessweb.com/index.php/5003/article/view/3865/6079 (application/pdf)
https://archive.aessweb.com/index.php/5003/article/view/3865/6423 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:asi:joasrj:v:8:y:2018:i:1:p:1-12:id:3865

Access Statistics for this article

More articles in Journal of Asian Scientific Research from Asian Economic and Social Society
Bibliographic data for series maintained by Robert Allen ().