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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

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  • Chelang A Arslan
  • Enas Kayis

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

Suggested Citation

  • Chelang A Arslan & Enas Kayis, 2018. "Weather Forecasting Models Using Neural Networks and Adaptive Neuro Fuzzy Inference for Two Case Studies at Huoston, Texas and Dallas States," Journal of Asian Scientific Research, Asian Economic and Social Society, vol. 8(1), pages 1-12.
    • Handle: RePEc:asi:joasrj:v:8:y:2018:i:1:p:1-12:id:3865
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