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
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
