Prediction of Weather Parameters by Harmonic Analysis and Artificial Neural Networks
Author | : Manjusha Kulshrestha |
Publisher | : Booktango |
Total Pages | : 334 |
Release | : 2013-10-29 |
ISBN-10 | : 9781468940244 |
ISBN-13 | : 1468940244 |
Rating | : 4/5 (244 Downloads) |
Download or read book Prediction of Weather Parameters by Harmonic Analysis and Artificial Neural Networks written by Manjusha Kulshrestha and published by Booktango. This book was released on 2013-10-29 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, addresses the problem of prediction of weather Parameters like: i) Prediction of annual rainfall. ii) Prediction of return period of occurred highest one day maximum rainfall. iii) Prediction of weekly rainfall probabilities. iv) Prediction of Hourly air temperatures for one day. v) Prediction of soil temperatures at 5- 20 cm. depths. Double variable Fourier Series approach is first time applied to the problems of prediction in Meteorology. The main objective of this book is to predict the weather elements especially, annual rainfall and weekly rainfall probabilities, soil temperature (of Anand station of Gujarat, India) by using appropriate computational methods.The present book consists of six chapters. Chapter 2 gives necessary preliminaries on Weather, Artificial Neural Networks, the required Mathematical Analysis and Fourier series etc.A brief description of the chapters 3 to 6 is as follow: Chapter 3: Prediction of Annual Rainfall. Chapter 3 deals with the prediction of Annual Rainfall by using the following two methods: i) Artificial Neural Networks, and Double Variable Fourier Series. Chapter 4: Prediction of Rainfall Probabilities. In this chapter, we investigate following two problems: i) Return Period Analysis (RTPA) by Gumbel (13) and Fisher Tippett Type-II Distribution and ANN ii) Rainfall Probability Analysis (RPA) by Gamma Distribution ii) Model (GDM) and ANN. Chapter 5:- Prediction of Air temperatures. Chapter five deals with prediction of hourly air temperatures. The following two different methods are employed. i) Parton’s Mathematical Model. ii) Artificial Neural Network. Chapter 6.:- Prediction of weekly Soil temperatures. Chapter 6 deals with prediction of weekly soil temperatures at different depths by using following two different methods: i) Harmonic Analysis (HA) ii) Artificial Neural Networks (ANN).