The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Neural Models for Rainfall Forecasting
|
|
Author(s): A. Moreno (Universidad de Valencia, Spain), E. Soria (Universidad de Valencia, Spain), J. García (Universidad de Valencia, Spain), J. D. Martín (Universidad de Valencia, Spain)and R. Magdalena (Universidad de Valencia, Spain)
Copyright: 2010
Pages: 17
Source title:
Soft Computing Methods for Practical Environment Solutions: Techniques and Studies
Source Author(s)/Editor(s): Marcos Gestal Pose (University of A Coruna, Spain)and Daniel Rivero Cebrián (University of A Coruna, Spain)
DOI: 10.4018/978-1-61520-893-7.ch021
Purchase
|
Abstract
This chapter is focused on obtaining an optimal forecast of one month lagged rainfall in Spain. It is assessed by analyzing 22 years of both satellite observations of vegetation activity (e.g. NDVI) and climatic data (precipitation, temperature). The specific influence of non-spatial climatic indices such as NAO and SOI is also addressed. The approaches considered for rainfall forecasting include classical Auto-Regressive Moving-Average with Exogenous Inputs (ARMAX) models and Artificial Neural Networks (ANN), the so-called Multilayer Perceptron (MLP), in particular. The use of neural models is proven to be an adequate mathematical prediction tool in this problem due the non-linearity of the problem. These models enable us to predict, with one month foresight, the general rainfall dynamics, with average errors of 44 mm (RMSE) in a test series of 4 years with a rainfall standard deviation equal to 73 mm. Also, the sensitivity analysis in the neural network models reveals that observations in the status of the vegetation cover in previous months have a predictive power greater than other considered variables. Linear models yield average results of 55 mm (RMSE) although they need a large number of error terms (12) to obtain acceptable models. Nevertheless, they provide means for assessing the seasonal influence of the precipitation regime with the aid of linear dummy regression parameters, thereby offering an immediate interpretation (e.g. coherent maps) of the causality between vegetation cover and rainfall.
Related Content
|
Bhargav Naidu Matcha, Sivakumar Sivanesan, K. C. Ng, Se Yong Eh Noum, Aman Sharma.
© 2023.
60 pages.
|
|
Lavanya Sendhilvel, Kush Diwakar Desai, Simran Adake, Rachit Bisaria, Hemang Ghanshyambhai Vekariya.
© 2023.
15 pages.
|
|
Jayanthi Ganapathy, Purushothaman R., Ramya M., Joselyn Diana C..
© 2023.
14 pages.
|
|
Prince Rajak, Anjali Sagar Jangde, Govind P. Gupta.
© 2023.
14 pages.
|
|
Mustafa Eren Akpınar.
© 2023.
9 pages.
|
|
Sreekantha Desai Karanam, Krithin M., R. V. Kulkarni.
© 2023.
34 pages.
|
|
Omprakash Nayak, Tejaswini Pallapothala, Govind P. Gupta.
© 2023.
19 pages.
|
|
|