Abstract
The annual estimate of the availability of the amount of water for the agricultural sector has become a lifetime in places where rainfall is scarce, as is the case of northwestern Argentina. This work proposes to model and simulate monthly rainfall time series from one geographical location of Catamarca, Valle El Viejo Portezuelo. In this sense, the time series prediction is mathematical and computational modelling series provided by monthly cumulative rainfall, which has stochastic output approximated by neural networks Bayesian approach. We propose to use an algorithm based on artificial neural networks (ANNs) using the Bayesian inference. The result of the prediction consists of 20% of the provided data consisting of 2000 to 2010. A new analysis for modelling, simulation and computational prediction of cumulative rainfall from one geographical location is well presented. They are used as data information, only the historical time series of daily flows measured in mmH2O. Preliminary results of the annual forecast in mmH2O with a prediction horizon of one year and a half are presented, 18 months, respectively. The methodology employs artificial neural network based tools, statistical analysis and computer to complete the missing information and knowledge of the qualitative and quantitative behavior. They also show some preliminary results with different prediction horizons of the proposed filter and its comparison with the performance Gaussian process filter used in the literature.
Highlights
Climate variability in the semi-humid and arid parts of the northwestern part of Argentina poses a great risk to the people and resources of these regions [1] as the smallest fluctuations of weather parameters like precipitation damage the agriculture and economy of the region but disturb the overall water cycle [2].The Artificial Neural Networks (ANNs) are mostly used as predictor filter with an unknown number of parameters performed by a lot of author, recently, such as in [3][4][5][6]
In this article, forecasting rainfall time-series with stochastic output approximated by neural networks Bayesian approach has been presented
An ANNs algorithm based on Bayesian inference to model neural networks parameters were detailed
Summary
Climate variability in the semi-humid and arid parts of the northwestern part of Argentina poses a great risk to the people and resources of these regions [1] as the smallest fluctuations of weather parameters like precipitation damage the agriculture and economy of the region but disturb the overall water cycle [2]. The difficulties in modeling such complex systems are considerably reduced by the recent Artificial Intelligence tools like Artificial Neural Networks (ANNs); Genetic Algorithm (GA) [8] based evolutionary optimizer and Genetic Programming (GP). An ANNs filter is used and their parameters are set in function of the roughness of the time series These are considered as random variables whose distribution is inferred by posterior probability from the data, in which is included as an additional parameter, the number of hidden neurons and modelling uncertainty [10]. A model attempting to estimate the value of a random variable may have potential access to a wide range of measurements regarding the state of the environment Some of these quantities may provide the model with useful information regarding the random variable, whereas others may not. A network that receives both www.ijacsa.thesai.org (IJACSA) International Journal of Advanced Computer Science and Applications, Vol 5, No 6, 2014 useful inputs and “nuisance” inputs will contain too many free parameters and, be prone to overfitting the training data leading to poor generalization
Sign-in/Register to view highlights & summary 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 callMore From: International Journal of Advanced Computer Science and Applications
Disclaimer: 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.
