A neural network approach for cumulative monthly rainfall time series forecasting tuned by roughness
نویسندگان
چکیده
1 Mathematics Research Laboratory Applied to Control, Department of Electrical and Electromechanical Engineering, Faculty of Exact, Physical and Natural Sciences, National University of Córdoba, Córdoba, Argentina. 2 Department of Electrical Engineering, Faculty of Sciences and Applied Technologies, National University of Catamarca, Catamarca, Argentina. 3 Institute of Automatics, Faculty of Engineering, National University of San Juan, San Juan, Argentina.
منابع مشابه
A New Approach for Time Series Forecasting: Bayesian Enhanced by Fractional Brownian Motion with Application to Rainfall Series
A new predictor algorithm based on Bayesian enhanced approach (BEA) for long-term chaotic time series using artificial neural networks (ANN) is presented. The technique based on stochastic models uses Bayesian inference by means of Fractional Brownian Motion as model data and Beta model as prior information. However, the need of experimental data for specifying and estimating causal models has ...
متن کاملForecasting Rainfall Time Series with stochastic output approximated by neural networks Bayesian approach
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 co...
متن کاملThe use of wavelet - artificial neural network and adaptive neuro fuzzy inference system models to predict monthly precipitation
Precipitation forecasting due to its random nature in space and time always faced with many problems and this uncertainty reduces the validity of the forecasting model. Nowadays nonlinear networks as intelligent systems to predict such complex phenomena are widely used. One of the methods that have been considered in recent years in the fields of hydrology is use of wavelet transform as a moder...
متن کاملStochastic Monthly Rainfall Time Series Analysis, Modeling and Forecasting ( A cas study: Ardebilcity
Rainfall is the main source of the available water for human. Predicting the amount of the future rainfall is useful for informed policies, planning and decision making that will help potentially make optimal and sustainable use of available water resources. The main aim of this study was to investigate the trend and forecast monthly rainfall of selected synoptic station in Ardabil province usi...
متن کاملPrediction of monthly rainfall using artificial neural network mixture approach, Case Study: Torbat-e Heydariyeh
Rainfall is one of the most important elements of water cycle used in evaluating climate conditions of each region. Long-term forecast of rainfall for arid and semi-arid regions is very important for managing and planning of water resources. To forecast appropriately, accurate data regarding humidity, temperature, pressure, wind speed etc. is required.This article is analytical and its database...
متن کامل