Comparison of Three Intelligent Techniques for Runoff Simulation

In this study, performance of a feedback neural network, Elman, is evaluated for runoff simulation. The model ability is compared with two other intelligent models namely, standalone feedforward Multi-layer Perceptron (MLP) neural network model and hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) model. In this case, daily runoff data during monsoon period in a catchment located at south India were collected. Three statistical criteria, correlation coefficient, coefficient of efficiency and the difference of slope of a best-fit line from observed-estimated scatter plots to 1:1 line, were applied for comparing the performances of the models. The results showed that ANFIS technique provided significant improvement as compared to Elman and MLP models. ANFIS could be an efficient alternative to artificial neural networks, a computationally intensive method, for runoff predictions providing at least comparable accuracy. Comparing two neural networks indicated that, unexpectedly, Elman technique has high ability than MLP, which is a powerful model in simulation of hydrological processes, in runoff modeling

Modeling Financial Time Series with Artificial Neural Networks

Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001

Neural-Based Ensembles and Unorganized Machines to Predict Streamflow Series from Hydroelectric Plants

Estimating future streamflows is a key step in producing electricity for countries with hydroelectric plants. Accurate predictions are particularly important due to environmental and economic impact they lead. In order to analyze the forecasting capability of models regarding monthly seasonal streamflow series, we realized an extensive investigation considering: six versions of unorganized machines—extreme learning machines (ELM) with and without regularization coefficient (RC), and echo state network (ESN) using the reservoirs from Jaeger’s and Ozturk et al., with and without RC. Additionally, we addressed the ELM as the combiner of a neural-based ensemble, an investigation not yet accomplished in such context. A comparative analysis was performed utilizing two linear approaches (autoregressive model (AR) and autoregressive and moving average model (ARMA)), four artificial neural networks (multilayer perceptron, radial basis function, Elman network, and Jordan network), and four ensembles. The tests were conducted at five hydroelectric plants, using horizons of 1, 3, 6, and 12 steps ahead. The results indicated that the unorganized machines and the ELM ensembles performed better than the linear models in all simulations. Moreover, the errors showed that the unorganized machines and the ELM-based ensembles reached the best general performances

Multiple-input multiple-output vs. single-input single-output neural network forecasting

This study attempts to improve the forecasting accuracy of tourism demand by using the existing common trends in tourist arrivals form all visitor markets to a specific destination in a multiple-input multiple-output (MIMO) structure. While most tourism forecasting research focuses on univariate methods, we compare the performance of three different Artificial Neural Networks in a multivariate setting that takes into account the correlations in the evolution of inbound international tourism demand to Catalonia (Spain). We find that the MIMO approach does not outperform the forecasting accuracy of the networks when applied country by country, but it significantly improves the forecasting performance for total tourist arrivals. When comparing the forecast accuracy of the different models, we find that radial basis function networks outperform multilayer-perceptron and Elman networks

Numerical weather prediction wind correction methods and its impact on computational fluid dynamics based wind power forecasting

Numerical weather prediction (NWP) of wind speed (WS) is an important input to wind power forecasting (WPF), which its accuracy will limit the WPF performance. This paper proposes three NWP correcting methods based on the multiple linear regression, a radial basis function neural network, and an Elman neural network. The proposed correction methods exhibit small sample learning and efficient computational ability. So, they are in favour of forecasting the performance of planned large-scale wind farms. To this end, a physical WPF model based on computational fluid dynamics is used to demonstrate the impact of improving the NWP WS data based forecasting. A certain wind farm located in China is selected as the case study, and the measured and NWP WS forecasts before and after correction are taken as inputs to the WPF model. Results show that all three correction methods improve the precision of the NWP WS forecasts, with the nonlinear correction models performing a little better than the linear one. Compared with the original NWP, the three corrected NWP WS have higher annual, single point, and short-term prediction accuracy. As expected, the accuracy of wind power forecasting will increase with the accuracy of the input NWP WS forecast. Moreover, the WS correction enhances the consistency of error variation trends between input WS and output wind power. The proposed WS correction methods greatly improve the accuracy of both original NWP WS and the WPF derived from them

Flood Forecasting Using Machine Learning Methods

This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate

Multiple-input multiple-output vs. single-input single-output neural network forecasting

Working PapersThis study attempts to improve the forecasting accuracy of tourism demand by using the existing common trends in tourist arrivals form all visitor markets to a specific destination in a multiple-input multiple-output (MIMO) structure. While most tourism forecasting research focuses on univariate methods, we compare the performance of three different Artificial Neural Networks in a multivariate setting that takes into account the correlations in the evolution of inbound international tourism demand to Catalonia (Spain). We find that the MIMO approach does not outperform the forecasting accuracy of the networks when applied country by country, but it significantly improves the forecasting performance for total tourist arrivals. When comparing the forecast accuracy of the different models, we find that radial basis function networks outperform multilayer-perceptron and Elman networks.Preprin

Big Data Analysis application in the renewable energy market: wind power

Entre as enerxías renovables, a enerxía eólica e unha das tecnoloxías mundiais de rápido crecemento. Non obstante, esta incerteza debería minimizarse para programar e xestionar mellor os activos de xeración tradicionais para compensar a falta de electricidade nas redes electricas. A aparición de técnicas baseadas en datos ou aprendizaxe automática deu a capacidade de proporcionar predicións espaciais e temporais de alta resolución da velocidade e potencia do vento. Neste traballo desenvólvense tres modelos diferentes de ANN, abordando tres grandes problemas na predición de series de datos con esta técnica: garantía de calidade de datos e imputación de datos non válidos, asignación de hiperparámetros e selección de funcións. Os modelos desenvolvidos baséanse en técnicas de agrupación, optimización e procesamento de sinais para proporcionar predicións de velocidade e potencia do vento a curto e medio prazo (de minutos a horas)