Forecasting River Flow in the USA: A Comparison between Auto-Regression and Neural Network Non-Parametric Models

Abstract

Forecasting a time series became one of the most challenging tasks to variety of data sets. The existence of large number of parameters to be estimated and the effect of uncertainty and outliers in the measurements makes the time series modeling too complicated. Recently, Artificial Neural Network (ANN) became quite successful tool to handle time series modeling problem. This paper provides a solution to the forecasting problem of the river flow for two well known Rivers in the USA. They are the Black Water River and the Gila River. The selected ANN models were used to train and forecast the daily flows of the first station no: 02047500, for the Black Water River near Dendron in Virginia and the second station no: 0944200 for the Gila River near Clifton in Arizona. The feed forward network is trained using the conventional back propagation learning algorithm with many variations in the NN inputs. We explored models built using various historical data. The selection process of various architectures and training data sets for the proposed NN models are presented. A comparative study of both ANN and the conventional Auto-Regression (AR) model networks indicates that the artificial neural networks performed better than the AR model. Hence, we recommend ANN as a useful tool for river flow forecasting.

Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir

“…Fig. 12 shows a compression between current study and previous studies (Abrahart et al, 2001;Baareh et al, 2006;Balaguer et al, 2008;Banihabib et al, 2008;Chiang et al, 2007;Coulibaly et al, 2000;Heidarnejad and Gholami, 2012;Karunasinghe and Liong, 2006;Kisi and Cigizoglu, 2005;Pulido-Calvo and Portela, 2007;Sahoo et al, 2006;Sadrolashrafi et al, 2008;Teschl and Randeu, 2006) based on forecasting period. In this study, because of simultaneous effectiveness of dynamic ANN (use from output delay as input to better training) and optimization for number of hidden layer neurons (17), horizon time of forecasting increased from short or mid-term to long-term period.…”

Dynamic rule curves for flood control of a multipurpose dam

“…Several flow forecasting techniques; such as rainfall-runoff model, time series analysis, and artificial neural network (see, e.g., Dawson et al, 2002;Sivakumar et al, 2002;Baareh et al, 2006;Chetan and Sudheer, 2006;Joorabchi et al, 2007); are available in water resource literature. These existing techniques generally aim to forecast the flows for a short period (e.g., a few days) ahead.…”

Optimizing Software Effort Estimation Models Using Back-Propagation Versus Radial Base Function Networks

“…This means that the user should give the neural network the examples of what he wants (desired output) and the network change the weights of the network's related to that, when training is completed, the output will be estimated according to the desired one which is called the (target output) for a particular input. The Back-propagation Artificial Network still proves its efficiency in a variety of application solving numerous serious real-life problems in finance sectors, cancer disease recognition (Braik and Sheta, 2011), science, forecasting (Baareh et al, 2006;Sheta et al, 2015;2018), feature extraction (Al-Batah et al, 2010), classifications (Seethe et al, 2007;Hongjun et al, 1996;El-Sayyad et al, 2015), face recognition (Radha and Nallammal, 2011), Fingerprint recognition (Al-Najjar and Sheta, 2008) etc. The back-propagation artificial neural is used in this paper to solve the software cost estimation problem.…”