Forecast of daily mean, maximum and minimum temperature time series by three artificial neural network methods

Abstract

Temperature forecasting has been one of the most important factors considered in climate impact studies on sectors of agriculture, vegetation, water resources and tourism. The main purpose of this study is to forecast daily mean, maximum and minimum temperature time series employing three different artificial neural network (ANN) methods and provide the best-fit prediction with the observed actual data using ANN algorithms.The geographical location considered is one of Turkey's most important areas of agricultural production, the Geyve and Sakarya basin, located in the south-east of the Marmara region (40°N and 30°E). The methods chosen in this study are:(1) feed-forward back propagation (FFBP), (2) radial basis function (RBF) and, (3) generalized regression neural network (GRNN). Additionally, predictions with a multiple linear regression (MLR) model were compared to those of the ANN methods. All three different ANN methods provide satisfactory predictions in terms of the selected performance criteria; correlation coefficient (R), root mean square error (RMSE), index of agreement (IA) and the results compared well with the conventional MLR method.

Comparative performance evaluation of blast furnace flame temperature prediction using artificial intelligence and statistical methods

“…(8) below. [5]. Multiple regression modeling is very popular method of statistical analysis in most fields because of its power and flexibility.…”

“…The ANN approach is applied because of its high potential for complex, nonlinear, and time-varying input-output mapping and generally it is thought to be more powerful than other regression-based techniques [5]. On the other hand, in most of the studies the results obtained from complex ANN models are compared with those from more standard linear techniques such as regression and time series analysis for benchmarking [5]. The model results are compared with each other in terms of the performance criteria regression coefficient (R 2 ) and root mean squared error (RMSE).…”

Estimating soil temperature using neighboring station data via multi-nonlinear regression and artificial neural network models

“…It can be trained to overcome the limitations of the conventional approaches to solve complex problems that are difficult to model analytically (Sözen et al 2005;Ustaoglu et al 2008;Walter and Schönwiese 2003;Kostopoulou et al 2007). …”

Univariate modelling of monthly maximum temperature time series over northeast India: neural network versus Yule–Walker equation based approach

“…The advantages of ANNs in forecasting a time series are well documented in literature. In an extensive review, Tim et al (1994) discussed the advantages of an ANN which include the following: ANNs can automatically approximate whatever functional form best characterizes the data; Not only do ANNs estimate non‐linear functions well but also they can extract any residual non‐linear elements from the data after linear terms are removed, and, ANNs have a modest capability for building piece‐wise non‐linear models. Some examples of the application of ANN in surface temperature forecasting are Snell et al (2000), Tang et al (2000) and Ustaoglu et al (2008). The types of ANN that are adopted in the present study include Multilayer Perceptron (MLP), Generalized Feed Forward Neural Network (GFFNN) and Modular Neural Network (MNN).…”