Spatial cross-validation and average-error statistics are examined with respect to their abilities to evaluate alternate spatial interpolation methods. A simple crossvalidation methodology is described, and the relative abilities of three, dimensioned error statistics—the root-mean-square error (RMSE), the mean absolute error (MAE), and the mean bias error (MBE)—to describe average interpolator...

We evaluate and compare two common methods, artificial neural networks (ANN) and support vector regression (SVR), for predicting energy productions from a solar photovoltaic (PV) system in Florida 15 min, 1 h and 24 h ahead of time. A hierarchical approach is proposed based on the machine learning algorithms tested. The production data used in this work corresponds to 15 min averaged power meas...

This paper presents a relative humidity predictions using feedforward artificial neural network (FFNN). Relative humidity values obtained from weather records for Malaysia are used in training the FFNNs. The prediction of the relative humidity is in terms of Sun shine ration and cloud cover. However, three statistical parameters, namely, mean absolute percentage error, MAPE, mean bias error, MB...

The Artificial Neural Network (ANN) fitting tool is used for the prediction of solar radiation. Solar radiation data from 12 Indian stations with different climatic conditions are used for training and testing the ANN. The Levenberg-Marquard (LM) algorithm is used in this analysis. The results of ANN model are compared with measured data on the basis of root mean square error (RMSE) and mean bi...

Abstract. The root-mean-squared error (RMSE) and mean absolute (MAE) are widely used metrics for evaluating models. Yet, there remains enduring confusion over their use, such that a standard practice is to present both, leaving it the reader decide which more relevant. In recent reprise 200-year debate Willmott Matsuura (2005) Chai Draxler (2014) give arguments favoring one metric or other. How...

abstract long-term historical weather data are needed to conduct crop simulation analysis. simulated climatic data may be used when long series of historic data are not available or convenient or when future data are needed. the aim of this study was to generate daily weather values for maximum and minimum air temperatures. a number of weather generators are compared here. data from ten station...

Saturated hydraulic conductivity of Soil is an important property in processes involving water and solute flow in soils. Saturated hydraulic conductivity of soil is difficult to measure and can be highly variable, requiring a large number of replicate samples. In this study, 60 sets of soil samples were collected at Saqhez region of Kurdistan province-IRAN. The statistics such as Correlation Co...