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Landeras G.,Basque Country Research Institute for Agricultural Development | Lopez J.J.,University of Pamplona | Kisi O.,CanikBasari University | Shiri J.,University of Tabriz
Energy Conversion and Management | Year: 2012

Surface incoming solar radiation is a key variable for many agricultural, meteorological and solar energy conversion related applications. In absence of the required meteorological sensors for the detection of global solar radiation it is necessary to estimate this variable. Temperature based modeling procedures are reported in this study for estimating daily incoming solar radiation by using Gene Expression Programming (GEP) for the first time, and other artificial intelligence models such as Artificial Neural Networks (ANNs), and Adaptive Neuro-Fuzzy Inference System (ANFIS). A comparison was also made among these techniques and traditional temperature based global solar radiation estimation equations. Root mean square error (RMSE), mean absolute error (MAE) RMSE-based skill score (SS RMSE), MAE-based skill score (SS MAE) and r 2 criterion of Nash and Sutcliffe criteria were used to assess the models' performances. An ANN (a four-input multilayer perceptron with 10 neurons in the hidden layer) presented the best performance among the studied models (2.93 MJ m -2 d -1 of RMSE). The ability of GEP approach to model global solar radiation based on daily atmospheric variables was found to be satisfactory. © 2012 Elsevier Ltd. All rights reserved. Source


Shiri J.,University of Tabriz | Marti P.,Polytechnic University of Valencia | Nazemi A.H.,University of Tabriz | Sadraddini A.A.,University of Tabriz | And 3 more authors.
Hydrology Research | Year: 2015

The improvement of methods for estimating reference evapotranspiration (ET0) requiring few climatic inputs is crucial, due to the partial or total lack of climatic inputs in many situations. The current paper compares the effect of local and external training procedures in neuro-fuzzy and neural network models for estimating ET0 relying on two input combinations considering k-fold testing. Therefore, different data set configurations were defined based on temporal and spatial criteria allowing for a complete and suitable testing scan of the complete data set. The proposed methodology enabled the comparison in each station of models trained with local data series and models trained with the data series from the remaining stations. Results showed that the external training based on a suitable input choice and a representative pattern collection might be a valid alternative to the more common local training. © IWA Publishing 2015. Source


Shiri J.,University of Tabriz | Sadraddini A.A.,University of Tabriz | Nazemi A.H.,University of Tabriz | Kisi O.,Canik Basari University | And 3 more authors.
Journal of Hydrology | Year: 2014

When dealing with climatic variables, the performance assessment of many Artificial Intelligence (AI) and/or data mining applications is based on a single data set assignment of the training and test sets. Further, it is very usual that this assignment is defined according to a local and temporary criterion, i.e. the models are trained and tested using data of the same station. Based on this procedure, the performance of the models outside the training location cannot be inferred. The present work evaluates the performance of Gene Expression Programming (GEP) based models for estimating reference evapotranspiration (ET0) according to temporal and spatial criteria and data set scanning procedures in coastal environments of Iran. The accuracy differences between the local and the external performance depend on the specific climatic trends of the test stations, as well as on the input combination used to feed the models. When relying on a suitable input selection, externally trained models might be a valid alternative to locally trained ones, which would be a crucial advantage in places where only limited climatic variables are available. K-fold testing is a good choice to prevent partially valid conclusions derived from model assessments based on a simple data set assignment. Further, calibration of the GEP model may not be needed, if enough climatic data are available at other stations for external model application. The performance of the GEP model fluctuates chronologically and spatially. A suitable assessment of the model should consider a complete local and/or external scan of the data set used. © 2013 Elsevier B.V. Source


Shiri J.,University of Tabriz | Sadraddini A.A.,University of Tabriz | Nazemi A.H.,University of Tabriz | Kisi O.,Canik Basari University | And 3 more authors.
Hydrology Research | Year: 2013

Temperature and solar radiation-based modeling procedures are reported in this study for estimating daily reference evapotranspiration (ET0) by using gene expression programming (GEP) and adaptive neuro-fuzzy inference system (ANFIS). A comparison is also made among these techniques and the corresponding traditional temperature/radiation-based ET0 estimation equations. Two data management scenarios were evaluated for estimating ET0: (1) the models were trained and tested using the local data of each studied weather station; and (2) the models were trained using the pooled data from all the stations and tested in each individual station. The GEP and ANFIS models were found to be better than the Hargreaves Samani, Makkink and Turc ET0 equations in the first scenario. Comparison of GEP and ANFIS models trained with pooled data and tested for each station showed that the ANFIS models generally performed better than the GEP models. However, the comparison of GEP and ANFIS models trained and tested with pooled data revealed that the GEP models performed better than the ANFIS models in the second scenario. © IWA Publishing 2013. Source

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