hamide afkhami; azam habibi pour; mohammad reza ekhtesasi
Abstract
Evaporation is considered one of the key climatic variables, especially in arid regions and evaporation losses is one of the important issues in irrigation and water resources management in these areas. Therefore, it is important being aware of the amount of evaporation and its modeling, as one of the ...
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Evaporation is considered one of the key climatic variables, especially in arid regions and evaporation losses is one of the important issues in irrigation and water resources management in these areas. Therefore, it is important being aware of the amount of evaporation and its modeling, as one of the most important hydrological variables in agricultural research and water and soil conservation. In recent decades, artificial intelligence techniques have proven high capability and flexibility to estimate and predict nonlinear phenomena. In this study, three important data mining techniques including Artificial Neural Network, Active Neuro-Fuzzy Inference System and Regression Decision Tree were used for predicting evaporation. For this purpose, 8 climatic variables (Minimum average temperature, average maximum temperature, average temperature, sunshine hours, wind speed, wind direction, relative humidity and evaporation averages) were employed in this study. The results showed three models are able to predict evaporation for 12 months after. Finally among the used models, ANN showed better performance with coefficient efficiency of 0.97 and RMSE of 5.1and ME of 0.48. Also, The results showed that there is not significant difference in simulation results to predict the evaporation between two scenario, original data and normalized data.
Hamide Afkhami; mohammad dastorani; farzaneh fotouhi firuzabadi
Abstract
Due to the nature of the sediment data, selection of appropriate methods for processing the data before entering them to the artificial intelligence models can enhance the reliability of simulations results. In this study, the effects of sediment data processing procedures on ANN and ANFIS models outputs ...
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Due to the nature of the sediment data, selection of appropriate methods for processing the data before entering them to the artificial intelligence models can enhance the reliability of simulations results. In this study, the effects of sediment data processing procedures on ANN and ANFIS models outputs in 7 Dez Basin stations were evaluated. Accordingly, three scenarios were considered: In the first scenario, original data was used without exerting any processing technique; in the second scenario, the data was normalized; and in the third scenario, logarithm of data were used according to logarithmic distribution governing. The simulation results showed that using data logarithm leads to higher performance and lower error, especially in stations where the best fit probability distribution is one of the log family distributions. Finally, among applied models, ANFIS showed the best performance with coefficient efficiency of 0.95 and RMSE of 5.4, MSE of 1.4 and ME of 0.42 in Biatoon gauging station and using the third scenario.