Implementing a reliable computational model for predicting the reference evapotranspiration (ET0)... more Implementing a reliable computational model for predicting the reference evapotranspiration (ET0) process is essential for several agricultural and hydrological applications, especially for the rural water resource systems, water use allocations, utilization and demand assessments, and the management of irrigation systems. In this research, two artificial intelligence (AI) models, artificial neural network (ANN) and model tree (MT), were investigated for modelling ET0. To validate model performance, five climatic stations such as Urmia, Mahabad, Takab, Khoy, and sardasht in West Azerbaijan Province of Iran. In the next step and to improve the model's accuracy, a novel preprocessing algorithm, ensemble empirical mode decomposition (EEMD), was coupled with those AI models to remove the trends or noise in the time series dataset. The extracted results indicated that the EEMD-MT model for all five stations outperformed other standalone and hybrid models.
Snow cover area on a river basin, affects so many meteorologic and environmental parameters. By g... more Snow cover area on a river basin, affects so many meteorologic and environmental parameters. By growing remote sensing technology, nowadays snow cover area could be measured on a regular basis for scientific purposes. In this study, the monthly average of snow cover area of the Baranduz river basin from West Azerbaijan in Iran had been used for modelling by ANN and SVM. The snow cover area was extracted from MODIS 8-day maximum snow extent products from 2000 to 2019. Also, the 20 meteorologic parameters were collected from Bibakran and Babarud ground hydrometeorological stations and 20 parameters were collected from satellite base data powered by NASA LaRC projects. After BoxCox transformation analysis, the feature selection methods were used to select the modelling subsets. Partial least square regression base filter and wrapper feature selection methods were used to select modelling subsets. LW, RC, SR, VIP, SMC, MRMR, JT filter methods and GA, MCUVE and REP wrapper methods were u...
Implementing a reliable computational model for predicting the reference evapotranspiration (ET0)... more Implementing a reliable computational model for predicting the reference evapotranspiration (ET0) process is essential for several agricultural and hydrological applications, especially for the rural water resource systems, water use allocations, utilization and demand assessments, and the management of irrigation systems. In this research, two artificial intelligence (AI) models, artificial neural network (ANN) and model tree (MT), were investigated for modelling ET0. To validate model performance, five climatic stations such as Urmia, Mahabad, Takab, Khoy, and sardasht in West Azerbaijan Province of Iran. In the next step and to improve the model's accuracy, a novel preprocessing algorithm, ensemble empirical mode decomposition (EEMD), was coupled with those AI models to remove the trends or noise in the time series dataset. The extracted results indicated that the EEMD-MT model for all five stations outperformed other standalone and hybrid models.
Snow cover area on a river basin, affects so many meteorologic and environmental parameters. By g... more Snow cover area on a river basin, affects so many meteorologic and environmental parameters. By growing remote sensing technology, nowadays snow cover area could be measured on a regular basis for scientific purposes. In this study, the monthly average of snow cover area of the Baranduz river basin from West Azerbaijan in Iran had been used for modelling by ANN and SVM. The snow cover area was extracted from MODIS 8-day maximum snow extent products from 2000 to 2019. Also, the 20 meteorologic parameters were collected from Bibakran and Babarud ground hydrometeorological stations and 20 parameters were collected from satellite base data powered by NASA LaRC projects. After BoxCox transformation analysis, the feature selection methods were used to select the modelling subsets. Partial least square regression base filter and wrapper feature selection methods were used to select modelling subsets. LW, RC, SR, VIP, SMC, MRMR, JT filter methods and GA, MCUVE and REP wrapper methods were u...
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