Mohammad Ansari Ghojghar; Masoud Pourgholam-Amiji; Shahab Araghinejad; Banafsheh Zahraie; Saman Razavi; Ali Salajegheh
Abstract
Due to the growing development of meta-models and their combination with optimization algorithms for modeling and predicting meteorological variables, in this research four metaheuristic optimization algorithms of Particle Swarm Optimization (PSO), Genetics Algorithms (GA), Ant Colony Optimization for ...
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Due to the growing development of meta-models and their combination with optimization algorithms for modeling and predicting meteorological variables, in this research four metaheuristic optimization algorithms of Particle Swarm Optimization (PSO), Genetics Algorithms (GA), Ant Colony Optimization for Continuous Domains (ACOR) and Differential Evolutionary (DE) were combined with the adaptive neural-fuzzy inference system (ANFIS) model. The performance of four combined models developed with ANFIS model to predict the Frequency variables of Dust Stormy Days (FDSD) on a seasonal scale in Khuzestan province in the southwest of Iran was evaluated. For this purpose, hourly dust data and codes of the Word Meteorological Organization were used on a seasonal scale with a statistical period of 40 years (1980-2019) in seven synoptic stations of Khuzestan province. The results of good fit indices in the training and testing phase showed that there is no significant difference between the ANFIS method and other combined models used. R and RMSE values of the best combined model (ANFIS-PSO) from 0.88 to 0.97 and 0.10 to 0.19, respectively, and in the ANFIS model from 0.83 to 0.94 and 0.11 to 21, respectively, were variable. The results also showed that the combination of optimization algorithms used with the ANFIS model does not significantly improve the results of the model compared to the individual ANFIS model.
Zhila Ghorbani; Kiomars Sefidi; Farshad Keivan Behjou; Mehdi Moameri; Ali Ashraf Soltani Tolarod
Abstract
The most current way for measuring the soil fragmentation is determination of mean weight diameter (MWD). In this study, the adaptive neuro-fuzzy inference system (ANFIS) was used to predict of range soil fragmentation affected by different grazing intensities, distance from village and sampling depth. ...
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The most current way for measuring the soil fragmentation is determination of mean weight diameter (MWD). In this study, the adaptive neuro-fuzzy inference system (ANFIS) was used to predict of range soil fragmentation affected by different grazing intensities, distance from village and sampling depth. Present study conducted at 2015 in 3 adjacent rural areas (Alvars, Aldashin and Asbe marz) in Darvishchai watershed in Ardabil County. The studied parameters on the soil fragmentation including different grazing intensities in 3 levels (low, medium and high intensity), distance from village in 3 levels (200, 400 and 600 meters) and the soil sampling depths in 2 levels (0-15cm and 15-30cm). Obtained data were transferred to MATLAB software for the development of ANFIS models. For evaluating the models operation, mean squares error (MSE) and correlation (R2) were used. The result of best ANFIS model in prediction of soil fragmentation was compared with results of regression model. The results show that different grazing intensities, distance from village, sampling depth and their combinations had significant effect on the soil fragmentation. Increase of grazing intensity resulted in increment of soil fragmentation. With increment the distance from village from 200 to 400 meters, soil fragmentation decreased but with increment of distance, increased. Soil fragmentation in all conditions was higher at depth of 0-15 cm than depth of 15-30 cm. ANFIS model had more precision in prediction of soil fragmentation (R2=0.96) relative to regression model (R2=0.76).