Mohammad Ansari Ghojghar; Sosan Salajegheh; Paria Pourmohammad
Abstract
This study aims to model dust storms in Khuzestan province using hybrid Jenkins-Catalyst SARIMA-ACOR and SARIMA-PSO models. For this purpose, hourly dust data and codes from the World Meteorological Organization (WMO) were used from seven synoptic stations across Khuzestan over a 40-year period. To enhance ...
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This study aims to model dust storms in Khuzestan province using hybrid Jenkins-Catalyst SARIMA-ACOR and SARIMA-PSO models. For this purpose, hourly dust data and codes from the World Meteorological Organization (WMO) were used from seven synoptic stations across Khuzestan over a 40-year period. To enhance accuracy and minimize potential errors, this research employs the integration of optimization algorithms, namely Ant Colony Optimization (ACOR) and Particle Swarm Optimization (PSO), with the Box-Jenkins SARIMA model. The optimization algorithms are used for model training, parameter selection, pattern recognition, clustering, reinforcement learning, image processing, intelligent system design, and optimizing generative models. To determine the best-fitting model, the goodness-of-fit criteria, including R, RMSE, MAE, and NS, were applied. The results indicate that the hybrid SARIMA-ACOR model outperforms both the SARIMA-PSO hybrid model and the standalone SARIMA model. Among the seasonal combinations tested, combinations one and two demonstrated the highest performance and accuracy. The SARIMA-ACOR hybrid model showed superior performance in predicting the FDSD index, with Root Mean Square Error (RMSE = 0.219–0.198), Correlation Coefficient (R = 0.891–0.859), Mean Absolute Error (MAE = 0.142–0.123), and Nash-Sutcliffe Efficiency (NS = 0.881–0.862) compared to the other models.
Mohammad Ansari Ghojghar; Masoud Pourgholam-Amiji; Shahab Araghinejad; Iman Babaeian; Abdolmajid Liaghat; Ali Salajegheh
Abstract
It is clear that the ENSO phenomenon affects the hydrological and climatic regimes in different parts of the world, but the extent of this effect in different parts of the world has not yet been answered. Therefore, this research has been done to answer this important question. In this research, using ...
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It is clear that the ENSO phenomenon affects the hydrological and climatic regimes in different parts of the world, but the extent of this effect in different parts of the world has not yet been answered. Therefore, this research has been done to answer this important question. In this research, using the Oceanic Niño Index (ONI), the effect of the positive phase of the El Niño-Southern Oscillation (ENSO) on the Frequency of Dust Stormy Days (FDSD) in 12 synoptic stations located in Khuzestan and Sistan and Baluchestan provinces over a period of 40 years (2019-1980) has been reviewed. For this purpose, hourly dust data, codes of the World Meteorological Organization, Adaptive Neural-Fuzzy Inference System (ANFIS) and time changes of FDSD index in two neutral phases and the occurrence of El Niño were used. The results of ANFIS model estimation and observational values of FDSD index showed that at the occurrence time of El Niño in Khuzestan and Sistan and Baluchestan provinces, equal to 33 and 17 events, respectively, the observable values of the frequency of days with dust storm were less than the estimated values. The results also showed that the positive phase of ONI is more effective on dust storms in Khuzestan province than in Sistan and Baluchestan province. Therefore, during the hot phase of ENSO, more measures should be taken to control and manage dust storms and their destructive effects in areas where the source of dust storms is external.
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.
