Performance Evaluation of Genetic Algorithm and GA-SA Hybrid Method in Forecasting Dust Storms (Case Study: Khuzestan Province)

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Ansari Ghojghar Mohammad; Pourgholam Amiji Masoud; BAZRAFSHAN JAVAD; ARAGHINEJAD SHAHAB; LIAGHAT ABDOLMAJID; HOSSEINI MOGHARI SEYED MOHAMMAD

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Iranian Journal of Soil and Water Research Year:2021 | Volume:51 | Issue:10 Page(s): 2623-2639

Download Full-Text

Persian Verion

View:

337

Download:

Cites:

Information Journal Paper

Title

Performance Evaluation of Genetic Algorithm and GA-SA Hybrid Method in Forecasting Dust Storms (Case Study: Khuzestan Province)

Author(s)

Keywords

DustQ1

FDSD IndexQ1

Desert GreeningQ2

Genetic-AnnealingQ1

ANFISQ1

Abstract

The increase in Dust storms occurrence in recent years in southwestern Iran, especially in Khuzestan province, and consequently the decrease in air quality in these areas, has doubled the importance of forecasting and linking this phenomenon with climate variations. The aim of this study was to investigate the efficiency of hybrid Genetic-Annealing (GA-SA) and Genetic Algorithm (GA) methods for selecting optimal input variables in forecasting the frequency of days with Dust storm (FDSD). For this purpose, hourly Dust data and meteorological organization codes, as well as climatic data including maximum temperature, minimum temperature, average temperature, total rainfall and maximum wind speed on a seasonal scale with a statistical period of 35 years (1984-2018) in seven synoptic stations in Khuzestan province were used. Then, by having a time series of FDSD Index and other climatic variables, it was compared to the efficiency of different modes of input variables, in order to forecasting the frequency of days with Dust storm in the next season. The results showed that the hybrid Genetic-Annealing method (GA-SA) had the best performance among all the modes of selecting the input variables; In this case, the evaluation criteria of R, MAE and RMSE varied from 0. 91, 0. 29, and 0. 44 to 0. 99, 0. 13 and 0. 17 in the studied stations, respectively. Also, the average frequency of days with Dust storm on a seasonal scale in the studied stations varied from 1. 68 to 4. 10, respectively, so that with increasing FDSD Index in the study station, the predictive accuracy of all modes increased so that in the first forecast state (based solely on the FDSD Index), the correlation coefficient between the observational values of the days associated with Dust storms and its computational values increased from 0. 87 to 0. 95. For the second case (forecast based on frequency of days with Dust storm and all Auxiliary Characteristics, ie FDSD & AC), the third mode (based on the optimization of the Genetic Algorithm) and the fourth mode (based on the hybrid Genetic-Annealing method) the correlation coefficient also varied from 0. 93 to 0. 94, 0. 91 to 0. 97 and 0. 94 to 0. 99 in the studied stations, respectively. In general, by comparing the modes used, the hybrid Genetic-Annealing method (GA-SA) performed the best, followed by the Genetic Algorithm (GA). The results of this study can be useful in managing the consequences of Dust storms and desertification programs in the study areas.

Cites

No record.

References

No record.

Cite

APA: Copy

Ansari Ghojghar, Mohammad, Pourgholam Amiji, Masoud, BAZRAFSHAN, JAVAD, ARAGHINEJAD, SHAHAB, LIAGHAT, ABDOLMAJID, & HOSSEINI MOGHARI, SEYED MOHAMMAD. (2021). Performance Evaluation of Genetic Algorithm and GA-SA Hybrid Method in Forecasting Dust Storms (Case Study: Khuzestan Province). IRANIAN JOURNAL OF SOIL AND WATER RESEARCH, 51(10 ), 2623-2639. SID. https://sid.ir/paper/375609/en

Vancouver: Copy

Ansari Ghojghar Mohammad, Pourgholam Amiji Masoud, BAZRAFSHAN JAVAD, ARAGHINEJAD SHAHAB, LIAGHAT ABDOLMAJID, HOSSEINI MOGHARI SEYED MOHAMMAD. Performance Evaluation of Genetic Algorithm and GA-SA Hybrid Method in Forecasting Dust Storms (Case Study: Khuzestan Province). IRANIAN JOURNAL OF SOIL AND WATER RESEARCH[Internet]. 2021;51(10 ):2623-2639. Available from: https://sid.ir/paper/375609/en

IEEE: Copy

Mohammad Ansari Ghojghar, Masoud Pourgholam Amiji, JAVAD BAZRAFSHAN, SHAHAB ARAGHINEJAD, ABDOLMAJID LIAGHAT, and SEYED MOHAMMAD HOSSEINI MOGHARI, “Performance Evaluation of Genetic Algorithm and GA-SA Hybrid Method in Forecasting Dust Storms (Case Study: Khuzestan Province),” IRANIAN JOURNAL OF SOIL AND WATER RESEARCH, vol. 51, no. 10 , pp. 2623–2639, 2021, [Online]. Available: https://sid.ir/paper/375609/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops