تعیین رابطه بین متغیر‌های حدی دما با فراوانی گردو غبارزیست‌محیطی و ارزیابی بهترین مدل پیش‌بینی شاخص FDSD در غرب کشور

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی آبیاری و آبادانی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج.

2 دانشیار گروه مهندسی آبیاری و آبادانی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران.

10.22059/ijswr.2022.342666.669261

چکیده

گرد و غبار همواره به عنوان یکی از مهم‌ترین مخاطرات محیطی مطرح بوده و پیامدهای زیست‌محیطی نامطلوبی را برجای می­گذارد. هدف از این پژوهش، بررسی رابطه­ی متغیرهای حدی دمایی با طوفان­های گرد و غبار و ارزیابی بهترین مدل جهت پیش­بینی شاخص FDSD در غرب کشور می­باشد. با استفاده از داده­های ساعتی قدرت دید افقی، کدهای سازمان جهانی هواشناسی، نمایه­های حدی دمایی شامل دمای بیشینه (T) و دمای کمینه () در مقیاس ماهانه برای 14 ایستگاه هواشناسی واقع در غرب کشور با طول دورۀ آماری 25 ساله (2014-1990) و ضرایب همبستگی تاو-کندال و پیرسون به ارتباط سنجی پرداخته شد. نقشه ضرایب همبستگی برای نمایش بهتر نتایج به روش اسپلاین (روش شعاع پایه) در نرم­افزار ArcGIS تهیه گردید. همچنین سه مدل هوش مصنوعی شامل الگوریتم بهترین همسایگی (KNN)، برنامه­ریزی بیان ژن (GEP) و شبکه بیزین (BN) جهت پیش­بینی گرد و غبار مورد ارزیابی قرار گرفتند. نتایج نشان داد که طوفان­های گرد و غباری همبستگی مثبت و معنی­داری با نمایه­های حدی دمایی در 14 ایستگاه مورد مطالعه دارند به نحوی که بالاترین ضریب همبستگی تاو-کندال با شاخص FDSD مربوط به متغیر بیشینه دما در ایستگاه دو گنبدان با مقدار 202/0 و دمای کمینه در ایستگاه سر پل ذهاب با مقدار 242/0 بود. همچنین بالاترین ضریب همبستگی پیرسون با شاخص FDSD  نیز برای متغیر بیشینه دما در ایستگاه دوگنبدان با مقدار 415/0 و دمای کمینه در ایستگاه اسلام آباد با مقدار 211/0 بود. همچنین نتایج پیش­بینی حاکی از عملکرد مناسب روش  KNNمی­باشد که در 13 ایستگاه رتبه نخست را به خود اختصاص داده است و در ایستگاه اسلام­آباد روش BN بهترین عملکرد را داشته است. نتایج نشان داد که این مطالعه می­تواند به درک صحیح وقوع طوفان­های گرد و غبار و بررسی روابط اقلیمی و همچنین کاهش خسارات ناشی از این پدیده در منطقه مورد مطالعه کمک شایانی کند.

کلیدواژه‌ها


عنوان مقاله [English]

Determining the relationship between temperature extreme variables and the frequency of environmental dust and evaluating the best model for predicting the FDSD index in the west of the country

نویسندگان [English]

  • Haniyeh Mohammadi 1
  • Javad Bazrafshan 2
1 Irrigation and Development Engineering Department, College of Agriculture and Natural Resources, University of Tehran, Karaj.
2 Associate Professor, Department of Irrigation and Development Engineering, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.
چکیده [English]

Dust has always been one of the most important environmental hazards and has adverse environmental consequences. The purpose of this study is to investigate the relationship between temperature extreme variables and dust storms and evaluate the best model for predicting the FDSD index in the west of the country. We used hourly visibility data, World Meteorological Organization codes and temperature extreme indices including maximum temperature (TXx) and minimum temperature (TNn) on a monthly basis for 14 meteorological stations located in the west of the country with a statistical period of 25 years (1990-2014) and correlation between them were considered using Tau-Kendall and Pearson correlation coefficients. Map of correlation coefficients to better display the results was prepared by spline method (base radius method) in ArcGIS software. Also, three artificial intelligence models including best neighbor algorithm (KNN), gene expression programming (GEP) and Bayesian network (BN) were evaluated to predict dust. The results showed that dust storms have a positive and significant correlation with temperature extreme indices in 14 studied stations, so that the highest Tau-Kendall correlation coefficient with FDSD index is related to the maximum temperature variable in Dogonbadan station with a value of 0.202 and with the minimum temperature at Sare-Pole-Zahab station with the correlation coefficient 0.242. Also, the highest Pearson correlation coefficient with FDSD index for the maximum temperature variable in Dogonbadan station was 0.415 and that of the minimum temperature in Islamabad station 0.211. Also, the results of the forecast indicated the proper performance of the KNN method, which is ranked first in 13 stations and the BN method had the best performance in Islamabad station. The results of this study can help to better understand the occurrence of dust storms and to studying their climatic relations, as well as to reducing the damage caused by this phenomenon in the study area.

کلیدواژه‌ها [English]

  • Temperature limit variables
  • Tau Kendall correlation
  • Forecast
  • Best Neighborhood Algorithm
  • Bayesn Network
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