بهبود منحنی‌های شدت ـ مدت ـ فراوانی همگام با الگوریتم‌های رایانه‌ای ژنتیک و توده‌ریزه‌ها در حوزه آبخیز دریاچۀ ارومیه

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری علوم و مهندسی آبخیزداری، گروه مهندسی طبیعت، دانشکده منابع طبیعی و علوم زمین، دانشگاه کاشان، کاشان، ایران

2 استاد، گروه مهندسی طبیعت، دانشکده منابع طبیعی و علوم زمین، دانشگاه کاشان، کاشان، ایران

3 دانشیار، گروه مهندسی طبیعت، دانشکده منابع طبیعی و علوم زمین، دانشگاه کاشان، کاشان، ایران

‎10.22052/deej.2023.248676.1003

چکیده

نوترین داده‌های منحنی‌های شدت ـ زمان ـ فراوانی در ایران از ۶۶ ایستگاه سازمان هواشناسی به‌ دست ‌آمده است که در سال ۱۳۷۴ نگاشته شده است و آگاه به اینکه برای به ‌دست ‌آوردن فیزیکی این داده‌ها به بهره‌بری از باران‌سنج‌ها نیاز داریم، نمی‌توان دورافتاده‌ترین پهنه‌ها را با این ابزار نگریست و گاهی با همین ابزارها هم دچار نادرستی‌های فراوان خواهیم بود. بدین روی برای کاهش این نادرستی‌ها از ساختارههای شدت ـ زمان ـ فراوانی سود می‌‌جوییم. تاکنون ساختاره‌های‌ بسیاری برای به‌ دست‌ آوردن داده‌های مورد نیاز نمودارهای شدت ـ زمان ـ فراوانی بیان شده است که نمی‌توان آن را در همۀ پهنه‌ها به کار برد، مگر آنکه نرخهای پهنه‌ای آن‌ها ارزیابی شده باشد. در این پژوهش، نرخ‌های این ساختاره‌ها همگام با الگوریتم‌های رایانه‌ای در پهنۀ آبخیز دریاچۀ ارومیه بررسی و بهینه‌سازی شد. آنگاه بهره‌وری دو الگوریتم رایانه‌ای توده‌ریزه‌ها و زادشناسی با یکدیگر سنجش شده و سرانجام با الگوریتم نزدیک‌ترین همسایه، کارایی دگرگونی‌های اقلیمی بر نمودارهای شدت ـ زمان ـ فراوانی در این سرزمین بررسی شد. برایند این پژوهش نشان داد که نرخ‌های ساختارۀ آبخضر در همۀ ایستگاه‌های پهنۀ دریاچۀ ارومیه، مگر کمپ مهاباد با نزدیکی خوبی درست است. می‌شود گفت میانگین نرخ‌های پهنه‌ای بهینه‌سازی‌شده با نرخهای به‌دست‌آمده برازش 100 درصدی دارند.

کلیدواژه‌ها


عنوان مقاله [English]

Optimizing Intensity-Duration-Frequency Curves in Consistency with Genetic and Particle Swarm Algorithms: A Case Study of Urmia Lake’s Basin

نویسندگان [English]

  • Emad fahim 1
  • Reza ghazavi ghazavi 2
  • hoda ghasemieh 3
  • Ebrahim Omidvar 3
1 PhD. Student, Department of Nature Engineering, Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan, Iran
2 Professor, Department of Nature Engineering, Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan, Iran
3 Associate Professor, Department of Nature Engineering, Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan, Iran
چکیده [English]

Extended Abstract
Introduction: Collected from sixty-six meteorological stations, the most recent data concerning the intensity-duration-frequency curve in Iran have been recorded in 1374. While rain gauges are required for physically obtaining such data, the collected data may be fraught with inaccuracies, considering the fact that the meteorological data of the extremely remote areas may be impossible to be gauged. Therefore, intensity-duration-frequency formulas are used to reduce the rate of such inaccuracies. While many formulas have so far been developed to be used for obtaining the required data regarding intensity-duration-frequency curves, they could only be applied to those areas whose regional rates have been estimated.
This study sought to identify and optimize such formulas’ rates together with the computational algorithms in the Urmia Lake’s basin. Then, the efficiency of the mass-fine computational algorithm and the genetic algorithm was measured and compared. Finally, the influence of climate change on intensity-duration-frequency diagrams in the basin was investigated using the nearest neighbor algorithm.
 
Materials and Methods: Considering the precipitation intensity data collected from Western Azerbaijan Province’s Regional Water Organization, first, the method for selecting the function of the precipitation’s intensity-duration-frequency was determined. Then, the fitting methods for the different return years’ functions of the intensity-duration were presented.
As for the extraction of IDF curves, the required data regarding the highest annual precipitation rates in 30-, 45-, and 60-minute durations were obtained from the statistics published by the Iranian Meteorological Organization. Then, the precipitation rate was investigated in twenty-seven meteorological stations of the Urmia Lake, and the intensity-duration-frequency curves were made accordingly. Taking the extracted curves into account, the regional coefficients of Abkhezr’s equation were measured and optimized based on the Genetic and Particles Swarm algorithms. Then, to determine the measurement accuracy, the daily precipitation rates were extracted from five sample meteorological stations in terms of 2-200 return years and millimeters per hour unit using the SMADA software. Finally, the IDF curves were made based on the optimized rates and Abkhezr formulas.
 
Results: The average rates of optimizing the coefficients of the Abkhezr formula with both algorithms mentioned above were found to be the same, bearing a very close prediction. Taking into account the average intensity and continuity rates of the IDF curves in terms of an integral diagram, the study undertook to compare the measurement of pre- and post-optimization IDF curves in five sample stations. Generally, the findings of this study indicated the accuracy of the Abkhezr Formula’s rates (with an acceptable closeness) in all meteorological stations of the Urmia Lake except for the Mahabad camp. It could therefore be argued that the average optimized regional rate are hundred percent fitted with the obtained rates.
 
Discussion and conclusion: The findings of the study suggested a high accuracy of the Abkhezr formula’s coefficients in all Urmia Lake’s meteorological stations (with acceptable closeness) except for the Mahabad camp, with the Siyah-Ceshmeh station having the best fitting rate. In this regard, the fitting rate of the coefficients in the above-mentioned stations differed merely at thousandth and ten-thousandth decimal rates. On the other hand, the pre-and post-optimization IDF curves were found to have slight differences, bearing an acceptable fitting. Moreover, the comparison of the integral curves of the IDF parameters indicates the great consistency between the results of this study and the formulas developed by Abkhezr and Bell. Furthermore, a good correlation was found between the results of this study and the research findings of Aghajani and Kerami, which were based on Sherman and Bernahu's formulas. As t and T in Bell's formula can have a local dependence in each area, such a dependence has been included in Abkheder's formula based on regional coefficients.
The results also showed that the PSO algorithm performed more efficiently in terms of optimization and its intended function is closer to zero, as it measures each particle with other neighboring particles, putting it in numerous cycles. However, the sensitivity analysis revealed a very slight difference between the two algorithms used in this study, suggesting that both algorithms could be applied.

کلیدواژه‌ها [English]

  • Optimization
  • Meteorological Stations
  • Formula
  • Precipitation Patterns
  • Regional Coefficients
AbdullahSaad Al, W., 2020. Intensity-duration-frequency curve derivation from different rain gauge records. Journal of King Saud University - Science 32, 3421-3431
Aghajani, N., and karami, H., 2015. Extraction of IDF curves from daily precipitation data of Mashhad synoptic station case study. Journal of Water and soil sciences 10, 30-37
Borji, H., How Takht Suleiman Lake is formed in the catchment area of Urmia Lake. 2015 "34 th Conference: International Specialized Congress of Science and Earth
Demaree, G., and Van de Vyver, H., 2013. Construction of intensity-duration-frequency (IDF) curves for precipitation with annual maxima data in Rwanda, Central Africa. Journal of Advances in Geosciences 35, 1-5
5. Dpaola, F., Giugni, M., Elena Topa, M., and Bucchignani, E., 2014. Intensity-Duration-Frequency (IDF) rainfall curves, for data series and climate projection in African cities. Journal of Earth and Environmental Sciences 3, 133
Eschelf, K., Kenneth, E., CaseyBrown, Y., Dennis, P., AnnaWagner, M., Thomas, R., R.Easterling, Kimberly., J.WangjBaptiste, F., and Eugene, Y., 2023. Incorporating non-stationarity from climate change into rainfall frequency and intensity-duration-frequency (IDF) curves. Journal of Hydrology 616, 128757
Ghahraman, B., and Sepaskhah, A., 1990. Estimation of the intensity-duration-frequency relationship of rainfall in Iran using ten-year one-hour rain "3 th International Congress of Road and Construction Engineering of Iran, Faculty of Engineering, Shiraz University
Ghahreman, Bizhan., and Abkhezr, H., 2004. Modifying the intensity-duration-frequency relationship of rainfall in Iran. Journal of Water and soil sciences 8, 1-13
9. Hongxiang, Y., Ning, S., Xiaodong, S., and Mark, S., 2020. Next-Generation Intensity-Duration-Frequency Curves for Climate-Resilient Infrastructure Design: Advances and Opportunities. Journal of Water and Built Environment 10, 3389
Juan Carlos, F., Neruda, R., and GermanHernandez, P., 2023. A genetic algorithm for multivariate missing data imputation. Information Sciences 616, 947-967
Kennedy, J., and Eberhart, R., 1995. Particle Swarm Optimization. Purdue School of Engineering and Technology. Journal of Purdue School of Engineering and Technology 46202-5160
12. Mailhot, A., Duchesne, S., Caya, D., and Talbot, G., 2007. Assessment of future change in intensity–duration–frequency (IDF) curves for Southern Quebec using the Canadian Regional Climate Model (CRCM). Journal of Hydrology 347, 1-2
13. Mottakan, A., Darvishzadeh, R., Hoseini asl, A., Ebrahimi khosefi, M., and Ebrahimi khosefi, Z., 2011. Drought risk zoning of dry areas using knowledge-based methods in GIS environment Case study: Shitoor basin, Yazd. Journal of Climatology research 2, 6-5
Muhammad Saiful, A., Zulkifli, Y., Fadhillah, Y., Zulfaqar, S., and Nor Eliza, A., 2020. Detecting Rainfall Trend and Development of Future Intensity Duration Frequency (IDF) Curve for the State of Kelantan. Journal of Water Resources Management34, 3165–3182
15. MuhammadNoor, T., Shamsuddin, S., Md, A., and Ashraf, D., 2021. Evaluating intensity-duration-frequency (IDF) curves of satellite-based precipitation datasets in Peninsular Malaysia. Journal of Atmospheric Research 248, 105-203
Qin,Y., Li, Zh., Ding, J., Zhao, F., and Meng, M., 2023. Automatic optimization model of transmission line based on GIS and genetic algorithm. Array 7, 100266
Safavi, H., Dadjoo, Sh., and Naimi, G., 2019. Extraction of intensity-duration-frequency (IDF) curves in climate change conditions, case study: Isfahan synoptic station. Journal of Iran's water resources research 15, 217-227
18. Taesoon, K., Ju-Young, S., Kewtae, K., and Jun-Haeng, H., 2008. Improving Accuracy of IDF Curves Using Long- and Short-Duration Separation and Multi-Objective Genetic Algorithm. Journal of World Environmental and Water Resources Congress 316, 128
Xing, Z., Zhu, J., Zhang, Z., Qin, Y., and Jia, L., 2022. Energy consumption optimization of tramway operation based on improved PSO algorithm. Journal of Energy 258, 124848
20. Yabin, S., Wendi, D., Kim, D., and Liong, S., 2019. Deriving intensity–duration–frequency (IDF) curves using downscaled in situ rainfall assimilated with remote sensing data. Journal of the Asia Oceania Geosciences Society 17