ارزیابی مدل‌های یادگیری ماشین در پیش‌بینی شاخص‌های خشک‌سالی( مطالعۀ موردی: محدودۀ عجب‌شیر)

نوع مقاله : پژوهشی

نویسندگان

1 دانشجوی دکتری، مهندسی و مدیریت منابع آب، گروه آبیاری، دانشکدۀ علوم کشاورزی و صنایع غذایی، دانشگاه آزاد اسلامی، واحد تهران مرکزی، تهران

2 استادیار، مهندسی و مدیریت منابع آب، گروه آبیاری، دانشکدۀ علوم کشاورزی و صنایع غذایی، دانشگاه آزاد اسلامی، واحد تهران مرکزی، تهران

3 استادیار، مهندسی و مدیریت منابع آب، گروه علوم و مهندسی آب، دانشکدۀ علوم کشاورزی و صنایع غذایی، دانشگاه آزاد اسلامی، واحد تهران مرکزی، تهران

4 استاد، مهندسی و مدیریت منابع آب، علوم و مهندسی آب، دانشکدۀ علوم کشاورزی و صنایع غذایی، دانشگاه آزاد اسلامی، واحد تهران مرکزی، تهران

10.22059/ije.2023.364229.1754

چکیده

خشک‏سالی یکی از پدیده‏های مخرب است که می‏تواند تأثیرات منفی زیادی بر منابع آب و نیازهای آبی بگذارد. مدل‏های یادگیری ماشین یکی از ابزارهای سودمند در پیش‏بینی‏های سری زمانی هستند که می‏توانند پیش‏بینی مناسبی بدون داشتن اطلاعات اساسی از یک سامانه ارائه دهند. بنابراین، در این تحقیق از مدل‏های شبکۀ عصبی‌ فازی (ANFIS) و حداقل مربعات رگرسیون بردار پشتیبان (LSSVR) برای پیش‏بینی شاخص خشک‏سالی هواشناسی (SPI) و شاخص خشک‏سالی هیدرولوژیکی (SDI) برای یک دوره (1380-1398) استفاده شد. از ایستگاه‏های هواشناسی و هیدرولوژیکی آجی‏چای در محدودۀ مطالعاتی عجب‏شیر به‌ترتیب برای محاسبۀ شاخص‏های خشک‏سالی SPI و SDI استفاده شد. به منظور پیش‏بینی شاخص SPI داده‏های بارش و برای شاخص SDI داده‏های دبی به ‏عنوان پارامترهای ورودی به مدل‏ها در نظر گرفته شدند. نتایج شاخص‏های خشک‏سالی نشان داد طی دورۀ مورد بررسی، طی سال‏های 1385-1390 خشک‏سالی هواشناسی و از 1386 تا 1390 خشک‏سالی هیدرولوژیکی شدیدتر بوده است (SPI<-3). نتایج پیش‏بینی شاخص‏ها نیز نشان داد عملکرد مدل LS-SVR بهتر از ANFIS در هر دو شاخص بوده است. LS-SVR با شاخص ارزیابی خطای RMSE و MAPE برای SPI به‌ترتیب 74/0 و 59/0 پیش‏بینی کرد که این مقادیر برای SDI به‌ترتیب 62/0 و 46/0 به دست آمد. نتایج این تحقیق نشان داد مدل‏های یادگیری ماشین ابزار مناسبی برای پیش‏بینی شاخص‏های خشک‏سالی هستند. لذا استفاده از آن‏ها برای پیش‏بینی شاخص‏های خشک‏سالی در سایر محدوده‏های مشابه پیشنهاد می‏شود.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Evaluation of machine learning models in predicting drought indicators (Case Study: Ajabshir area)

نویسندگان [English]

  • Mahtab Faramarzpour 1
  • Ali Saremi 2
  • Amir Khosrojerdi 3
  • Hossain Babazadeh 4
1 PhD Student, Water Resources Engineering and Management, Irrigation Department, Faculty of Agricultural Sciences and Food Industry, Islamic Azad University, Central Tehran Branch, Tehran
2 Assistant Professor, Water Resources Engineering and Management, Irrigation Department, Faculty of Agricultural Sciences and Food Industry, Islamic Azad University, Central Tehran Branch, Tehran
3 Assistant Professor, Water Resources Engineering and Management, Department of Water Science and Engineering, Faculty of Agricultural Sciences and Food Industry, Islamic Azad University, Central Tehran Branch, Tehran
4 Professor, Water Resources Engineering and Management, Water Science and Engineering, Faculty of Agricultural Sciences and Food Industry, Islamic Azad University, Central Tehran Branch, Tehran
چکیده [English]

Drought is one of the destructive phenomena with adverse impacts on water resources and water needs. Machine-learning models are among the helpful tools in time-series prediction that can provide suitable results without the requirements for basic information about a system. In this study, adaptive neuro-fuzzy inference system (ANFIS) and least square support vector regression (LSSVR) models were utilized to predict the standardized precipitation index (SPI) as a meteorological drought indicator and streamflow drought index (SDI) as a hydrological drought indicator for a period (2001-2019). Ajabshir, located in the northwest of Iran, was selected as the study area, where the data of Qaleh Chay meteorological and hydrological stations were used to calculate SPI and SDI, respectively. The precipitation and flow rate data were considered input variables of the machine-learning models in predicting the SPI and SDI, respectively. The results revealed that during the period under review, meteorological drought was more severe in 2004-2011. While in this period, hydrological drought was more severe in 2007-2011 (SPI<-3). Moreover, the prediction results of the indices showed that the performance of the LSSVR model was better than that of ANFIS for both indicators. Using LSSVR, the RMSE and MAPE error evaluation criteria for SPI were 0.74 and 0.59, respectively, while these values for SDI were obtained as 0.62 and 0.46, respectively. The findings of this study show that machine-learning models are suitable tools for predicting drought indicators. Therefore, it is suggested to use such models in predicting drought indicators in other similar regions.

کلیدواژه‌ها [English]

  • Prediction
  • Ajab Shir
  • Machine Learning
  • SPI
  • SDI

مراجع

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اکوهیدرولوژی
دوره 10، شماره 3
مهر 1402
صفحه 405-419

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  • تاریخ دریافت: 22 اردیبهشت 1402
  • تاریخ بازنگری: 22 خرداد 1402
  • تاریخ پذیرش: 22 تیر 1402

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