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ارزیابی مدل‌های یادگیری ماشین در پیش‌بینی شاخص‌های خشک‌سالی( مطالعۀ موردی: محدودۀ عجب‌شیر) | ||
| اکوهیدرولوژی | ||
| دوره 10، شماره 3، مهر 1402، صفحه 405-419 اصل مقاله (1.55 M) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ije.2023.364229.1754 | ||
| نویسندگان | ||
| مهتاب فرامرزپور1؛ علی صارمی* 2؛ امیر خسروجردی3؛ حسین بابازاده4 | ||
| 1دانشجوی دکتری، مهندسی و مدیریت منابع آب، گروه آبیاری، دانشکدۀ علوم کشاورزی و صنایع غذایی، دانشگاه آزاد اسلامی، واحد تهران مرکزی، تهران | ||
| 2استادیار، مهندسی و مدیریت منابع آب، گروه آبیاری، دانشکدۀ علوم کشاورزی و صنایع غذایی، دانشگاه آزاد اسلامی، واحد تهران مرکزی، تهران | ||
| 3استادیار، مهندسی و مدیریت منابع آب، گروه علوم و مهندسی آب، دانشکدۀ علوم کشاورزی و صنایع غذایی، دانشگاه آزاد اسلامی، واحد تهران مرکزی، تهران | ||
| 4استاد، مهندسی و مدیریت منابع آب، علوم و مهندسی آب، دانشکدۀ علوم کشاورزی و صنایع غذایی، دانشگاه آزاد اسلامی، واحد تهران مرکزی، تهران | ||
| 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 | ||
| پیش‌بینی؛ عجب‌شیر؛ یادگیری ماشین؛ SPI؛ SDI | ||
| عنوان مقاله [English] | ||
| Evaluation of machine learning models in predicting drought indicators (Case Study: Ajabshir area) | ||
| نویسندگان [English] | ||
| Mahtab Faramarzpour1؛ Ali Saremi2؛ Amir Khosrojerdi3؛ Hossain Babazadeh4 | ||
| 1PhD Student, Water Resources Engineering and Management, Irrigation Department, Faculty of Agricultural Sciences and Food Industry, Islamic Azad University, Central Tehran Branch, Tehran | ||
| 2Assistant Professor, Water Resources Engineering and Management, Irrigation Department, Faculty of Agricultural Sciences and Food Industry, Islamic Azad University, Central Tehran Branch, Tehran | ||
| 3Assistant 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 | ||
| 4Professor, 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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