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پتانسیل‌سنجی خشکسالی با استفاده از شاخص‌های سنجش از دور و شبکۀ عصبی مصنوعی (مطالعۀ موردی: استان کرمانشاه) | ||
| اکوهیدرولوژی | ||
| دوره 9، شماره 2، تیر 1401، صفحه 387-402 اصل مقاله (2.2 M) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ije.2022.339610.1613 | ||
| نویسندگان | ||
| نادیا عباس زاده طهرانی1؛ میلاد جانعلی پور* 2؛ میلاد باقری2 | ||
| 1استادیار، گروه کاربرد هوافضا در محیط زیست، مرکز زیست فضا و محیط زیست، پژوهشگاه هوافضا، وزارت علوم، تحقیقات و فناوری | ||
| 2پژوهشگر، گروه کاربرد هوافضا در محیط زیست، مرکز زیست فضا و محیط زیست، پژوهشگاه هوافضا، وزارت علوم، تحقیقات و فناوری | ||
| چکیده | ||
| خشکسالی، یک دورۀ ممتد کمبود بارش است که موجب بروز خسارت و کاهش عملکرد در محصولات زراعی میشود و بر کیفیت و کمیت منابع آب و کشاورزی منطقه تأثیر مستقیم دارد. فناوری سنجش از دور با تلفیق سیستم اطلاعات جغرافیایی (ساج) و با ایجاد امکان دسترسی، پردازش و تفسیر دادههای مکانی، امکان پایش مطلوبتر خشکسالی را فراهم کرده است. هدف این مطالعه، تعیین مناسبترین شاخص‌های سنجش از دوری و ارائۀ یک نمایۀ ترکیبی مبتنی بر روش هوشمند شبکۀ عصبی مصنوعی و درنهایت، استخراج نقشۀ پتانسیل خشکسالی است. بر اساس نتایج، بهترین شاخص‌های سنجش از دور جهت تعیین میزان ریسک بیابانزایی در منطقه، شاخص‌های پوشش گیاهی، میزان بارش و دمای سطح زمین هستند. نتایج بر اساس مقادیر شاخص بارش استانداردشده (SPI) بهدستآمده از ایستگاههای هواشناسی ارزیابی شدند. بر این اساس، مقدار دقت نتایج روش رگرسیون چندمتغیره، R2=0.62 و شبکۀ عصبی پرسپترون چندلایه R2=0.91 به دست آمد. بنابراین، روش شبکۀ عصبی پرسپترون چندلایه از روش رگرسیون چندمتغیره برای ایجاد یک شاخص ترکیبی خشکسالی توانایی بسیار بیشتری دارد. بر اساس نتایج، در بیشتر مناطق استان کرمانشاه پتانسیل بروز خشکسالی ماهانه وجود دارد. همچنین، پتانسیل خشکسالی سالانه، در مناطق شرقی استان بیشتر مشاهده میشود. اسلامآباد، سنقر و تا حدودی هرسین، شهرستانهای دارای خطر پایین وقوع خشکسالی هستند. | ||
| کلیدواژه‌ها | ||
| پایش خشکسالی؛ شبکۀ عصبی؛ سنجش از دور؛ محیط زیست؛ تصاویر ماهواره ای | ||
| عنوان مقاله [English] | ||
| Drought Potential Mapping using Remote Sensing Indices and Artificial Neural Networks (Case study: Kermanshah Province, Iran) | ||
| نویسندگان [English] | ||
| Nadia Abbaszadeh Tehrani1؛ Milad Janalipour2؛ Milad Bagheri2 | ||
| 1Aerospace Research Institute, Ministry of Science, Research and Technology, Tehran, Iran | ||
| 2Aerospace Research Institute, Ministry of Science, Research and Technology, Tehran, Iran | ||
| چکیده [English] | ||
| Drought is a continuous period of lack of rainfall that causes damage and reduced yields in crops and has a direct impact on the quality and quantity of water and agricultural resources in the region. Remote sensing technology is Geographic Information Systems (GIS) are useful tools for processing and interpretation of spatial data and can be used for drought monitoring. The purpose of this study is to determine the most appropriate remote sensing indicators and to present a composite drought index based on the intelligent artificial neural network method. Based on the results, the best remote sensing indicators to determine the risk of drought in the region are vegetation indices, rainfall, and land surface temperature. The results were evaluated based on standardized precipitation index (SPI) values obtained from meteorological stations. Accordingly, the accuracy of the results of the multivariate regression method was R2 = 0.62 and the multilayer perceptron neural network was R2 = 0.91. Therefore, the multilayer perceptron neural network method is more robust than the multivariate regression method to create a more accurate hybrid drought index. According to the results, there is a potential for monthly drought in most areas of Kermanshah province. Moreover, the annual drought potential is observed in the eastern regions of the province. Islamabad, Songor, and Harsin are cities with a low risk of drought. | ||
| کلیدواژه‌ها [English] | ||
| Environment, Remote sensing, artificial neural network, satellite imagery, drought index | ||
| مراجع | ||
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