نوع مقاله : پژوهشی

نویسندگان

• مهدی حسنلو ¹
• میثم جمشیدی ²
• محمدتقی ستاری ³

¹ دانشکده مهندسی نقشه برداری و اطلاعات مکانی پردیس دانشکده های فنی دانشگاه تهران

² دانشجوی کارشناسی ارشد هیدروگرافی دانشکده‌ی مهندسی نقشه‌برداری و اطلاعات مکانی، پردیس دانشکده‌های فنی، دانشگاه تهران، تهران، ایران.

³ ـ استادیار گروه مهندسی­ آب، دانشکده کشاورزی، دانشگاه تبریز. تبریز، ایران.

چکیده

چکیده
شوری آب مشخص‌کننده­ی پراکندگی حیات جانوری و گیاهی در دریاچه­ها، دریاها و اقیانوس­هاست. در این مقاله با مطالعه بازتابش از سطح آب، شاخص­های شوری و همچنین داده­های میدانی نقشه­ی شوری دریاچه­ی ارومیه تهیه گردید. سپس مدل­سازی شوری این دریاچه با استفاده از رگرسیون بردار پشتیبان و تصاویر لندست-8 انجام گردید. جهت انتخاب ویژگی­های مناسب از میان هفده ویژگی ورودی اولیه­ی مدل از دو الگوریتم ژنتیک و انتخاب ویژگی ترتیبی به کمک نرم­افزار متلب استفاده شد. در نهایت میزان شوری آب دریاچه­ی ارومیه با خطا و دقت نسبتاً مناسبی تخمین گردید. به­طوری­که مدل رگرسیون بردار پشتیبان با تمام ویژگی­ها با RMSE=24.55psu و R²=41%، مدل رگرسیون ­بردار پشتیبان مبتنی بر الگوریتم ژنتیک با RMSE=21.97psu و R²=54% و مدل رگرسیون ­بردار پشتیبان مبتنی بر انتخاب ویژگی ترتیبی با مقادیر RMSE=21.93psu و R²=53% توانستند میزان شوری دریاچه­ی ارومیه را تخمین بزنند.

تازه های تحقیق

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کلیدواژه‌ها

• کلمات کلیدی: شوری آب
• تصاویر لندست-8
• رگرسیون بردار پشتیبان
• الگوریتم ژنتیک

20.1001.1.23833254.1397.5.14.3.7

عنوان مقاله [English]

Urmia Lake Salinity Mapping Using Support Vector Regression and Landsat-8 Imagery

نویسندگان [English]

• Mahdi Hasanlou ¹
• Meysam Jamshidi ²
• Mohammad Taghi Sattari ³

¹ School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran

² M.SC Student, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran.

³ Assistant Professor, Department of Water Engineering, Faculty of Agriculture, University of Tabriz

چکیده [English]

Introduction
Urmia Lake is located in the North West of Iran and its area between 4750 to 6100 square kilometers at an altitude of 1250 meters above sea level. This lake is a permanent lake in Iran. In fact, Urmia Lake is one of the lowest parts of the catchment area North West of Iran. The total surface area of Urmia Lake is 51,876 km square, which is 3.15% of the total area of Iran and 7% of all the water’s surface in the country. The depth varies between 6 and 16 meters, the length of the lake is 50 km and its width varies between 128 km to 140 km.  In the catchment area of the lake, there is the main river with annual input about 2 billion cubic meters. Annual rainfall in the catchment area is variable between 200 and 300 mm. Air temperature the area around the lake in winter to 20°C and 40°C in summer increases. Urmia Lake is important in terms of economically, transport, exploitation of the mineral wealth of biodiversity, mitigating climate, and tourism. This unique Lake addition to the previous is habitat for kind of native artemia its name is urumiana artemia that this artemia is unique to this lake. Also, Urmia Lake is the world's second largest habitat for Artemia. According to the research, the main elements in the Urmia Lake include Cl^-, Na ⁺, Ca2⁺, Mg2⁺, HCo3^-, K⁺, Li, So42^- and F.
Methodology
In this study, newly launched Landsat series (Landsat-8) was used for monitoring Urmia Lake salinity and retrieving the salinity map. By incorporating the Landsat-8 datasets, this study determined the salinity changes and created a model to estimate the salinity in Urmia Lake with processing Landsat-8 satellite images as a result; we can obtain salinity map regularly without ground operations. We can also monitor the health of the habitat in terms of salinity and examine the impact of increasing salinity on the plants, animals, and ecosystems of the region. This study applied remote sensing techniques to develop a salinity prediction model for Urmia Lake. In this study, we use Landsat-8 satellite images radiances of Urmia Lake and some salinity indices and in-situ data so we have 17 features to make water surface salinity model with support vector regression (SVR) with all features. After that, we use two algorithms; GA and SFS for selecting suitable features and make models with those features.
Result
Results with all features model show RMSE=24.55 and R²=41% and result with GA feature selection model shows RMSE=21.97 and R²=54% and results with SFS feature selection model shows RMSE=21.93 and R²=53%.
 
Discussion and Conclusion
Satellite images show that from 1995 to 2003, the lake water surface dropped and proportionate to the dropping water salinity increased to 220 to 300 grams per liter. Also although Artemia is resistant to salt, appropriate salinity is below 100 grams per liter. When water salt is more than 100 grams per liter contents of his tiny body lost and die. Now because of reduction in salinity, the lake has arrived at about 300 grams per liter. Dissolved salt in water has a direct effect on the electrical conductivity of water. In this regard, incorporating high spatial resolution satellite like Landsat-8 images is inevitable. Also, the proposed modeling methods show these changes in multi-data and in widespread Urmia Lake very well.

کلیدواژه‌ها [English]

• Keyword: Water salinity modeling
• Urmia lake
• remote sensing
• Landsat-8
• SVR
• GA
• SFS