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کاربرد هوش مصنوعی در تهیه نقشه خطر فرسایش آبکند در شهرستان الشتر، استان لرستان | ||
| مهندسی و مدیریت آبخیز | ||
| دوره 18، شماره 2، تیر 1405، صفحه 237-256 اصل مقاله (1.64 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/ijwmse.2026.371373.2139 | ||
| نویسندگان | ||
| نگار ارجمند1؛ علیرضا سپهوند* 2؛ امید رحمتی3 | ||
| 1دانشجوی دکتری، گروه مهندسی مرتع و آبخیزداری، دانشکده منابع طبیعی، دانشگاه لرستان، خرم آباد، ایران | ||
| 2دانشیار، گروه مهندسی مرتع و آبخیزداری، دانشکده منابع طبیعی، دانشگاه لرستان، خرم آباد، ایران | ||
| 3استادیار، بخش تحقیقات حفاظت خاک و آبخیزداری، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی، سازمان تحقیقات آموزش و ترویج کشاورزی کردستان، سنندج، ایران | ||
| چکیده | ||
| مقدمه امروزه مطالعه فرسایش خندقی بهدلیل تولید رسوب و خسارات متعدد در نتیجه فعالیتهای انسانی از اهمیت بالایی برخوردار است. در مطالعات انجام شده عوامل طبیعی زیادی بهعنوان عوامل کنترل کننده فرسایش آبکندی گزارش شدهاند که ازجمله میتوان به عوامل زمین-محیطی که شرایط بحرانی برای وقوع و توسعه فرسایش آبکندی را کنترل میکنند و در درجه اول با توپوگرافی، سنگشناسی، بارندگی، خاک و کاربری اراضی مرتبط هستند اشاره کرد. شناسایی عوامل مؤثر در وقوع فرسایش آبکندی و پهنهبندی آن یکی از ابزارهای اساسی و مهم برای کنترل و مدیریت این پدیده است. فرسایش آبکندی یک خطر مهم برای وضعیت اکوسیستمها در سراسر جهان است، در نتیجه توسعه نقشههای حساسیت فرسایش آبکندی ضروری است. بنابراین این پژوهش بهمنظور پهنهبندی حساسیت وقوع فرسایش آبکندی با استفاده از مدلهای هوش مصنوعی از نوع شبکه عصبی مصنوعی (MLP)، حداکثرآنتروپی (MaxEnt)، تحلیل تفکیککنندهانعطافپذیر (FDA)، در شهرستان الشتر انجام شد. مواد و روشها منطقه مورد مطالعه از نظر ژئومورفولوژیکی در بخش مرکزی رشتهکوه زاگرس در استان لرستان قرار دارد که خود بخشی از زیرحوضه کرخه است. شهرستان الشتر با مساحت 1523.55 کیلومتر مربع بین طولهای جغرافیایی”38 ΄03 ْ48 تا ”52 ΄30 48 شرقی و عرضهای ”37 ΄44 33 تا ”21΄01 34 شمالی قرار دارد. میانگین بارندگی حوضه حدود 570 میلیمتر و دارای اقلیم نیمهخشک و سرد است. برای پهنهبندی حساسیت به وقوع فرسایش خندقی با استفاده از مدلهای هوش مصنوعی و تعیین بهترین مدل، از مدلهای شبکه عصبی مصنوعی (MLP) حداکثرآنتروپی (MaxEnt)، تحلیل تفکیککنندهانعطافپذیر (FDA)، استفاده شد. در این تحقیق از 12 عامل شیب، جهت شیب، بارش، فاصله از جاده، فاصله از رودخانه، فاصله از گسل، خاک، کاربری اراضی، سازند زمینشناسی، شاخص رطوبت توپوگرافی (TWI)، شاخص موقعیت توپوگرافی TPI و شاخص پوشش گیاهی NDVI به عنوان پارامترهای ورودی و نقاط خندقی و غیرخندقی بهعنوان پارامترهای خروجی برای مدلسازی و پهنهبندی حساسیت به وقوع خندق استفاده شد. سپس از مجموع 151 نقطه وقوع و عدم وقوع خندق (89 خندق و 62 غیر خندق)، 70 درصد بهعنوان دادههای آموزش و 30 درصد برای مرحله اعتبارسنجی استفاده شد. بهمنظور ارزیابی مدلها، از منحنی تشخیص عملکرد نسبی (ROC) برای قدرت پیشبینی مدلها استفاده شد. نتایج و بحث نتایج نشان داد که مدل MLP با مقادیر AUC برابر با 0.98 در مرحله آموزش و 0.92 در مرحله اعتبارسنجی، بهترین عملکرد را در پیشبینی حساسیت وقوع فرسایش آبکندی دارد. پس از آن بهترتیب مدلهای ( 0.87=AUC) FDA و (0.5=AUC) MaxEnt قرار دارند. تحلیل عوامل مؤثر نشان داد که بیشترین آبکندها در طبقات بارشی ۷۰۰-۶۰۰ میلیمتر، فواصل بیش از ۳۰۰ متر از گسل، جاده و رودخانه، شیبهای ۰-۵ و ۵-۱۵ درصد، جهتهای شمالی، کاربری کشاورزی دیم و سازندهای آبرفت قدیم و مارنها واقع شدهاند. همچنین بین شاخصهای TWI و NDVI بهترتیب رابطه مستقیم و معکوس با وقوع آبکند مشاهده شد. در نهایت، نقشه نهایی پهنهبندی حساسیت فرسایش آبکندی با استفاده از مدل برتر MLP تهیه شد. نتیجهگیری با توجه به اینکه فرسایش آبکندی یکی از شکلهای پیشرفته فرسایش آبی است، شناسایی عوامل مؤثر و پهنهبندی آن برای کنترل و مدیریت این پدیده حائز اهمیت است. این مطالعه با هدف شناسایی عوامل مهم و تأثیرگذار در فرسایش آبکندی و ایجاد مدلهای یادگیری ماشین برای پهنهبندی حساسیت وقوع به فرسایش آبکندی در شهرستان الشتر انجام شد. نتایج نشان داد که مدل شبکه عصبی مصنوعی (MLP) بهترین عملکرد را با توجه به معیار ارزیابی مدل (0.92=AUC) کسب کرده است و پس از آن بهترتیب مدلهای ( 0.87=AUC) FDA و ( 0.5=AUC) MaxEnt قرار دارند. نتایج بهدستآمده از این پژوهش، دیدگاه مناسبی را در مورد تأثیر عوامل مؤثر در ایجاد فرسایش خندقی در اختیار برنامهریزان و محققان قرار میدهد. در نتیجه با انجام تحقیقات بیشتر میتوان استفاده از سایر تکنیکهای یادگیری ماشین را بررسی نمود و دیگر عوامل مؤثر را برای بهبود دقت مدلهای پیشبینی فرسایش خندقی در نظر گرفت. | ||
| کلیدواژهها | ||
| استان لرستان؛ پهنهبندی وقوع فرسایش آبکندی؛ مدلسازی؛ مدل FDA؛ یادگیری ماشین | ||
| عنوان مقاله [English] | ||
| Application of artificial intelligence in preparing gully erosion susceptibility maps in the Alshater County, Lorestan Province | ||
| نویسندگان [English] | ||
| Negar Arjmand1؛ Alireza Sepahvand2؛ Omid Rahmati3 | ||
| 1Ph.D. Student, Department of Range and Watershed Management, Faculty of Natural Resources, Lorestan University, Khorramabad, Lorestan Province, Iran | ||
| 2Associate Professor, Department of Range and Watershed Management Engineering, Faculty of Natural Resources, Lorestan University, Khorramabad, Lorestan Province, Iran | ||
| 3Assistant Professor, Soil Conservation and Watershed Management Research Department, Agricultural and Natural Resources Research and Education Center, Kurdistan Agricultural Research, Education and Extension Organization, Kurdistan, Iran | ||
| چکیده [English] | ||
| Introduction Today, the study of gully erosion is of great importance due to sediment production and numerous damages resulting from human activities. Many natural factors have been reported as controlling factors of gully erosion. Among the geo-environmental factors that control the critical conditions for the occurrence and development of gully erosion, primarily related to topography, lithology, rainfall, soil, and land use, other studies have stated that surface runoff is one of the main factors affecting the occurrence of gully erosion. Identifying the factors affecting the occurrence of gully erosion and its zoning is one of the essential and important tools for controlling and managing this phenomenon. Gully erosion is a significant threat to the state of ecosystems worldwide; consequently, developing gully erosion susceptibility maps is essential. Therefore, this research was conducted to mapping the susceptibility of gully erosion occurrence using artificial intelligence models of the Multilayer Perceptron (MLP) neural network, Maximum Entropy (MaxEnt), and Flexible Discriminant Analysis (FDA) in Al-Shatr city. Materials and methods The study area is located in the central part of the Zagros mountain range in Lorestan Province, Iran, within the Karkheh sub-basin. Alashtar County covers an area of 1,523.55 km², situated between longitudes 48°30′52″E to 48°03′38″E and latitudes 34°01′21″N to 33°44′37″N. The basin has an average annual precipitation of approximately 570 mm and features a semi-arid, cold climate. To map gully erosion susceptibility and identify the most effective model, three artificial intelligence models were employed: Multi-Layer Perceptron (MLP), Maximum Entropy (MaxEnt), and Flexible Discriminant Analysis (FDA). Twelve environmental factors were used as input variables: slope, aspect, precipitation, distance from roads, distance from rivers, distance from faults, soil type, land use, geological formation, Topographic Wetness Index (TWI), Topographic Position Index (TPI), and Normalized Difference Vegetation Index (NDVI). The output variables consisted of gully and non-gully points. A total of 151 points (89 gully and 62 non-gully locations) were collected. Seventy percent of the data were used for model training, while the remaining 30% were reserved for validation. Model performance was evaluated using the Relative Operating Characteristic (ROC) curve to assess predictive accuracy. Results and discussion The results showed that the MLP model, with AUC values of 0.98 in the training phase and 0.92 in the validation phase, had the best performance in predicting gully erosion susceptibility. This was followed by the FDA (AUC = 0.87) and MaxEnt (AUC = 0.5) models, respectively. Analysis of the influencing factors revealed that most gullies were located in precipitation classes of 600-700 mm, distances greater than 300 meters from faults, roads, and rivers, slope classes of 0-5% and 5-15%, northern aspects, dry farming land use, and geological formations of old alluvium and marls. Furthermore, a direct relationship was observed between the TWI index and gully occurrence, while an inverse relationship was found for the NDVI index. Finally, the gully erosion map was prepared using the MLP model. Conclusions Given that gully erosion is an advanced form of water erosion, identifying its driving factors and mapping its susceptibility are crucial for effective control and management. This study aimed to identify key factors influencing gully erosion and develop machine learning models to map susceptibility in Shastar County. Results indicate that the Artificial Neural Network (MLP) model performed best, achieving an AUC of 0.92, followed by FDA (AUC = 0.87) and MaxEnt (AUC = 0.50). These findings provide valuable insights for planners and researchers regarding the factors driving gully erosion. Future research should explore additional machine learning techniques and incorporate other influential factors to further improve prediction accuracy. | ||
| کلیدواژهها [English] | ||
| FDA Model, Lorestan province, Machine Learning, Mapping of gully erosion, Modeling | ||
| مراجع | ||
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