نشریه علمی - پژوهشی مرتع و آبخیزداری

نشریه علمی - پژوهشی مرتع و آبخیزداری

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نقشه‌برداری رقومی هدایت هیدرولیکی اشباع خاک در حوزه آبخیز کیلانه، استان کردستان

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه شهید چمران اهواز، اهواز، ایران

2 گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه کردستان، سنندج، ایران

3 گروه علوم زمین، علوم خاک و ژئومرفولوژی، دانشگاه توبینگن، توبینگن، آلمان

10.22059/jrwm.2025.389637.1801

چکیده

هدایت هیدرولیکی اشباع خاک سطحی (Ks) به‌عنوان یکی از مهم‌ترین ویژگی‌های فیزیکی خاک، نقشی کلیدی در توزیع آب و مواد مغذی در محیط خاک ایفا می‌کند و در مدیریت منابع آب و خاک اهمیت ویژه‌ای دارد. این پژوهش با هدف مدل‌سازی رقومی هدایت هیدرولیکی اشباع خاک سطحی با استفاده از رویکردهای یادگیری ماشین در حوزه آبخیز کیلانه واقع در استان کردستان با مساحت 12 هزار هکتار انجام شد. سه الگوریتم یادگیری ماشین شامل: درخت تصمیم تقویت شده با گرادیان (XGBoost)، جنگل تصادفی (RF) و مدل نزدیکترین -k همسایگی (k-NN) با بهره‌گیری از تعدادی متغیرهای محیطی از مدل رقومی ارتفاع و تصاویر ماهواره سنتینل-2 شامل فاصله از کانال آبراهه، عمق دره، موقعیت نسبی شیب، سطح پایه کانال آبراهه، شاخص روشنایی، شاخص اثر باد، شاخص نرمال شده تفاوت پوشش گیاهی، باند 12، شاخص سبزینگی، انحنای سطح و پارامترهای خاک شامل ماده آلی، آهک، جرم مخصوص ظاهری، میانگین هندسی قطر ذرات، بافت خاک و داده‌های طیف سنجی نزدیک خاک در طول موج 2450-400 نانومتر به عنوان نمایندگان عوامل خاکسازی برای مدل‌سازی Ks مورد استفاده قرار گرفتند. نتایج نشان داد که مدل XGBoost برای پیش‌بینی Ks با R2 برابر 65/0 و nRMSE برابر 25/0 نسبت به سایر مدل‌ها دارای صحت بالاتری بودند. داده‌های طیفی، متغیرهای توپوگرافی و پارامترهای خاک، به‌عنوان ورودی مدل، نقش مهمی در پیش‌بینی تغییرپذیری مکانی Ks داشتند و مدل XGBoost با استفاده از این داده‌ها توانست پیش‌بینی دقیقی ارائه دهد. نتایج نشان داد که Ks تحت تأثیر متغیرهای توپوگرافی، فیزیکی و طیفی قرار دارد؛ ماده آلی، بافت خاک و شاخص‌های توپوگرافی مانند شیب و موقعیت نسبی بیشترین تأثیر را داشتند. نقشه‌های تولیدشده از این رویکرد تغییرپذیری مکانی می‌توانند در مدیریت منابع آب و خاک و مدل‌های هیدرولوژیکی مورد استفاده قرار گیرند.

کلیدواژه‌ها

عنوان مقاله [English]

Digital Mapping of Soil Saturated Hydraulic Conductivity in the Kilanah Watershed, Kurdistan

نویسندگان [English]

  • Farzaneh Parsaie 1
  • Ahmad Farrokhian Firouzi 1
  • Masoud Davari 2
  • Ruhollah Taghizadeh-Mehrjardi 3

1 Department of Soil Science and Engineering, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran

2 Department of Soil Science, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran

3 Department of Geosciences, Soil Science and Geomorphology, University of Tübingen, Tübingen, Germany

چکیده [English]

Surface soil saturated hydraulic conductivity (Ks), as one of the most important physical properties of soil, plays a key role in the distribution of water and nutrients within the soil environment and holds particular significance in water and soil resource management. This study aimed to digitally model Ks using machine learning approaches in the Kilanah watershed, located in Kurdistan Province, covering an area of 12,000 hectares. Three machine learning algorithms, including Gradient Boosted Decision Tree (XGBoost), Random Forest (RF), and k-Nearest Neighbors (k-NN), were utilized, incorporating various environmental variables derived from the digital elevation model and Sentinel-2 satellite imagery. These variables included distance from the drainage channel, valley depth, relative slope position, channel base level, brightness index, wind effect index, Normalized Difference Vegetation Index (NDVI), Band 12, greenness index, and surface curvature. Additionally, soil parameters such as organic matter, lime content, bulk density, geometric mean particle diameter, soil texture, and near-soil spectroscopic data (Latent Variable) within the wavelength range of 400–2450 nm were used as proxies for pedogenic factors to model saturated hydraulic conductivity. The results indicated that the XGBoost model exhibited the highest accuracy for predicting Ks, with an R² value of 0.65 and an nRMSE of 0.25, outperforming the other models. Spectral data, topographic variables, and soil parameters, as model inputs, played a significant role in predicting the spatial variability of Ks. The XGBoost model was able to provide highly accurate predictions. The results demonstrated that topographic, physical, and spectral variables influence Ks; organic matter, soil texture, and topographic indices such as slope and relative position had the most substantial impact. The generated maps can be utilized for water and soil resource management and hydrological models.

کلیدواژه‌ها [English]

  • environmental variables
  • machine learning
  • Spatial variability
  • soil sensing
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نشریه علمی - پژوهشی مرتع و آبخیزداری
دوره 78، شماره 3
مهر 1404
صفحه 323-340
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  • تاریخ دریافت: 10 بهمن 1403
  • تاریخ بازنگری: 24 اسفند 1403
  • تاریخ پذیرش: 16 فروردین 1404
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