ارزیابی تناسب ارضی با استفاده از رویکردهای سنتی و مدلهای یادگیری ماشینی (مطالعه موردی: دشت آبیک، استان قزوین)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه علوم و مهندسی خاک، دانشکدگان کشاورزی و منابع طبیعی دانشگاه تهران، کرج، ایران

2 عضو هیأت علمی گروه مهندسی علوم خاک، پردیس کشاورزی و منابع طبیعی دانشگاه تهران

3 استاد گروه مهندسی ماشین‌های کشاورزی، پردیس کشاورزی ومنابع طبیعی دانشگاه تهران

چکیده

تناسب اراضی یک عامل اساسی در برنامه‌ریزی استفاده از اراضی و تولید پایدار محصولات کشاورزی است. ارزیابی تناسب اراضی به بهینه‌سازی استفاده از اراضی، ترویج استفاده پایدار از اراضی، حفاظت از محیط‌زیست و اطمینان از استفاده بهینه از منابع طبیعی کمک می‌کند. این تحقیق در منطقه آبیک استان قزوین واقع در شمال غرب ایران به وسعت 60 هزار هکتار انجام شده است، پس از جمع آوری داده‌ها از 300 خاکرخ و تعیین کلاس‌های تناسب زمین برای گندم با آبیاری سطحی با استفاده از سامانه طبقه بندی فائو، نقشه‌های رقومی به دو روش مرسوم و یادگیری ماشینی با استفاده از متغیرهای محیطی مستخرج از مدل رقومی ارتفاع، تصاویر ماهواره لندست-8 و سنتینل-2 بدست آمد. نتایج نشان داد که روش یادگیری ماشینی با دقت کلی 74 درصد و شاخص کاپای 68 توانست دقت بالاتری را نسبت به روش مرسوم با دقت کلی 62 درصد و شاخص کاپای 53 از خود نشان دهد. همچنین مهم ترین متغیرهای محیطی که در مدلسازی یادگیری ماشینی استفاده شدند متغیرهای مستخرج از مدل رقومی ارتفاع و ماهواره لندست-8 بود. بیشترین وسعت منطقه برای کشت گندم با آبیاری سطحی در کلاس نسبتاً مناسب (S2) با 30753 هکتار در روش جنگل‌های تصادفی و 21028 هکتار در روش سنتی بدست آمد و کمترین وسعت نیز متعلق به کلاس نامناسب (N) با 3052 هکتار در روش جنگل‌های تصادفی و 7185 هکتار در روش سنتی شناسایی شد. 15000 هکتار از منطقه مورد مطالعه نیز بدون محدودیت (S1)کشت برای گندم با آبیاری سطحی گزارش گردید.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Land suitability evaluation using traditional and machine learning approaches: a case study in abiek plain, Qazvin province, Iran

نویسندگان [English]

  • Seyyed Erfan Khamoshi 1
  • Fereydoon Sarmadian 2
  • Mahmoud Omid 3
1 Department of Soil Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.
2 soil science department< faculty of agricultural engineering and technology, university of Tehran
3 Department of Agricultural Machinery Engineering, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.
چکیده [English]

Land suitability is a crucial factor in land use planning and sustainable agricultural production. Evaluating land suitability helps optimize land use, promote sustainable land use, protect the environment, and ensure optimal use of natural resources. This study was conducted in the Abiek region of Qazvin province in northwest Iran, covering an area of 60,000 hectares. After collecting data from 300 soil profiles and determining land suitability classes for wheat cultivation with surface irrigation using the FAO classification system, digital elevation models, Landsat-8 and Sentinel-2 satellite images, and environmental variables extracted from the digital elevation model were used to create digital maps using both traditional and machine learning methods. The results showed that the machine learning method had a higher accuracy rate of 74% and a Kappa index of 68 compared to the traditional method with an accuracy rate of 62% and a Kappa index of 53. The most important environmental variables used in the machine learning model were those extracted from the digital elevation model and Landsat-8 satellite images. The largest area for wheat cultivation with surface irrigation was found in the relatively suitable class (S2), with 30,753 hectares in the random forest method and 21,028 hectares in the traditional method. In contrast, the smallest area belongs to the unsuitable class (N), with 3,052 hectares in the forest method. Additionally, random fields and 7185 hectares were identified in the traditional method. Also, 15,000 hectares of the study area are suitable for wheat cultivation without restrictions.

کلیدواژه‌ها [English]

  • Geomorphological characteristics
  • parametric method
  • Random Forests
  • Wheat

Land Suitability Evaluation Using Traditional and Machine Learning Approaches: A Case Study in Abiek Plain, Qazvin Province, Iran

EXTENDED ABSTRACT

Introduction:

Various factors such as rising food prices, economic factors, and climate change have led to crises in different parts of the world. To address these challenges, the world needs to create organizations and develop various programs to ensure food security and reduce food waste. One important program is increasing food production without increasing the cultivated area, which requires evaluating land suitability and identifying the main obstacles to achieving maximum crop performance in each region. Land suitability assessment is an essential step in optimizing and sustainable land use planning, especially in crop rotation planning. Traditional methods of soil mapping have limitations in representing the continuous nature of soil changes. Therefore, it is necessary to use different methods to better understand the variability of land suitability classes for sustainable land management. Machine learning techniques can be used to improve land suitability analysis. Data mining methods and digital mapping attempt to identify the environmental variables that are easily accessible, the features of the soil, and the land suitability classes. Therefore, this study aimed to evaluate land suitability for sustainable agricultural production in the Abiek region of Qazvin province in northwest Iran, covering an area of 60,000 hectares.

Methods:

The study collected data from 300 soil profiles and determined land suitability classes for wheat cultivation with surface irrigation using the FAO classification system. Digital elevation models, Landsat-8 and Sentinel-2 satellite images, and environmental variables extracted from the digital elevation model were used to create digital maps using both traditional and machine learning methods. The traditional method used the FAO classification system, while the machine learning method used a classification algorithm based on environmental variables. The accuracy of both methods was evaluated using a Kappa index and overall accuracy.

Results and Discussion:

The study found that the machine learning method had a higher accuracy rate of 74% and a Kappa index of 68 compared to the traditional method with an accuracy rate of 62% and a Kappa index of 53. The most important environmental variables used in the machine learning model were those extracted from the digital elevation model and Landsat-8 satellite images. The study identified the most suitable class (S2) for wheat cultivation with surface irrigation and the least suitable class (N) in terms of land suitability. The results of this study can be used to optimize land use, promote sustainable land use, protect the environment, and ensure optimal use of natural resources in the study area. The study identified the most suitable and least suitable classes for wheat cultivation with surface irrigation, which can be used to guide land use planning and agricultural production in the region. The use of machine learning methods for land suitability analysis can be further explored in future studies.

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تحقیقات آب و خاک ایران
دوره 55، شماره 2
اردیبهشت 1403
صفحه 269-283

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