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رویکردی نوین در تخمین ضریب زبری مانینگ در فازهای مختلف آبیاری جویچهای با بهرهگیری از پردازش تصویر و یادگیری ماشین | ||
تحقیقات آب و خاک ایران | ||
دوره 56، شماره 4، تیر 1404، صفحه 1011-1039 اصل مقاله (2.3 M) | ||
نوع مقاله: مقاله پژوهشی | ||
شناسه دیجیتال (DOI): 10.22059/ijswr.2025.387670.669860 | ||
نویسندگان | ||
هادی رضایی راد* 1؛ حامد ابراهیمیان2؛ عبدالمجید لیاقت2؛ محمود امید3؛ نیما تیموری4 | ||
1پژوهشکده کشاورزی هسته ای، پژوهشگاه علوم و فنون هسته ای، سازمان انرژی اتمی، کرج- ایران | ||
2گروه آبیاری و آبادانی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران | ||
3گروه مهندسی ماشینهای کشاورزی، پردیس کشاورزی ومنابع طبیعی، دانشگاه تهران، کرج، ایران | ||
4گروه پردازش سیگنال، دانشکده مهندسی کامپیوتر و برق، دانشگاه آرهوس، آرهوس، دانمارک | ||
چکیده | ||
این تحقیق به بررسی کارایی استفاده تلفیقی از تکنیکهای پردازش تصویر و روشهای یادگیری ماشین برای تخمین ضریب زبری مانینگ در آبیاری جویچهای در فازهای پیشروی و ذخیره پرداخته است. برای این منظور، مقادیر مختلف دبی ورودی، نوبت، مرحله و دورهای متفاوت آبیاری در دو نوع بافت خاک در نظر گرفته شد. تصاویری از سطح جویچهها قبل و بعد از هر آبیاری ثبت گردید و ضریب زبری در فازهای پیشروی و ذخیره به ترتیب با استفاده از مدل SIPAR_ID و معادله مانینگ تخمین زده شد. سپس با استفاده از این دادهها، الگوریتمی بر مبنای استفاده تلفیقی از تکنیکهای پردازش تصویر و روشهای یادگیری ماشین در سه سناریوی مختلف توسعه یافت. نتایج نشان داد که الگوریتم با استفاده از تصاویر یا دادههای مزرعهای بهصورت مجزا نمیتواند بهدرستی آموزش ببیند و دقت بسیار پایینی دارد؛ چراکه برخی از ویژگیها صرفاً از تصاویر و برخی دیگر از دادههای مزرعهای قابلدسترسی هستند. نتایج همچنین بیانگر، دقت بسیار مناسب الگوریتم در تخمین ضریب زبری مانینگ در فازهای پیشروی و ذخیره با استفاده از تلفیق تصاویر و برخی دادههای مزرعهای نظیر سطح مقطع جریان و دبی، بود. در سناریوی منتخب، روش جنگل تصادفی و CART با شاخصهای precision، recall و F1-score برابر با ۹۵، ۹۶ و ۹۵ درصد، بهترین عملکرد را در تخمین ضریب زبری مانینگ نسبت به دیگر روشهای یادگیری ماشین داشتند. در نهایت پیشنهاد شد که تحقیقات مشابهی با در نظر گرفتن سایر عوامل مؤثر بر زبری (نظیر پوشش گیاهی) و در شرایط متفاوت مزرعهای (نظیر بافت و ساختمان خاک متفاوت) صورت پذیرد و الگوریتم متناسب با آن مجدداً آموزش ببیند تا کارایی و جامعیت آن ارتقا یابد. | ||
کلیدواژهها | ||
ضریب زبری مانینگ؛ پردازش تصویر؛ یادگیری ماشین؛ فاز پیشروی و ذخیره | ||
مراجع | ||
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