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بهینهسازی بهرهوری آب در مواجهه با تغییرات اقلیمی: نقش محوری رویکردهای یادگیری ماشین | ||
| تحقیقات آب و خاک ایران | ||
| دوره 56، شماره 11، بهمن 1404، صفحه 2929-2949 اصل مقاله (1.19 M) | ||
| نوع مقاله: مروری | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2025.403943.670022 | ||
| نویسندگان | ||
| ایمان حاجی راد* 1؛ پریا پورمحمد2؛ مسعود پورغلام1 | ||
| 1گروه مهندسی آبیاری و آبادانی، دانشکدگان کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران. | ||
| 2گروه مهندسی احیاء مناطق خشک و کوهستانی، دانشکده مناطق طبیعی، دانشگاه تهران، کرج، ایران | ||
| چکیده | ||
| تغییرات اقلیمی با ایجاد نوسانات شدید در الگوهای بارش، دما و تبخیر- تعرق، مدیریت منابع آب را بهویژه در مناطق خشک و نیمهخشک با چالشهای فزایندهای مواجه ساخته است. در این شرایط بحرانی، ارتقاء بهرهوری مصرف آب (WUE) در بخش کشاورزی به عنوان مؤثرترین راهکار برای سازگاری با کمآبی و حفظ امنیت غذایی مطرح میشود. ارزیابی و بهینهسازی WUE، بهدلیل طبیعت غیرخطی و پویای روابط میان متغیرهای اقلیمی، خاکی و زراعی، از توان مدلهای سنتی فراتر رفته است. پیشرفتهای اخیر در حوزه علم داده و هوش مصنوعی، و بهطور خاص توسعهی مدلهای یادگیری ماشین (ML) و یادگیری عمیق (DL)، امکان تحلیل حجم عظیمی از دادههای اقلیمی، هیدرولوژیکی و زراعی را فراهم کرده است. این مقاله مروری جامع، به بررسی نقش رویکردهای دادهمحور در بهینهسازی بهرهوری مصرف آب در شرایط عدم قطعیت اقلیمی میپردازد. در این پژوهش، با مرور مطالعات انجام شده، کاربرد انواع مدلها از جمله جنگل تصادفی، ماشین بردار پشتیبان، و شبکههای عصبی در حوزههای کلیدی مانند پیشبینی نیاز آبی، برآورد دقیق تبخیر - تعرق و ارزیابی عملکرد سامانههای آبیاری تحلیل میشود. مرور ادبیات نشان میدهد که استفاده از مدلهای ترکیبی با ترکیب دادههای چندمنبعی (سنجش از دور، سنسورهای IoT و دادههای زمینی) دقت تصمیمگیری در مدیریت آب را بهطور چشمگیری افزایش میدهد. این رویکرد، نه تنها چالشهای ناشی از ناپایداری اقلیمی را مدیریت میکند، بلکه زمینه را برای توسعه سامانههای آبیاری هوشمند و تطبیقی فراهم میسازد که برای افزایش تابآوری منابع آب ضروری هستند. | ||
| کلیدواژهها | ||
| یادگیری عمیق؛ آبیاری هوشمند؛ پیشبینی؛ تابآوری | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 165 تعداد دریافت فایل اصل مقاله: 167 |
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