در این پژوهش، با بهرهگیری از الگوریتم جنگل تصادفی و روش تفسیرپذیری شپلی، نقشه حساسیت خشکسالی استان خوزستان تهیه شد. برای تهیه نقشه وقوع خشکسالی، از دادههای شاخص بارش استاندارد شده در بازه زمانی 2018 تا 2022 استفاده شد. در این مطالعه از 18 فاکتور ( رطوبتنسبی، سرعت باد، تبخیروتعرق، کمینه و بیشینه دما، ارتفاع، شیب، جهت شیب، شاخص رطوبت توپوگرافی، پوشش اراضی، شاخص پوشش گیاهی، محتوای آبخاک، تراکم رودخانه، سطح آب، خاک ماسه، چگالی ظاهری خاک، خاک رس، بافت خاک) بهعنوان متغیرهای ورودی برای مدل استفاده شد. ارزیابی کارایی مدل با شاخص منحنی مشخصه عملکرد سیستم (ROC) نشان داد که دقت پیشبینی 0.987 بود که بیانگر عمکرد عالی مدل در نقشه خروجی مدل جنگل تصادفی بود. براساس نتایج حاصل از نقشه حساسیت به خشکسالی، 31.03 درصد منطقه مطالعاتی در طبقه "خیلی کم"، 8.18 درصد در طبقه "کم"،8.30 درصد منطقه در طبقه "متوسط"، 10.88درصد منطقه در طبقه زیاد و 41.58 درصد منطقه در طبقه "خیلی زیاد" قرار گرفته است. بیشترین فراوانی مشاهده شده طبقات مرتبط با دو طبقه "خیلی کم" و "خیلی زیاد" بوده است. براساس روش شپلی فاکتورهایی مانند ارتفاع، دمای بیشینه، شاخص رطوبت توپوگرافی، سرعت باد و شیب بیشترین تاثیر را در وقوع خشکسالی داشتهاند. این مطالعه مزایای استفاده از روشهای یادگیری ماشین تفسیرپذیر را نشان میدهد.
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