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مقالات فارسیISIکنفرانسهاژورنالها

Machine Learning Based Prediction of Urban Flood Susceptibility from Selected Rivers in a Tropical Catchment Area

محل انتشار: ژورنال مهندسی عمران، دوره: 8، شماره: 9
سال انتشار: 1401
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 22

فایل این مقاله در 144 صفحه با فرمت PDF قابل دریافت می باشد

استخراج به نرم افزارهای پژوهشی:

لینک ثابت به این مقاله:
https://civilica.com/doc/1962533

شناسه ملی سند علمی:

JR_CEJ-8-9_008

تاریخ نمایه سازی: 1 اردیبهشت 1403

چکیده مقاله:

Unexpected flood due to climate change has caused tremendous damage to both lives and properties, especially in tropical areas. Nigeria Southeastern region has been devastated by flood from extreme weather conditions. Flood mitigation involves accurate forecasting, precise prediction, evaluation, and intervention strategy. This study aims at using machine learning solutions to investigate and predict flood susceptibility from selected rivers in the south-eastern region of Nigeria. The regional hydrogeological data from ۱۹۸۱–۲۰۱۹ was collected and analysed. The remote sensing datasets from the National Aeronautics and Space Administration (NASA), Modern-Era Retrospective Analysis for Research and Applications (MERRA) version ۲ & ۳ platforms from five selected rivers were processed. With the data output of the hydrology, streamline flows, and exposed geology, the ARIMA model was built and used to forecast the flood. The result shows that the flooding pattern would increase by ۱۵-۱۵۰% within ۲۰۲۰-۲۰۲۴. The forecast indicated that within five years, the river discharge for Adada, Ajali, Imo, Ivo, and Otanmiri will increase within ranges ۲۰۰-۷۰۲ m۳s-۱, ۱۶-۲۶ m۳s-۱, ۵۰۸-۱۲۸۰ m۳s-۱, ۴۳-۶۸.۵ m۳s-۱, and ۲۲-۳۵.۱ m۳s-۱ respectively. Climate change has impacted severely on flood in the region. This knowledge will help the regional agencies and authorities in adapting to flood innuendoes and assessment of hydrologic extremes. Doi: ۱۰.۲۸۹۹۱/CEJ-۲۰۲۲-۰۸-۰۹-۰۸ Full Text: PDF

کلیدواژه ها:

Machine Learning ، Flood Prediction ، Susceptibility ، Hydrologic Extremes ، Autoregression ، Climate Change.

نویسندگان

Benjamin Nnamdi Ekwueme

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