14 آذر 1403
فرشاد سليماني ساردو

فرشاد سلیمانی ساردو

مرتبه علمی: استادیار
نشانی: دانشگاه جیرفت- دانشکده منابع طبیعی-
تحصیلات: دکترای تخصصی / بیابانزدایی
تلفن: 03443347065
دانشکده: دانشکده منابع طبیعی

مشخصات پژوهش

عنوان
Comparison of statistical and machine learning approaches in land subsidence modelling
نوع پژوهش مقاله چاپ شده
کلیدواژه‌ها
Statistical models, machine learning, Boruta algorithml and subsidence prediction
پژوهشگران الهام رفیعی ساردوئی، حمیدرضا پورقاسمی، علی آذره، فرشاد سلیمانی ساردو، جان جی کلاگ

چکیده

his study attempted to predict ground subsidence occurrence using statistical and machine learning models, specifically the evidential belief function (EBF), index of entropy (IoE), support vector machine (SVM), and random forest (RF) models in the Rafsanjan Plain in southern Iran to investigate 11 possible causative factors: slope percent, aspect, topographic wetness index (TWI), plan and profile curvatures, normalized difference vegetation index (NDVI), land use, lithology, distance to river, groundwater drawdown, and elevation. The Boruta algorithm was applied to determine the importance of the possible causative factors. NDVI, groundwater drawdown, land use, and lithology had the strongest relationships with land subsidence. Finally, we generated land subsidence maps using different machine learning and statistical models. The accuracy of these models was assessed using the AUC value and the true skill statistic (TSS) metrics. The SVM model had the highest prediction accuracy (AUC = 0.967, TSS = 0.91), followed by RF (AUC = 0.936, TSS = 0.87), EBF (AUC = 0.907, TSS = 0.83), and IoE (AUC= 0.88, TSS = 0.8).