Tên bài báo:

A novel hybrid artificial intelligence approach for flood susceptibility assessment
Tác giả:
Phạm Thái Bình
Tham gia cùng:
Tạp chí:
Environmental Modelling & Software
Năm xuất bản:
Từ trang 229 đến trang 245
Lĩnh vực:
Kỹ thuật xây dựng công trình giao thông
Phạm vi:
Quốc tế

Tóm tắt:

A new artificial intelligence (AI) model, called Bagging-LMT - a combination of bagging ensemble and Logistic Model Tree (LMT) - is introduced for mapping flood susceptibility. A spatial database was generated for the Haraz watershed, northern Iran, that included a flood inventory map and eleven flood conditioning factors based on the Information Gain Ratio (IGR). The model was evaluated using precision, sensitivity, specificity, accuracy, Root Mean Square Error, Mean Absolute Error, Kappa and area under the receiver operating characteristic curve criteria. The model was also compared with four state-of-the-art benchmark soft computing models, including LMT, logistic regression, Bayesian logistic regression, and random forest. Results revealed that the proposed model outperformed all these models and indicate that the proposed model can be used for sustainable management of flood-prone areas.

Từ khóa:

Flood susceptibility Bagging-LMT Bayesian logistic regression Logistic model tree Iran
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