首页|Improvement of Best First Decision Trees Using Bagging and Dagging Ensembles for Flood Probability Mapping

Improvement of Best First Decision Trees Using Bagging and Dagging Ensembles for Flood Probability Mapping

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  • NETL
  • NSTL
  • Springer Nature
Development of zoning and flood-forecasting models is essential for making optimal management decisions before and after floods. The Komijan watershed of Markazi Province, Iran is often affected by floods that have caused great material damage and loss of life. The main objective of this study is to use a new machine-learning method to create three models: best-first decision tree (BFT), a bagging best-first decision tree (BBFT) ensemble and a dagging best-first decision tree (DBFT) ensemble to spatially predict flood probability. Twelve conditioning-factor measures for 272 locations of past floods were used to train and test three models. Receiver operating characteristic (ROC), positive predictive value (PPV), negative predictive value (NPV), sensitivity (SST), specificity (SPF), accuracy (ACC), kappa (K), and root mean square error (RMSE) are applied to compare and validate the models. The results are that all three models performed well in mapping, flood probabilities (AUC > 0.904). The BBFT model was best, however, with an AUC = 0.96. Based on the results of the Relief-F attribute evaluation method, two soil and slope factors were weighted highest among the parameters, indicating that they are the most important flood-conditioning factors. These models may improve identification of zones that are most susceptible to flooding, improving the capacity for risk management and providing more detailed information for managers and decision-makers.

Flood-probability mapMachine learningGISROCKomijan watershed

Yariyan, Peyman、Janizadeh, Saeid、Phong Van Tran、Huu Duy Nguyen、Costache, Romulus、Hiep Van Le、Binh Thai Pham、Pradhan, Biswajeet、Tiefenbacher, John P.

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Islamic Azad Univ, Saghez Branch, Dept Surveying Engn, Saghez, Iran

Tarbiat Modares Univ, Dept Watershed Management Engn, Coll Nat Resources, POB 14115-111, Tehran, Iran

Vietnam Acad Sci & Technol, Inst Geol Sci, Hanoi 10000, Vietnam

Vietnam Natl Univ, VNU Univ Sci, Fac Geog, 334 Nguyen Trai, Hanoi, Vietnam

Univ Bucharest, Res Inst, 90-92 Sos Panduri,5th Dist, Bucharest, Romania|Natl Inst Hydrol & Water Management, Bucuresti Ploiesti Rd,97E,1st Dist, Bucharest 013686, Romania

Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam

Univ Transport Technol, Hanoi 100000, Vietnam

Univ Technol Sydney, Ctr Adv Modelling & Geospatial Informat Syst CAMG, Fac Engn & IT, Sydney, NSW 2007, Australia|Sejong Univ, Dept Energy & Mineral Resources Engn, 209 Neungdong Ro, Seoul 05006, South Korea

Texas State Univ, Dept Geog, San Marcos, TX 78666 USA

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2020

10.1007/s11269-020-02603-7

Water resources management

Water resources management

ISSN：0920-4741
年,卷(期)：2020.34(9)
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