Journal ArticleDOI
Modeling flood susceptibility using data-driven approaches of naïve Bayes tree, alternating decision tree, and random forest methods.
Wei Chen,Yang Li,Weifeng Xue,Himan Shahabi,Shaojun Li,Haoyuan Hong,Haoyuan Hong,Xiaojing Wang,Huiyuan Bian,Shuai Zhang,Biswajeet Pradhan,Baharin Bin Ahmad +11 more
TLDR
The results indicated that the RF method is an efficient and reliable model in flood susceptibility assessment, with the highest AUC values, positive predictive rate, negative predictive rates, specificity, and accuracy for the training and validation datasets.About:
This article is published in Science of The Total Environment.The article was published on 2020-01-20. It has received 256 citations till now. The article focuses on the topics: Flood myth & Alternating decision tree.read more
Citations
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Journal ArticleDOI
Flood susceptibility modelling using advanced ensemble machine learning models
Abu Reza Md. Towfiqul Islam,Swapan Talukdar,Susanta Mahato,Sonali Kundu,Kutub Uddin Eibek,Quoc Bao Pham,Alban Kuriqi,Nguyen Thi Thuy Linh +7 more
TL;DR: The methodology and solution-oriented results presented in this paper will assist the regional as well as local authorities and the policy-makers for mitigating the risks related to floods and also help in developing appropriate mitigation measures to avoid potential damages.
Journal ArticleDOI
Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier
Himan Shahabi,Ataollah Shirzadi,Kayvan Ghaderi,Ebrahim Omidvar,Nadhir Al-Ansari,John J. Clague,Marten Geertsema,Khabat Khosravi,Ata Amini,Sepideh Bahrami,Omid Rahmati,Kyoumars Habibi,Ayub Mohammadi,Hoang Nguyen,Assefa M. Melesse,Baharin Bin Ahmad,Anuar Ahmad +16 more
TL;DR: The results show that the Bagging–Cubic–KNN ensemble model outperformed other ensemble models and should be more widely applied for the sustainable management of flood-prone areas.
Journal ArticleDOI
Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil
Quang Hung Nguyen,Hai-Bang Ly,Lanh Si Ho,Nadhir Al-Ansari,Hiep Van Le,Van Quan Tran,Indra Prakash,Binh Thai Pham +7 more
TL;DR: The results presented herein showed an effective manner in selecting the appropriate ratios of datasets and the best ML model to predict the soil shear strength accurately, which would be helpful in the design and engineering phases of construction projects.
Journal ArticleDOI
GIS-based comparative assessment of flood susceptibility mapping using hybrid multi-criteria decision-making approach, naïve Bayes tree, bivariate statistics and logistic regression: A case of Topľa basin, Slovakia
Sk Ajim Ali,Farhana Parvin,Quoc Bao Pham,Matej Vojtek,Jana Vojteková,Romulus Costache,Nguyen Thi Thuy Linh,Hong Quan Nguyen,Ateeque Ahmad,Mohammad Ali Ghorbani +9 more
TL;DR: The presented methodological approach used for the identification of flood susceptible areas can serve as an alternative for the updating of preliminary flood risk assessment based on the EU Floods Directive.
References
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Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Book
Data Mining: Concepts and Techniques
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
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Data Mining: Practical Machine Learning Tools and Techniques
TL;DR: This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
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A model of inexact reasoning in medicine
TL;DR: In this paper, a quantification scheme is proposed to model the inexact reasoning processes of medical experts, which is essentially an approximation to conditional probability, but offers advantages over Bayesian analysis when they are utilized in a rule-based computer diagnostic system.