Journal ArticleDOI
Flood hazard mapping in Jamaica using principal component analysis and logistic regression
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TLDR
In this article, a GIS-based multi-criteria statistical methodology was developed to quantify hazard potential and to map flood characteristics on the island of Jamaica, where 14 factors potentially responsible for flooding were identified and used as initial input in a hybrid model that combined principal component analysis with logistic regression and frequency distribution analysis.Abstract:
Jamaica, the third largest island in the Caribbean, has been affected significantly by flooding and flood-related damage. Hence assessing the probability of flooding and susceptibility of a place to flood hazard has become a vital part of planning and development. In addition to heavy rainfall from tropical storms and Atlantic hurricanes, several terrestrial factors play significant roles in flooding, including local geology, geomorphology, hydrology and land-use. In this study, a GIS-based multi-criteria statistical methodology was developed to quantify hazard potential and to map flood characteristics. Fourteen factors potentially responsible for flooding were identified and used as initial input in a hybrid model that combined principal component analysis with logistic regression and frequency distribution analysis. Of these factors, seven explained 65 % of the variation in the data: elevation, slope angle, slope aspect, flow accumulation, a topographic wetness index, proximity to a stream network, and hydro-stratigraphic units. These were used to prepare the island’s first map of flood hazard potential. Hazard potential was classified from very low to very high, nearly one-fifth (19.4 %) of the island was included within high or very high flood hazard zones. Further analysis revealed that areas prone to flooding are often low-lying and flat, or have shallow north- or northwest-facing slopes, are in close proximity to the stream network, and are situated on underlying impermeable lithology. The multi-criteria hybrid approach developed could classify 86.8 % of flood events correctly and produced a satisfactory validation result based on the receiver operating characteristic curve. The statistical method can be easily repeated and refined upon the availability of additional or higher quality data such as a high resolution digital elevation model. Additionally, the approach used in this study can be adopted to evaluate flood hazard in countries with similar characteristics, landscapes and climatic conditions, such as other Caribbean or Pacific Small Island Developing States.read more
Citations
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Journal ArticleDOI
Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution.
Haoyuan Hong,Haoyuan Hong,Mahdi Panahi,Ataollah Shirzadi,Tianwu Ma,Tianwu Ma,Junzhi Liu,Junzhi Liu,A-Xing Zhu,A-Xing Zhu,Wei Chen,Ioannis Kougias,Nerantzis Kazakis +12 more
TL;DR: This paper addresses the development of a flood susceptibility assessment that uses intelligent techniques and GIS and an adaptive neuro-fuzzy inference system (ANFIS) was coupled with a genetic algorithm and differential evolution for flood spatial modelling.
Journal ArticleDOI
A novel hybrid approach based on a swarm intelligence optimized extreme learning machine for flash flood susceptibility mapping
Dieu Tien Bui,Phuong Thao Thi Ngo,Tien Dat Pham,Abolfazl Jaafari,Nguyen Quang Minh,Pham Viet Hoa,Pijush Samui +6 more
TL;DR: A new soft computing approach that is an integration of an Extreme Learning Machine and a Particle Swarm Optimization, named as PSO-ELM, for the spatial prediction of flash flood susceptibility at high frequency tropical typhoon areas is proposed and validated.
Journal ArticleDOI
A comparison of statistical methods and multi-criteria decision making to map flood hazard susceptibility in Northern Iran
TL;DR: Four methods were tested: evidential belief function (EBF), frequency ratio (FR), Technique for Order Preference by Similarity To ideal Solution (TOPSIS) and Vlse Kriterijumsk Optimizacija Kompromisno Resenje (VIKOR) for flood hazard susceptibility mapping (FHSM) in this area and all models except VIKOR showed acceptable accuracy of classification.
Journal ArticleDOI
Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran
Saeid Janizadeh,Mohammadtaghi Avand,Abolfazl Jaafari,Tran Van Phong,Mahmoud Bayat,Ebrahim Ahmadisharaf,Indra Prakash,Binh Thai Pham,Saro Lee +8 more
TL;DR: In this article, the authors used five machine learning methods, i.e., alternating decision tree (ADT), functional tree (FT), kernel logistic regression (KLR), multilayer perceptron (MLP), and quadratic discriminant analysis (QDA), to estimate flash flood susceptibility in the Tafresh watershed.
Journal ArticleDOI
Flash Flood Hazard Susceptibility Mapping Using Frequency Ratio and Statistical Index Methods in Coalmine Subsidence Areas
TL;DR: Wang et al. as mentioned in this paper focused on producing flash flood hazard susceptibility maps (FFHSM) using frequency ratio and statistical index (SI) models in the Xiqu Gully (XQG) of Beijing, China.
References
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Book
Applied Logistic Regression
David W. Hosmer,Stanley Lemeshow +1 more
TL;DR: Hosmer and Lemeshow as discussed by the authors provide an accessible introduction to the logistic regression model while incorporating advances of the last decade, including a variety of software packages for the analysis of data sets.
Journal ArticleDOI
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TL;DR: Applied Logistic Regression, Third Edition provides an easily accessible introduction to the logistic regression model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables.
A physically based, variable contributing area model of basin hydrology
Mike Kirkby,Keith Beven +1 more
TL;DR: In this paper, a hydrological forecasting model is presented that attempts to combine the important distributed effects of channel network topology and dynamic contributing areas with the advantages of simple lumped parameter basin models.
Journal ArticleDOI
A physically based, variable contributing area model of basin hydrology / Un modèle à base physique de zone d'appel variable de l'hydrologie du bassin versant
Keith Beven,Mike Kirkby +1 more
TL;DR: In this paper, a hydrological forecasting model is presented that combines the important distributed effects of channel network topology and dynamic contributing areas with the advantages of simple luminescence.