干旱区生态环境知识资源中心(Arid): Flood susceptibility mapping in an arid region of Pakistan through ensemble machine learning model

Arid

DOI	10.1007/s00477-022-02179-1
	Flood susceptibility mapping in an arid region of Pakistan through ensemble machine learning model
	Yaseen, Andaleeb; Lu, Jianzhong; Chen, Xiaoling
通讯作者	Lu, JZ (corresponding author)，Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China.
来源期刊	STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
ISSN	1436-3240
EISSN	1436-3259
出版年	2022-02
英文摘要	Floods are among the most destructive natural hazards. Therefore, their prediction is pivotal for flood management and public safety. Factors contributing to flood are different for every watershed as they depend upon the characteristics of each watershed. Therefore, this study evaluated the factors contributing to flood and the precise location of high and very high flood susceptibility regions in Karachi. A new ensemble model (LR-SVM-MLP) is introduced to develop the susceptibility map and evaluate influencing factors. This ensemble model was formed by employing a stacking ensemble on Logistic Regression (LR), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). A spatial database was generated for the Karachi watershed, which included; twelve conditioning factors as independent variables, 652 flood points and the same number of non-flood points as dependent variables. This data was then randomly divided into 70% and 30% to train and validate models, respectively. To analyse the collinearity among factors and to scrutinize each variable's predictive power, multicollinearity test and Information Gain Ratio were applied, respectively. After training, the models were evaluated on various statistical measures and compared with benchmark models. Results revealed that the proposed ensemble model outperformed Logistic Regression (LR), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP) and produced a precise and accurate map. Results of ensemble model showed 99% accuracy in training and 98% accuracy in testing datasets. This ensemble model can be used by flood management authorities and the government to contribute to future research studies.
英文关键词	Flash flood Logistic regression Support vector machine Multi-layer perceptron Ensemble classifier
类型	Article ; Early Access
语种	英语
开放获取类型	Green Submitted
收录类别	SCI-E
WOS记录号	WOS:000754241100001
WOS关键词	SUPPORT VECTOR MACHINE ; ARTIFICIAL-INTELLIGENCE APPROACH ; ANALYTIC HIERARCHY PROCESS ; LOGISTIC-REGRESSION ; NEURAL-NETWORKS ; DECISION TREE ; HAZARD AREAS ; GIS ; VALIDATION ; RIVER
WOS类目	Engineering, Environmental ; Engineering, Civil ; Environmental Sciences ; Statistics & Probability ; Water Resources
WOS研究方向	Engineering ; Environmental Sciences & Ecology ; Mathematics ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/377302
作者单位	[Yaseen, Andaleeb; Lu, Jianzhong; Chen, Xiaoling] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China
推荐引用方式 GB/T 7714	Yaseen, Andaleeb,Lu, Jianzhong,Chen, Xiaoling. Flood susceptibility mapping in an arid region of Pakistan through ensemble machine learning model[J],2022.
APA	Yaseen, Andaleeb,Lu, Jianzhong,&Chen, Xiaoling.(2022).Flood susceptibility mapping in an arid region of Pakistan through ensemble machine learning model.STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT.
MLA	Yaseen, Andaleeb,et al."Flood susceptibility mapping in an arid region of Pakistan through ensemble machine learning model".STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT (2022).