气候变化领域集成服务门户(CCPortal): Integrated Assessment of Coastal Vulnerability in the Bonny Bay: A Combination of Traditional Methods (Simple and AHP) and Machine Learning Approach

CCPortal

DOI	10.1007/s12237-024-01362-7
	Integrated Assessment of Coastal Vulnerability in the Bonny Bay: A Combination of Traditional Methods (Simple and AHP) and Machine Learning Approach
	Nourdi, Njutapvoui F.; Raphael, Onguene; Achab, Mohammed; Loudi, Yap; Rudant, Jean-Paul; Minette, Tomedi E.; Kambia, Pouwedeou; Claude, Ntonga Jean; Romaric, Ntchantcho
发表日期	2024
ISSN	1559-2723
EISSN	1559-2731
英文摘要	The coast of Cameroon, located at the bottom of the Gulf of Guinea, is confronted with coastal hazards whose magnitude, distribution, and consequences are currently largely underestimated if not poorly understood. This study aims to fill this gap by proposing an integrated approach to coastal vulnerability assessment, combining simple traditional methods, multicriteria AHP (analytic hierarchy process) analysis, and machine learning techniques. Using geospatial data, field observations, and numerical models, we assessed the 402-km Cameroon coastline, taking into account interactions between physical, geological, and socio-economic factors. The results highlight geomorphology, slope, coastal erosion, and population density as the main contributors to vulnerability. The Integrated Coastal Vulnerability Index (IVCI) calculated by the simple method shows variable levels of vulnerability, with a predominance of very low and low in the northern sectors (S1 = 58%, S2 = 99%, and S3 = 87%) and high and very high in the south (S4 = 58% and S5 = 61%). The AHP method reveals a more balanced distribution of vulnerability levels, highlighting a sector (S3 = 96%) at very strong and strong risk. The application of six machine learning algorithms shows good predictive capabilities for ICVI, with the exception of the support vector machine (SVM). The artificial neural network (ANN) algorithm stands out for its superior accuracy, with an F-score of 0.9, ability to explain data variance (R = 0.95), accurate predictions (RMSE = 0.2), and excellent ability to distinguish classes (kappa coefficient of 0.9 and ROC AUC of 0.9). This study emphasizes the magnitude and complexity of interactions as indicators of the susceptibility of coastal populations to vulnerability.
英文关键词	Coastal vulnerability; Gulf of Guinea; Multicriteria analysis; Machine learning
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Marine & Freshwater Biology
WOS类目	Environmental Sciences ; Marine & Freshwater Biology
WOS记录号	WOS:001235752400001
来源期刊	ESTUARIES AND COASTS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/302755
作者单位	Universite Gustave-Eiffel; Mohammed V University in Rabat
推荐引用方式 GB/T 7714	Nourdi, Njutapvoui F.,Raphael, Onguene,Achab, Mohammed,et al. Integrated Assessment of Coastal Vulnerability in the Bonny Bay: A Combination of Traditional Methods (Simple and AHP) and Machine Learning Approach[J],2024.
APA	Nourdi, Njutapvoui F..,Raphael, Onguene.,Achab, Mohammed.,Loudi, Yap.,Rudant, Jean-Paul.,...&Romaric, Ntchantcho.(2024).Integrated Assessment of Coastal Vulnerability in the Bonny Bay: A Combination of Traditional Methods (Simple and AHP) and Machine Learning Approach.ESTUARIES AND COASTS.
MLA	Nourdi, Njutapvoui F.,et al."Integrated Assessment of Coastal Vulnerability in the Bonny Bay: A Combination of Traditional Methods (Simple and AHP) and Machine Learning Approach".ESTUARIES AND COASTS (2024).