邦尼湾海岸脆弱性综合评估:传统方法(简单和 AHP)与机器学习方法的结合

IF 2.3 3区 环境科学与生态学 Q3 ENVIRONMENTAL SCIENCES
Njutapvoui F. Nourdi, Onguene Raphael, Mohammed Achab, Yap Loudi, Jean-Paul Rudant, Tomedi E. Minette, Pouwédéou Kambia, Ntonga Jean Claude, Ntchantcho Romaric
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引用次数: 0

摘要

位于几内亚湾底部的喀麦隆沿海地区面临着各种沿海灾害,目前对这些灾害的规模、分布和后果即使不是知之甚少,也大多估计不足。本研究旨在通过提出一种海岸脆弱性评估的综合方法,将简单的传统方法、多标准 AHP(层次分析法)分析和机器学习技术结合起来,填补这一空白。利用地理空间数据、实地观测和数值模型,我们对喀麦隆 402 公里长的海岸线进行了评估,同时考虑了物理、地质和社会经济因素之间的相互作用。评估结果表明,地貌、坡度、海岸侵蚀和人口密度是造成海岸脆弱性的主要因素。用简单方法计算出的海岸脆弱性综合指数(IVCI)显示出不同程度的脆弱性,北部地区以 "非常低 "和 "低 "为主(S1 = 58%,S2 = 99%,S3 = 87%),南部地区以 "高 "和 "非常高 "为主(S4 = 58%,S5 = 61%)。AHP 方法显示了更均衡的脆弱性水平分布,突出了一个处于 "非常高 "和 "高 "风险的部门(S3 = 96%)。除支持向量机(SVM)外,六种机器学习算法的应用显示出对 ICVI 的良好预测能力。人工神经网络(ANN)算法因其卓越的准确性而脱颖而出,其 F 分数为 0.9,能够解释数据方差(R = 0.95),预测准确(RMSE = 0.2),并且具有出色的类别区分能力(卡帕系数为 0.9,ROC AUC 为 0.9)。这项研究强调了相互作用作为沿海居民易受脆弱性影响指标的程度和复杂性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Integrated Assessment of Coastal Vulnerability in the Bonny Bay: A Combination of Traditional Methods (Simple and AHP) and Machine Learning Approach

Integrated Assessment of Coastal Vulnerability in the Bonny Bay: A Combination of Traditional Methods (Simple and AHP) and Machine Learning Approach

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.

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来源期刊
Estuaries and Coasts
Estuaries and Coasts 环境科学-海洋与淡水生物学
CiteScore
5.60
自引率
11.10%
发文量
107
审稿时长
12-24 weeks
期刊介绍: Estuaries and Coasts is the journal of the Coastal and Estuarine Research Federation (CERF). Begun in 1977 as Chesapeake Science, the journal has gradually expanded its scope and circulation. Today, the journal publishes scholarly manuscripts on estuarine and near coastal ecosystems at the interface between the land and the sea where there are tidal fluctuations or sea water is diluted by fresh water. The interface is broadly defined to include estuaries and nearshore coastal waters including lagoons, wetlands, tidal fresh water, shores and beaches, but not the continental shelf. The journal covers research on physical, chemical, geological or biological processes, as well as applications to management of estuaries and coasts. The journal publishes original research findings, reviews and perspectives, techniques, comments, and management applications. Estuaries and Coasts will consider properly carried out studies that present inconclusive findings or document a failed replication of previously published work. Submissions that are primarily descriptive, strongly place-based, or only report on development of models or new methods without detailing their applications fall outside the scope of the journal.
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