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A Bayesian network approach to modelling rip current drownings and shore-break wave injuries
Natural Hazards and Earth System Sciences ( IF 4.6 ) Pub Date : 2021-01-28 , DOI: 10.5194/nhess-2021-36
Elias de Korte , Bruno Castelle , Eric Tellier

Abstract. A Bayesian network (BN) approach is used to model and predict shore-break related injuries and rip-current drowning incidents based on detailed environmental conditions (wave, tide, weather, beach morphology) on the high-energy Gironde coast, southwest France. Six years (2011–2017) of boreal summer (15 June–15 September) surf zone injuries (SZIs) were analysed, comprising 442 (fatal and non-fatal) drownings caused by rip currents and 715 injuries caused by shore-break waves. Environmental conditions at the time of the SZIs were used to train two separate Bayesian networks (BNs), one for rip current drownings and the other one for shore-break wave injuries, each one with a hidden hazard and exposure variables. Both BNs were tested for varying complexity using K-fold cross-validation based on multiple performance metrics. Validation (prediction) results slightly improve predictions of SZIs with a poor to fair skill based on a combination of different metrics. Shore-break related injuries appear more predictable than rip current drowning incidents as the shore-break BN systematically performed better than the rip current BN. Sensitivity and scenario analyses were performed to address the influence of environmental data variables and their interactions on exposure, hazard and resulting life risk. Most of our findings are in line with earlier SZI and physical hazard-based work, that is, that more SZIs are observed for warm sunny days with light winds, long-period waves, with specifically more shore-break related injuries at high tide and for steep beach profiles, and more rip current drownings near low tide with near shore-normal wave incidence and strongly alongshore non-uniform surf zone morphology. The BNs also provided fresh insight, showing that rip current drowning risk is approximately equally distributed between exposure (variance reduction Vr = 14.4 %) and hazard (Vr = 17.4 %), while exposure of water user to shore-break waves is much more important (Vr = 23.5 %) than the hazard (Vr = 10.9 %). Large surf is found to decrease beachgoer exposure to shore-break hazard, while this is not observed for rip currents. Rapid change in tide elevation during days with large tidal range was also found to result in more drowning incidents, presumably because it favors the rapid onset of rip current activity and can therefore surprise unsuspecting bathers. We advocate that such BNs, providing a better understanding of hazard, exposure and life risk, can be developed to improve public safety awareness campaigns, in parallel with the development of more skillful risk predictors to anticipate high life-risk days.

中文翻译:

用贝叶斯网络方法模拟水流淹没和岸波伤害

摘要。贝叶斯网络(BN)方法用于基于法国西南部高能吉伦特海岸的详细环境条件(波浪,潮汐,天气,海滩形态),对与海岸破坏相关的伤害和河堤淹死事件进行建模和预测。分析了北方夏季(6月15日至9月15日)的六年(2011-2017年)冲浪区伤害(SZIs),包括rip电流造成的442人(致命和非致命)溺水事故和岸破浪造成的715人受伤。SZI当时的环境条件用于训练两个独立的贝叶斯网络(BN),一个用于泛滥淹没,另一个用于海岸波伤害,每个都有隐患和暴露变量。使用K测试了两个BN的复杂度基于多个性能指标的多重交叉验证。验证(预测)结果基于不同指标的组合,略微改善了技能水平不高的SZI的预测。与岸上溺水事故相比,与岸上摔伤相关的伤害似乎更具可预测性,因为岸上摔跤BN在系统上的表现要优于岸上当前的BN。进行了敏感性和情景分析,以解决环境数据变量及其相互作用对暴露,危害和由此产生的生命风险的影响。我们的大多数发现与早期的SZI和基于物理危害的工作是一致的,也就是说,在温暖的晴天,以小风,长周期波浪观察到更多的SZI,特别是在涨潮和退潮时与海岸破坏相关的伤害更大。对于陡峭的海滩轮廓,在低潮附近有更多的沿岸淹没,靠近海岸的法线波入射和沿海岸的强烈非均匀冲浪带形态。BN还提供了新的见解,表明当前的河道溺水风险在各次暴露之间大致均等分布(方差降低VR  = 14.4%)和危险(VR  = 17.4%),而水的用户到岸上断波的暴露是重要得多(VR  = 23.5%)比危险(VR = 10.9%)。发现大浪可以减少流浪者遭受海岸破坏的危险,而对于激流没有观察到。人们还发现,在大范围潮汐的日子里,潮汐高度的快速变化会导致更多的溺水事件,这可能是因为这样有利于河道水流活动的迅速开始,因此可能使毫无防备的沐浴者感到惊讶。我们主张,可以开发出对危险,暴露和生命风险有更好了解的BN,以改善公共安全意识运动,同时开发更熟练的风险预测因子来预测高生命危险天数。
更新日期:2021-01-28
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