Floods and storm surges pose significant threats to coastal regions worldwide,demanding timely and accurate early warning systems(EWS)for disaster preparedness.Traditional numerical and statistical methods often fall ...Floods and storm surges pose significant threats to coastal regions worldwide,demanding timely and accurate early warning systems(EWS)for disaster preparedness.Traditional numerical and statistical methods often fall short in capturing complex,nonlinear,and real-time environmental dynamics.In recent years,machine learning(ML)and deep learning(DL)techniques have emerged as promising alternatives for enhancing the accuracy,speed,and scalability of EWS.This review critically evaluates the evolution of ML models—such as Artificial Neural Networks(ANN),Convolutional Neural Networks(CNN),and Long Short-Term Memory(LSTM)—in coastal flood prediction,highlighting their architectures,data requirements,performance metrics,and implementation challenges.A unique contribution of this work is the synthesis of real-time deployment challenges including latency,edge-cloud tradeoffs,and policy-level integration,areas often overlooked in prior literature.Furthermore,the review presents a comparative framework of model performance across different geographic and hydrologic settings,offering actionable insights for researchers and practitioners.Limitations of current AI-driven models,such as interpretability,data scarcity,and generalization across regions,are discussed in detail.Finally,the paper outlines future research directions including hybrid modelling,transfer learning,explainable AI,and policy-aware alert systems.By bridging technical performance and operational feasibility,this review aims to guide the development of next-generation intelligent EWS for resilient and adaptive coastal management.展开更多
Offshore structures such as platforms,pipelines,the hulls of ships,wind turbine foundations,etc.,are constantly subjected to harsh seawater environments with high salinity,changes in temperature,humidity,biological ac...Offshore structures such as platforms,pipelines,the hulls of ships,wind turbine foundations,etc.,are constantly subjected to harsh seawater environments with high salinity,changes in temperature,humidity,biological activity,etc.These conditions promote corrosion and jeopardize the service,safety and service life.In this study,recent developments in ocean materials for corrosion resistance are extensively reviewed.It classifies corrosion-resistant materials as metal alloys,nanocomposites,and nanostructured hybrid materials and discusses their performance,mechanisms of protection,and applications in the field.It treats high-performance materials such as stainless steels,Ni-based alloys,and Ti alloys,polymers and composites,ceramics,or evenbioinspired coatings.A comprehensive study including corrosion failure mechanisms,such as pitting,crevice,galvanic,microbiologically influenced,stress corrosion cracking(SCC),is provided to present a comprehensive view of the necessary selection of materials and corrosion control practices.Concurrently,the review presents protective technologies such as cathodic protection systems,anodizing,passivation,thermal spray coatings,as well as emerging ones such as plasma electrolytic oxidation and self-healing smart coatings.Advanced high entropy alloys,graphene barriers and additive manufacturing are stressed as they have the potential to disrupt marine corrosion protection.However,issues such as long-term performance verification,cost-performance ratio for optimal design,compatibility with on-line monitoring systems,and compliance with environmental standard still remain unsolved.The paper highlights significant research gaps and potential future directions in AI-enabled material design,green coatings,and development of digital corrosion management systems.In the end,this book is a great resource for engineers,researchers,and policymakers involved in the development of durable,efficient and ecologically friendly marine infrastructure.展开更多
文摘Floods and storm surges pose significant threats to coastal regions worldwide,demanding timely and accurate early warning systems(EWS)for disaster preparedness.Traditional numerical and statistical methods often fall short in capturing complex,nonlinear,and real-time environmental dynamics.In recent years,machine learning(ML)and deep learning(DL)techniques have emerged as promising alternatives for enhancing the accuracy,speed,and scalability of EWS.This review critically evaluates the evolution of ML models—such as Artificial Neural Networks(ANN),Convolutional Neural Networks(CNN),and Long Short-Term Memory(LSTM)—in coastal flood prediction,highlighting their architectures,data requirements,performance metrics,and implementation challenges.A unique contribution of this work is the synthesis of real-time deployment challenges including latency,edge-cloud tradeoffs,and policy-level integration,areas often overlooked in prior literature.Furthermore,the review presents a comparative framework of model performance across different geographic and hydrologic settings,offering actionable insights for researchers and practitioners.Limitations of current AI-driven models,such as interpretability,data scarcity,and generalization across regions,are discussed in detail.Finally,the paper outlines future research directions including hybrid modelling,transfer learning,explainable AI,and policy-aware alert systems.By bridging technical performance and operational feasibility,this review aims to guide the development of next-generation intelligent EWS for resilient and adaptive coastal management.
文摘Offshore structures such as platforms,pipelines,the hulls of ships,wind turbine foundations,etc.,are constantly subjected to harsh seawater environments with high salinity,changes in temperature,humidity,biological activity,etc.These conditions promote corrosion and jeopardize the service,safety and service life.In this study,recent developments in ocean materials for corrosion resistance are extensively reviewed.It classifies corrosion-resistant materials as metal alloys,nanocomposites,and nanostructured hybrid materials and discusses their performance,mechanisms of protection,and applications in the field.It treats high-performance materials such as stainless steels,Ni-based alloys,and Ti alloys,polymers and composites,ceramics,or evenbioinspired coatings.A comprehensive study including corrosion failure mechanisms,such as pitting,crevice,galvanic,microbiologically influenced,stress corrosion cracking(SCC),is provided to present a comprehensive view of the necessary selection of materials and corrosion control practices.Concurrently,the review presents protective technologies such as cathodic protection systems,anodizing,passivation,thermal spray coatings,as well as emerging ones such as plasma electrolytic oxidation and self-healing smart coatings.Advanced high entropy alloys,graphene barriers and additive manufacturing are stressed as they have the potential to disrupt marine corrosion protection.However,issues such as long-term performance verification,cost-performance ratio for optimal design,compatibility with on-line monitoring systems,and compliance with environmental standard still remain unsolved.The paper highlights significant research gaps and potential future directions in AI-enabled material design,green coatings,and development of digital corrosion management systems.In the end,this book is a great resource for engineers,researchers,and policymakers involved in the development of durable,efficient and ecologically friendly marine infrastructure.
