全面审查用于预报冰塞洪水发生、严重程度、时间和地点的人工智能方法

IF 3.8 2区 工程技术 Q1 ENGINEERING, CIVIL Cold Regions Science and Technology Pub Date : 2024-08-31 DOI:10.1016/j.coldregions.2024.104305
Amirhossein Salimi , Tadros Ghobrial , Hossein Bonakdari
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引用次数: 0

摘要

冬季寒冷气候条件下的大多数河流都会受到冰崩的影响,给沿河社区带来严重的冰塞洪水(IJF)威胁。由于水文气候因素和河流形态之间复杂的相互作用导致了冰塞洪水的混乱性质,因此预测冰塞洪水具有挑战性。此外,近几十年来,气候变化对河流结冰模式和 IJF 的严重程度产生了重大影响。然而,近来计算能力的进步促使人们开发了几种人工智能(AI)方法来预测 IJF。然而,目前仍缺乏系统性综述,以充分比较不同的人工智能方法和用于预报 IJF 的不同水文气象参数。因此,本研究的主要目的是审查现有的各种基于人工智能的 IJFs 预测模型、其输入参数以及潜在的优势和局限性。研究表明,基于人工智能的 IJF 预测模型可根据其预测 IJF 发生、严重程度、时间和地点的目标分为四类。研究还显示,基于台站的数据仍然是预测 IJF 的主要信息来源,但近年来,遥感、再分析产品和国家数据库有日益增长的趋势,这表明它们的地位越来越重要。总体而言,气温、降水和水文参数(排水量和水位)是最常用的输入参数。综述还将基于人工智能的 IJF 预测模型分为四种类型:机器学习、混合、集合和框架模型。虽然近年来框架方法越来越受欢迎,但机器学习和集合模型仍是最常用的方法。虽然直接比较不同建模方法的能力和局限性而不考虑其应用地点的具体情况可能会产生误导,但一些研究已经证明,与单一的机器学习模型相比,集合和混合方法具有提高模型准确性的潜力。不过,还需要更多的研究来证实这些结论。
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A comprehensive review of AI-based methods used for forecasting ice jam floods occurrence, severity, timing, and location

River ice breakup can affect most rivers in cold climate during winter, posing a serious threat of Ice-Jam Floods (IJFs) to riverine communities. IJFs are challenging to predict due to their chaotic nature that arises from the complex interaction between hydroclimatic factors and river morphology. In addition, climate change has significantly impacted river ice patterns and the severity of IJFs in recent decades. However, recent advancements in computing power have led to the development of several Artificial Intelligence (AI) approaches to forecast IJF. Still, there is a lack of a systematic review that can adequately compare the different AI approaches together with the different hydrometeorological parameters used to forecast IJF. Therefore, the primary objective of this study is to review the various existing AI-based IJFs prediction models, their input parameters, and their potential strengths and limitations. The review showed that AI-based IJF prediction models can be grouped into four categories based on their objectives to forecast IJF occurrence, severity, timing, and location. The study also revealed that station-based data remained the primary source of information for predicting IJFs, but there has been a growing trend in recent years toward remote sensing, reanalysis products, and national databases, indicating their increasing prominence. Overall, air temperature, precipitation, and hydrometric parameters (discharge and water level) were the most frequently utilized input parameters. The review also categorized AI-based IJF forecasting models into four types: machine learning, hybrid, ensemble, and framework models. Although the framework approach has gained recent popularity in recent years, but still the machine learning and ensemble models were the most frequently used. While directly comparing the capabilities and limitations of different modeling approaches without considering the specific context of the sites in which they were applied can be misleading, several studies have demonstrated the potential of ensemble and hybrid approaches to improve model accuracy compared to single machine learning models. However, more studies are needed to confirm these conclusions.

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来源期刊
Cold Regions Science and Technology
Cold Regions Science and Technology 工程技术-地球科学综合
CiteScore
7.40
自引率
12.20%
发文量
209
审稿时长
4.9 months
期刊介绍: Cold Regions Science and Technology is an international journal dealing with the science and technical problems of cold environments in both the polar regions and more temperate locations. It includes fundamental aspects of cryospheric sciences which have applications for cold regions problems as well as engineering topics which relate to the cryosphere. Emphasis is given to applied science with broad coverage of the physical and mechanical aspects of ice (including glaciers and sea ice), snow and snow avalanches, ice-water systems, ice-bonded soils and permafrost. Relevant aspects of Earth science, materials science, offshore and river ice engineering are also of primary interest. These include icing of ships and structures as well as trafficability in cold environments. Technological advances for cold regions in research, development, and engineering practice are relevant to the journal. Theoretical papers must include a detailed discussion of the potential application of the theory to address cold regions problems. The journal serves a wide range of specialists, providing a medium for interdisciplinary communication and a convenient source of reference.
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