Predicting flood for any location at times of extreme storms is a longstanding problem that has utmost importance in emergency management. Conventional methods that aim to predict water levels in streams use advanced hydrological models still lack of giving accurate forecasts everywhere. This study aims to explore artificial deep neural networks' performance on flood prediction. While providing models that can be used in forecasting stream stage, this paper presents a dataset that focuses on the connectivity of data points on river networks. It also shows that neural networks can be very helpful in time-series forecasting as in flood events, and support improving existing models through data assimilation.
Google Research（谷歌研究院）；The Hebrew University（希伯来大学）；Technion - Israel Institute of Technology（以色列理工学院）；Bar-Ilan University（巴伊兰大学）；Google Crisis Response（谷歌危机响应团队）
Large-scale flood modeling and forecasting with FloodCast
Technical University of Munich（慕尼黑工业大学）；University of Bristol（布里斯托尔大学）；Institute of Mountain Hazards and Environment, Chinese Academy of Sciences（中国科学院成都山地灾害与环境研究所）
Community HydroAgent: Formalizing Forecaster Expertise into Skill-Orchestrated Flood Forecasting Workflows
Politecnico di Milano（米兰理工大学）；China Three Gorges University（三峡大学）；Delft University of Technology（代尔夫特理工大学）；Qinhuangdao Hydrological Survey and Research Center of Hebei Province（河北省秦皇岛水文勘测研究中心）；Dalian University of Technology（大连理工大学）；TU Dresden University of Technology（德累斯顿工业大学）；Ningbo University（宁波大学）；China IPPR International Engineering Co., Ltd.（中国市政工程华北设计研究总院有限公司）；Hohai University（河海大学）；Nanjing University（南京大学）；Cornell University（康奈尔大学）；Tongji University（同济大学）；Zhejiang Institute of Hydraulics and Estuary（浙江省水利河口研究院）；Ghent University（根特大学）；University of British Columbia（不列颠哥伦比亚大学）；Beijing Normal University（北京师范大学）；Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工大学）
APS-LSTM: Exploiting Multi-Periodicity and Diverse Spatial Dependencies for Flood Forecasting
Hohai University（河海大学）；Key Laboratory of Water Big Data Technology of Ministry of Water Resources（水利部水大数据技术重点实验室）；College of Computer and Software, Hohai University（河海大学计算机与软件学院）
Research Institute for Infrastructure Paradigm Shift, Yachiyo Engineering Co., Ltd.（基础设施范式转换研究所，八千代工程株式会社）
最近，由于气候变化引起的前所未有的天气条件，洪水灾害已成为一个社会问题。对暴雨立即作出反应对于减轻经济损失和迅速恢复都很重要。时空降水预报可以提高大坝入库流量预测的准确性，提前6小时以上减轻洪水损失。然而，由于目标预测值与地面实况值之间存在不可约的偏差，普通的ConvLSTM在实际降水预报中存在着可预报范围大于3个时间步的局限性。本文提出了一种雨码的时空降水代码到代码的预测方法。我们提出了一种新的雨的功能，表示一个时间雨的过程，使用多帧融合的时间步长减少。我们基于标准ConvLSTM对各种术语范围进行了雨码研究。我们应用了一个坝区内的日本雨季小时降水数据，2006年至2019年下约12.7万小时，每年5月至10月。我们将雷达分析每小时的数据应用于面积为136 x 148 km 2的中央更广泛的区域。最后，我们在几个预报范围内提供了雨码大小和每小时准确度之间的敏感性研究。
Institute of Industrial Science, The University of Tokyo（东京大学工业科学研究所）；Kozo Keikaku Engineering Inc.（构计工程株式会社）；International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo（东京大学国际神经智能研究中心（WPI-IRCN））；Mathematics and Informatics Center, The University of Tokyo（东京大学数学与信息学中心）
Adaptive Bias Correction for Improved Subseasonal Forecasting
University of Toronto（多伦多大学）；Instituto de Matemática Pura e Aplicada（纯数学与应用数学研究所）；nLine Inc.（nLine公司）；Massachusetts Institute of Technology（麻省理工学院）；Woods Hole Oceanographic Institution（伍兹霍尔海洋研究所）；Atmospheric and Environmental Research（大气与环境研究公司）；Cornell University（康奈尔大学）；Microsoft Research New England（微软研究院新英格兰分部）
Integrating Newton's Laws with deep learning for enhanced physics-informed compound flood modelling
Center for Complex Hydrosystems Research（复杂水系统研究中心）；Department of Civil, Construction and Environmental Engineering（土木、建设与环境工程系）；The University of Alabama（阿拉巴马大学）
Adaptive Rainfall Forecasting from Multiple Geographical Models Using Matrix Profile and Ensemble Learning
Ha Noi University of Science and Technology（河内科学技术大学）；Center of Environmental Intelligence（环境智能中心）；VinUniversity（文大学）；University of Southern California（南加州大学）
Experimental study of time series forecasting methods for groundwater level prediction
University Clermont Auvergne（克莱蒙奥弗涅大学）；Clermont Auvergne INP（克莱蒙奥弗涅国立理工学院）；CNRS（法国国家科学研究中心）；Mines Saint-Etienne（圣艾蒂安国立高等矿业学校）；Inria（法国国家信息与自动化研究所）；Université de Tours（图尔大学）；BRGM（法国地质调查局）
Accelerating HEC-RAS: A Recurrent Neural Operator for Rapid River Forecasting
Gulf States Center for Env. Informatics（环境信息中心）；University of New Orleans（新奥尔良大学）；CoDiS-Lab ISDS；Graz Technical University of Technology（格拉茨技术大学）；Center for Geospatial Sciences（地理空间科学中心）；Naval Research Laboratory（海军研究实验室）；US Army Corps of Engineers（美国陆军工程兵团）；Vicksburg District（维克斯堡分局）；New Orleans District（新奥尔良分局）；Graz University of Technology（格拉茨大学）；Ocean Sciences Division（海洋科学分会）
RiverMamba: A State Space Model for Global River Discharge and Flood Forecasting
Institute of Computer Science, University of Bonn（波恩大学计算机科学研究所）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔人工智能与机器学习研究所）；Institute of Bio- and Geosciences Agrosphere (IBG-3), Research Centre Jülich（茹里奇研究中心生物与地质科学农业球面研究所）；Centre for High-Performance Scientific Computing in Terrestrial Systems, Geoverbund ABC/J, Jülich（地球系统高性能科学计算中心，Geoverbund ABC/J，茹里奇）
Enhancing the Forecasting Capability of Multi-Model Blending Algorithms for Extreme Precipitation via Joint Use of Station and Gridded Observations
National Meteorological Center of China Meteorological Administration（中国气象局国家气象中心）；Xiong’an Institute of Meteorological Artificial Intelligence（雄安气象人工智能研究所）；China Meteorological Administration Training Center（中国气象局培训中心）
Quantum-Enhanced Parameter-Efficient Learning for Typhoon Trajectory Forecasting
Graduate Institute of Applied Physics, National Taiwan University（国立台湾大学应用物理研究所）；Department of Electrical and Electronic Engineering, Imperial College London（伦敦帝国理工学院电子与电气工程系）；Centre for Quantum Engineering, Science and Technology (QuEST), Imperial College London（伦敦帝国理工学院量子工程、科学与技术中心）；Institute for Space-Earth Environmental Research, Nagoya University（名古屋大学空间-地球环境研究所）；Wells Fargo（沃尔摩斯银行）；Jij Inc.（Jij公司）；Quantum Information Center, Chung Yuan Christian University（Chung Yuan Christian University量子信息中心）；Master Program in Intelligent Computing and Big Data, Chung Yuan Christian University（Chung Yuan Christian University智能计算与大数据硕士项目）
Agricultural Recommendation System based on Deep Learning: A Multivariate Weather Forecasting Approach
Chittagong University of Engineering & Technology（吉大港工程技术大学）；Khulna University of Engineering & Technology（库尔纳工程技术大学）；University of Illinois at Chicago（芝加哥大学伊利诺伊分校）；Khulna Agricultural University（库尔纳农业大学）；Edith Cowan University（伊迪丝·考恩大学）
Physically consistent and uncertainty-aware learning of spatiotemporal dynamics
Technical University of Munich（慕尼黑技术大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；University of Bristol（布里斯托大学）；Universitat de València（瓦伦西亚大学）；Munich Climate Center（慕尼黑气候中心）
Fine-Tuning Foundation Models with Federated Learning for Privacy Preserving Medical Time Series Forecasting
University of Central Florida（中佛罗里达大学）；KnowledgeVis, LLC（诺维吉斯有限责任公司）；University of Michigan（密歇根大学）；University of California, Davis（加州大学戴维斯分校）；U.S. Army Institute of Surgical Research（美国陆军外科研究所）；University of Central Florida College of Medicine（中佛罗里达大学医学院）
Global spatio-temporal downscaling of ERA5 precipitation through generative AI
Institute of Meteorology and Climate Research - Atmospheric Environmental Research (IMK-IFU)（气象与气候研究所——大气环境研究（IMK-IFU））；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Institute of Meteorology and Climate Research - Atmospheric Trace Gases and Remote Sensing (IMK-ASF)（气象与气候研究所——大气痕量气体与遥感（IMK-ASF））；University of Augsburg（奥格斯堡大学）
Optimization paper production through digitalization by developing an assistance system for machine operators including quality forecast: a concept
Fraunhofer Institute for Process Engineering and Packaging（弗劳恩霍夫过程工程与包装研究所）；Consulting Talents AG（Consulting Talents股份公司）；University Siegen（锡根大学）；LEIPA Group GmbH（LEIPA集团有限公司）
