EM-T1Natural Hazards and Earth System Sciences202626(7): 3505-3521Use of nonlinear principal components of CHIRPS precipitation data and ocean–atmospheric variables for streamflow forecasting in an area of scarce data – case study: Tocaría river basin – Orinoquia ColombianaJhon Derly Sarria-Ospina, Camilo Ocampo-Marulanda, Lina Maria Ceron-Aramburo, Teresita Canchala 等 5 人被引 1高影响力 前 10%基金开放获取有全文环境工程疏散行为与风险认知
EM-T1Natural Hazards and Earth System Sciences202626(5): 2353-2366Enhancing hydrological hazard early warning: a 60 d streamflow forecasting framework integrating deep learning and process-based modelingZhijie Liu, Hanbo Yang, Dawen Yang被引 0基金开放获取环境工程洪涝风险与减灾疏散行为与风险认知金融风险与机器学习
EM-T1Journal of Flood Risk Management202518(4)Towards Accurate Flood Forecasting: Integrating Satellite Data, Hydrological Modeling, and Deep LearningSaeideh Pourentezari, Hossein Salehi, Alireza Razeghi Haghighi, Mojtaba Sadeghi 等 5 人被引 3开放获取环境工程洪涝风险与减灾疏散行为与风险认知金融风险与机器学习
EM-T1Stochastic Environmental Research and Risk Assessment202539(12): 6001-6018Integrating a hybrid statistical Downscaling-based HMM-RF model for enhanced rainfall prediction in SelangorNoor Hamizah Mohamad Sani, Shazlyn Milleana Shaharudin, Muhammad bin Ibrahim, Upmanu Lall 等 5 人被引 0基金开放获取环境工程疏散行为与风险认知
EM-T1Natural Hazards2025121(18): 21459-21482A residual correction approach for improving rainfall-runoff model performance in flood early warning systemsHaneul Lee, Seungmin Lee, Hoyong Lee, Narae Kang 等 5 人被引 6基金开放获取环境工程洪涝风险与减灾疏散行为与风险认知
EM-T1Stochastic Environmental Research and Risk Assessment202539(5): 2203-2229Hybrid CNN–GRU model for hourly flood forecasting index: case studies from the Fiji islandsRavinesh Chand Deo, Ravinesh Chand Deo, Sujan Ghimire, Thong Nguyen‐Huy 等 5 人被引 14高影响力 前 10%基金开放获取环境工程应急物资配置与人道物流洪涝风险与减灾疏散行为与风险认知金融风险与机器学习
EM-T1Natural Hazards and Earth System Sciences202525(1): 41-47Brief communication: Training of AI-based nowcasting models for rainfall early warning should take into account user requirementsGeorgy Ayzel, Maik Heistermann被引 7阅读 2基金开放获取环境工程疏散行为与风险认知金融风险与机器学习
EM-T1Natural Hazards2024121(6): 7229-7263Machine learning strategies for multiannual rainfall prediction and drought early warning: insights from Ceará, BrazilLarissa Zaira Rafael Rolim, Francisco de Assis de Souza Filho被引 3基金环境工程疏散行为与风险认知金融风险与机器学习
EM-T1Natural Hazards2024121(4): 4615-4638Early warning system for floods at estuarine areas: combining artificial intelligence with process-based modelsWillian Weber de Melo, Isabel Iglesias, José L. S. Pinho被引 11基金开放获取环境工程洪涝风险与减灾疏散行为与风险认知金融风险与机器学习
EM-T1Journal of Flood Risk Management202317(1)Real‐time prediction and ponding process early warning method at urban flood points based on different deep learning methodsYihong Zhou, Zening Wu, Mengmeng Jiang, Hongshi Xu 等 8 人被引 15阅读 2基金开放获取环境工程洪涝风险与减灾疏散行为与风险认知金融风险与机器学习
EM-T1Natural Hazards and Earth System Sciences202222(12): 4139-4165A multi-strategy-mode waterlogging-prediction framework for urban flood depthZongjia Zhang, Jun Liang, Yujue Zhou, Zhejun Huang 等 7 人被引 25基金开放获取环境工程洪涝风险与减灾疏散行为与风险认知金融风险与机器学习
EM-T1Natural Hazards and Earth System Sciences202222(12): 3859-3874Comparison of machine learning techniques for reservoir outflow forecastingOrlando García-Feal, José González-Cao, Diego Fernández-Nóvoa, Gonzalo Astray Dopazo 等 5 人被引 35阅读 1基金开放获取环境工程洪涝风险与减灾疏散行为与风险认知金融风险与机器学习
EM-T1Stochastic Environmental Research and Risk Assessment202237(1): 275-290Surrogate modelling for the forecast of Seveso-type atmospheric pollutant dispersionJuš Kocijan, Nadja Hvala, Matija Perne, Primož Mlakar 等 6 人被引 8基金开放获取环境工程疏散行为与风险认知
EM-T1Stochastic Environmental Research and Risk Assessment202236(10): 3417-3437Two-stage deep learning hybrid framework based on multi-factor multi-scale and intelligent optimization for air pollutant prediction and early warningJujie Wang, Wenjie Xu, Jian Dong, Yue Zhang被引 22基金开放获取环境工程疏散行为与风险认知金融风险与机器学习
EM-T1Natural Hazards2021107(3): 2555-2572Bayesian network reasoning and machine learning with multiple data features: air pollution risk monitoring and early warningXiao‐Liang Xie, Jinxia Zuo, Bingqi Xie, T. Dooling 等 5 人被引 16基金环境工程疏散行为与风险认知金融风险与机器学习
