EM-T1Reliability Engineering & System Safety2027278: 113557Topology-preserving environmental regimes integrated with machine learning and explainable AI for wildfire susceptibility assessmentAbolfazl Jaafari, Ahmad Hosseini, Binh Thai Pham被引 0基金全球变化金融风险与机器学习
EM-WoSExpert Systems with Applications2027333: 133997Physics-guided residual deep learning with multi-source feature fusion for robust groundwater level forecastingFi-John Chang, Li-Chiu Chang, Jun-Jie Lin, Wei Sun 等 5 人被引 0阅读 1环境工程金融风险与机器学习
EM-WoSTransportation Research Part E: Logistics and Transportation Review2027217: 105253A machine learning-enhanced algorithm incorporating domain knowledge for sustainable maritime transportHaoqing Wang, Yan Liu, Shuaian Wang, Lu Zhen被引 0阅读 4环境工程金融风险与机器学习
EM-WoSInformation Sciences2026757: 123941A novel deep learning framework integrating temporal convolution and bidirectional LSTM for multi-city AQI forecastingJunjie Xiong, Chengbin Liang, Deguang Wang, Ming Yang被引 1环境工程金融风险与机器学习
EM-T1Natural Hazards Review202627(4)Research Hotspots and Evolutionary Trends ofEarthquake-Induced Landslides: A BibliometricAnalysis Based on Web of ScienceQing Yang, Xiangli He, Chong Xu, Yuandong Huang 等 7 人被引 0阅读 1开放获取环境管理、监测、政策与法规复合灾害与灾害链应急治理与韧性政策金融风险与机器学习
EM-T1Natural Hazards Review202627(4)From Rainfall to Absolute Landslide Probability: A Machine Learning–Based ApproachJiale Jin, Zhiwen Xue, Chong Xu, Lei Li 等 13 人被引 0环境管理、监测、政策与法规应急物资配置与人道物流复合灾害与灾害链洪涝风险与减灾应急治理与韧性政策金融风险与机器学习
EM-T1Natural Hazards Review202627(4)Deep Learning LSTM-Based Model for Predicting SPI and SPEI Drought IndicesMohammad Hadi Fattahi, Tajeddin Derikvand, Farhang Rahmani被引 1高影响力 前 10%全球变化金融风险与机器学习
EM-T1Natural Hazards2026122(22)Diagnosing region-specific flood influencing factors in a volcanic island using explainable machine learningHyeon‐Tae Moon, Gilho Kim, Kyungtak Kim被引 0基金开放获取全球变化洪涝风险与减灾金融风险与机器学习
EM-T1Journal of Flood Risk Management202619(4)Rethinking Potential Flood Damage Assessment: A Data‐Driven Alternative to Varnes‐Based ModelsFederica Zambrini, Daniele Fabrizio Bignami, Daniele Bocchiola, Enrico Maria Nava 等 5 人被引 0基金开放获取全球变化洪涝风险与减灾金融风险与机器学习
EM-T1Geomatics, Natural Hazards and Risk202617(1)Integrated assessment of flood susceptibility and population exposure using machine learning: a case study of Wajima, JapanNoriyasu Tsumita, Kazuki Terayama, Suwanno Piyapong, Aditya Mahatidanar Hidayat被引 0基金开放获取全球变化洪涝风险与减灾金融风险与机器学习
EM-T2Journal of Hazardous Materials2026143881-143881An explainable machine learning framework for predicting colloid-mediated release of potentially toxic elements from Pb/Zn smelter contaminated soilsXU Da-mao, Haibin Wang, Rui-Yang Yong, Xiao-Lan Yi 等 9 人被引 0基金污染金融风险与机器学习
EM-T1Natural Hazards and Earth System Sciences202626(10): 4675-4721Spatial machine learning modelling reveals that soil indicators and tree type best explain shallow landslide releaseDenise Christina Rüther, Kristine Flacké Haualand, Iris Louisa Johanna Peeters, Mark Andrew Kusk Gillespie被引 0基金开放获取有全文环境管理、监测、政策与法规复合灾害与灾害链金融风险与机器学习
EM-T2Process Safety and Environmental Protection2026219: 109441Physics-constrained machine learning for scenario-based prediction of far-distance radionuclide attenuation at the Lufeng Nuclear Power PlantTemidayo Olayinka Alex-Oke, Olusola Bamisile, Dongsheng Cai, Osamong Gideon Akou 等 6 人被引 0阅读 2开放获取全球变化不确定性优化（随机/鲁棒/DRO/CVaR）金融风险与机器学习
EM-T2Process Safety and Environmental Protection2026219: 109497A decision-ready framework linking soil-dependent stabilization prediction to iron-based remediation process design for As and Sb-contaminated soils via explainable machine learningRuiyuan Xue, Qingliang Zhao, Liangliang Wei, Miao Jiang 等 9 人被引 0阅读 2污染金融风险与机器学习
EM-T2Journal of Hazardous Materials2026517: 143727Pathways, evidence types, and interpretive boundaries in machine learning-assisted source analysis of soil metal(loid)sQuan Zou, Yingming Wang, Zhenyang Han, Jie Shi 等 7 人被引 0基金污染金融风险与机器学习
EM-T2Journal of Hazardous Materials2026517: 143536综述Machine learning and remote sensing for soil heavy metal(loid) risk assessment: A systematic review of multi-source data fusion, benchmarking, and explainable AIMd Tashfique Enam Tutul, Shuvo Dip Datta被引 0污染金融风险与机器学习
EM-T2Journal of Hazardous Materials2026517: 143445Leveraging multi-source observations for vertical PM2.5 estimation: A deep learning fusion modelZhenyi Chen, Feilong Li, Qiankai Jin, Zhiliang Yao 等 13 人被引 0基金环境工程金融风险与机器学习
EM-T2Journal of Hazardous Materials2026517: 143506Interpretable machine learning predictions reveal climate-driven escalation of arsenic in Tibetan alpine headwater riversLei Chai, Juyi Sun, Huimin Yu, Xiaoping Wang 等 8 人被引 0基金环境化学金融风险与机器学习
EM-T1Geomatics, Natural Hazards and Risk202617(1)Machine learning surrogates for flash flood hazard mapping: CSI-optimized feature selection and spatial transferabilityAman Arora, Pierre Nicolle, Olivier Payrastre被引 0开放获取全球变化洪涝风险与减灾金融风险与机器学习
EM-T1Journal of Flood Risk Management202619(4)A Machine Learning Framework for Flood Risk Assessment With Imbalanced DataAlexia Tsouni, Constantinos F. Panagiotou, Charalampos Konstantinou, Vasiliki Pagana 等 8 人被引 1阅读 1高影响力 前 10%基金开放获取全球变化洪涝风险与减灾金融风险与机器学习
