Accurate forecasting of river water levels is vital for effectively managing traffic flow and mitigating the risks associated with natural disasters. This task presents challenges due to the intricate factors influencing the flow of a river. Recent advances in machine learning have introduced numerous effective forecasting methods. However, these methods lack interpretability due to their complex structure, resulting in limited reliability. Addressing this issue, this study proposes a deep learning model that quantifies interpretability, with an emphasis on water level forecasting. This model focuses on generating quantitative interpretability measurements, which align with the common knowledge embedded in the input data. This is facilitated by the utilization of a transformer architecture that is purposefully designed with masking, incorporating a multi-layer network that captures spatiotemporal causation. We perform a comparative analysis on the Han River dataset obtained from Seoul, South Korea, from 2016 to 2021. The results illustrate that our approach offers enhanced interpretability consistent with common knowledge, outperforming competing methods and also enhances robustness against distribution shift.
Clustering and Recognition of Spatiotemporal Features through Interpretable Embedding of Sequence to Sequence Recurrent Neural Networks
Department of Electrical & Computer Engineering（电气与计算机工程系）；Department of Applied Mathematics（应用数学系）；University of Washington（华盛顿大学）；Seattle, WA 98195（西雅图, WA 98195（注意：地址信息按要求不应提取，此处仅为示例说明））
Unifying Physics- and Data-Driven Modeling via Novel Causal Spatiotemporal Graph Neural Network for Interpretable Epidemic Forecasting
Frankfurt Institute for Advanced Studies（法兰克福高等研究院）；Goethe Universität Frankfurt（法兰克福大学）；Xidian-FIAS International Joint Research Center（西电-法兰克福高等研究院国际联合研究中心）；School of Science and Engineering, The Chinese University of Hong Kong（香港中文大学（深圳）理工学院）；GSI Helmholtzzentrum für Schwerionenforschung GmbH（GSI亥姆霍兹重离子研究中心）
Turning Time Series into Algebraic Equations: Symbolic Machine Learning for Interpretable Modeling of Chaotic Time Series
SAFIR, Sorbonne University Abu Dhabi, UAE（SAFIR，索邦大学阿布扎赫德分校）；Sorbonne Center for Artificial Intelligence, Sorbonne University, Paris, France（索邦人工智能中心，索邦大学巴黎分校）
GeoPro-Net: Learning Interpretable Spatiotemporal Prediction Models through Statistically-Guided Geo-Prototyping
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Georgia Institute of Technology（佐治亚理工学院）；University of Iowa（爱荷华大学）；Fudan University（复旦大学）；Logistics and Supply Chain MultiTech R&D Centre（物流与供应链多元技术研发中心）
FDN: Interpretable Spatiotemporal Forecasting with Future Decomposition Networks
Department of Intelligent Systems Engineering, Indiana University Bloomington（印第安纳大学布卢明顿分校智能系统工程系）；Department of Computer Science and Engineering, Texas A&M University（德克萨斯大学阿姆斯特朗分校计算机科学与工程系）
GTS_Forecaster: a novel deep learning based geodetic time series forecasting toolbox with python
School of Transportation Engineering, East China Jiao Tong University（东华交通大学交通运输工程学院）；Jiangxi Provincial Key Laboratory of Water Ecological Conservation in Headwater Regions（江西省头水地区水生态保育重点实验室）；Division of Geodetic Science, School of Earth Sciences, The Ohio State University（地球科学学院地质科学系，俄亥俄州立大学）；Institute Dom Luiz, University of Beira Interior（贝拉内里亚大学多姆路易斯研究所）；Institute of Meteorology and Climatology, Leibniz University Hannover（汉诺威莱比锡大学气象与气候研究所）；Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University（江西省鄱阳湖湿地与流域研究重点实验室，江西省教育厅，江西师范大学）
Regression modelling of spatiotemporal extreme U.S. wildfires via partially-interpretable neural networks
School of Mathematics, University of Edinburgh（爱丁堡大学数学学院）；Statistics Program, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学计算机、电气与数学科学与工程（CEMSE）分部统计项目）
Graph Neural Networks for Improved El Niño Forecasting
Technical University of Darmstadt（达姆施塔特工业大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of São Paulo（圣保罗大学）；TU Munich（慕尼黑工业大学）；Warsaw University of Technology（华沙理工大学）；Duquesne University（杜肯大学）；African Institute for Mathematical Sciences（非洲数学科学研究所）；Massachusetts Institute of Technology（麻省理工学院）
SpatioTemporal Causal Network Diagnostics for Geographic Tipping Point Early Warning
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application（江苏省地理信息资源开发与应用协同创新中心）；National Center for Applied Mathematics, Tianjin University（天津大学国家应用数学中心）
Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling
IDSIA USI-SUPSI（IDSIA USI-SUPSI（瑞士意大利语大学与瑞士应用科学大学联合机构））；Università della Svizzera italiana（瑞士意大利语大学）；Politecnico di Milano（米兰理工大学）；UiT the Arctic University of Norway（挪威北极大学）；NORCE, Norwegian Research Centre AS（NORCE挪威研究中心）
An Interpretable Implicit-Based Approach for Modeling Local Spatial Effects: A Case Study of Global Gross Primary Productivity
College of Urban and Environmental Sciences, Peking University（北京大学城市与环境学院）；The Hong Kong Polytechnic University（香港理工大学）；School of Safety Science, Tsinghua University（清华大学安全科学学院）；College of Surveying and Geo-Informatics, Tongji University（同济大学测绘与地理信息学院）；School of Architecture and Urban Planning, Shenzhen University（深圳大学建筑与城市规划学院）
Weaver: Kronecker Product Approximations of Spatiotemporal Attention for Traffic Network Forecasting
Department of Civil, Environmental, and Geo- Engineering, University of Minnesota, Twin Cities, 500 Pillsbury Drive S.E., Minneapolis, MN 55455-0116, United States of America（土木、环境与地球工程系，明尼苏达大学，双城校区）
Spatiotemporal Attention-Augmented Inverse Reinforcement Learning for Multi-Agent Task Allocation
College of Electronic and Information Engineering, Tongji University（同济大学电子与信息工程学院）；Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University（同济大学智能自主系统上海研究院）；Graz University of Technology（格拉茨技术大学）；Virtual Vehicle Research GmbH（虚拟车辆研究公司）
CrossTrafficLLM: A Human-Centric Framework for Interpretable Traffic Intelligence via Large Language Model
Beijing Key Laboratory of Multimedia and Intelligent Software Technology（北京多媒体与智能软件技术重点实验室）；Beijing Artificial Intelligence Institute（北京人工智能研究院）；Faculty of Information Technology（信息科技学院）；Beijing University of Technology（北京工业大学）
