With water quality management processes, identifying and interpreting relationships between features, such as location and weather variable tuples, and water quality variables, such as levels of bacteria, is key to gaining insights and identifying areas where interventions should be made. There is a need for a search process to identify the locations and types of phenomena that are influencing water quality and a need to explain how the quality is being affected and which factors are most relevant. This paper addresses both of these issues. A process is developed for collecting data for features that represent a variety of variables over a spatial region and which are used for training models and inference. An analysis of the performance of the features is undertaken using the models and Shapley values. Shapley values originated in cooperative game theory and can be used to aid in the interpretation of machine learning results. Evaluations are performed using several machine learning algorithms and water quality data from the Dublin Grand Canal basin.
Data Pre-Processing and Evaluating the Performance of Several Data Mining Methods for Predicting Irrigation Water Requirement
School of Environmental Sciences, Charles Sturt University（查尔斯斯特大学环境科学学院）；School of Computing and Mathematics, Charles Sturt University（查尔斯斯特大学计算与数学学院）；International Water Management Institute (IWMI)（国际水资源管理研究所）
Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Saarland University（萨尔大学）
MAIR: Framework for mining relationships between research articles, strategies, and regulations in the field of explainable artificial intelligence
MI 2 Data Lab（MI 2数据实验室）；Faculty of Mathematics and Information Science, Warsaw University of Technology（华沙理工大学数学与信息科学学院）；Faculty of Mathematics, Informatics and Mechanics, University of Warsaw（华沙大学数学、信息学与力学学院）；Faculty of Political Science and International Studies, University of Warsaw（华沙大学政治科学与国际研究学院）；Faculty of Physics, Warsaw University of Technology（华沙理工大学物理学院）；Faculty of Economics, University of Warsaw（华沙大学经济学院）
Explainable Fuzzy GNNs for Leak Detection in Water Distribution Networks
Jheronimus Academy of Data Science, Eindhoven University of Technology, Eindhoven, The Netherlands University of Bari Aldi Moro, Bari, Italy IHE Institute for Water Education, Delft, The Netherlands University of Cartagena, Cartagena, Spain School of Industrial Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
Investigating ADR mechanisms with knowledge graph mining and explainable AI
Université de Lorraine（洛林大学）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；LORIA（洛林计算机科学及其应用实验室）；Orange Labs（奥兰治实验室）；LIMICS（LIMICS实验室）；Inserm（法国国家健康与医学研究院）；Université Sorbonne Paris Nord（索邦巴黎北大学）；Sorbonne Université（索邦大学）；CHU de St Etienne（圣艾蒂安大学附属医院）；NGERE（NGERE实验室）；Centre Régional de Pharmacovigilance（区域药物警戒中心）；CHRU de Nancy（南锡大学区域医院中心）；Inria Paris（法国国家信息与自动化研究所巴黎中心）；Université de Paris（巴黎大学）
