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 why the quality is being affected and which factors are most relevant. This paper addresses both of these issues through the development of a process for collecting data for features that represent a variety of variables over a spatial region, which are used for training and inference, and analysing the performance of the features using the model 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.

本篇文章阐述了在水质管理过程中，通过搜索现象的位置与天气变量集对水质变量进行识别和解释的需要性，并提出了以机器学习算法和Shapley值来帮助解释水质数据，使用不同算法和都柏林运河数据进行了相关性能评估。

挖掘可解释的预测特征用于水质管理