本文讨论了人工神经网络（ANN）技术是如何发挥作用的 作为一个有价值的工具，在一个正在进行的住宅水资源保护计划的选择 加拿大艾伯塔省埃德蒙顿保护项目。这项研究的重点是 确定居民用水的重要影响因素并使用 通过公共教育计划来最好地影响学生行为的信息 居民用水者。人工智能（AI）被内部用于创建一个预测居民用水的人工神经网络模型 消费水平。创建模型最重要的步骤之一是确定 影响中国居民用水的统计显著因素 埃德蒙顿在过去30年里。该模型现已完成，可用于预测 消费预测基于这些因素的各种组合。人工神经网络模型 提供了影响用水量的主要因素清单，以及 基于各种因素的一系列值运行多个场景。每个 该情景产生了消费预测。 这些变化的程度 对消费预测进行分析，以确定哪些因素对消费影响最大。这些因素对居住秩序的影响意义重大 消费证明是: 基本消费指数（衡量全年最低使用量的指标）； 天气（夏季和冬季）；和 客户数量（可以是最重要的，也可以是最不重要的，取决于年份跨度） 研究）。确定 主要居民消费驱动因素的重要性顺序有助于确定 可通过公共水资源保护措施针对这些因素。ANN技术提供了消除选项和集中注意力所需的信息 为社区提供最有效、最具成本效益的项目资源。为了制定有针对性的住宅节水方案，本研究进行了以下研究: 这5个问题: 是具有合理准确度的预测消费量的人工神经网络模型； 可能的居民用水量范围是多少（使用了这些数值） 作为对以后计算的检查）； 哪些因素驱动居民用水（这些因素已确定） 在开发ANN住宅用水量预测模型期间）； 每个消费驱动因素的变化会对居民用水总量产生多大影响 消耗量（通过使用 神经网络模型）；和 哪些因素会受到节水运动的影响？ 包括6个参考文献、图表。

This paper discusses how Artificial Neural Network (ANN) technology served as a valuable tool in making conservation program choices in an ongoing residential water conservation program for Edmonton, Alberta, Canada. The focus of this study was to identify significant influencing factors for residential water consumption and use this information to adopt public education programs that would best influence the behavior of residential water users. Artificial Intelligence (AI) was used in-house to create an ANN model that forecasts residential water consumption levels. One of the most important steps in creating the model was determining the statistically significant factors that have affected residential water consumption in Edmonton over the last 30 years. Now complete, the model can be used to predict consumption forecasts based on various combinations of these factors. The ANN model provided the list of the main factors that influence water consumption as well as a means for running multiple scenarios based on a range of values for the various factors. Each scenario produced a consumption prediction. The degree of variability of these consumption predictions was analyzed to determine which factors have the greatest impact on consumption. The order of significance of impact of these factors on residential consumption proved to be: base consumption index (a measure of the lowest year-round minimum usage); weather (summer and winter months); and, customer count (can be most or least significant, depending on the span of years studied). Determining the order of significance of the main residential consumption drivers helped identify which factors could be targeted through public water conservation initiatives. ANN technology provided the information necessary to eliminate options and to focus resources on the most effective and cost efficient programs for the community. In order to create a targeted residential water conservation program, the study researched these 5 questions: is the ANN model predicting consumption with a reasonable degree of accuracy; what is the range of possible residential water consumption (these values were used as a check on later calculations); what factors drive residential water consumption (these factors were determined during development of an ANN residential water consumption forecasting model); how much can the changes in each consumption driver affect total residential water consumption (the degree of variability was assessed by running scenarios with the ANN model); and, which factors can be influenced by water conservation campaigns? Includes 6 references, figures.