自来水公司的主要目的是提供安全饮用水 向客户供水，但它还必须为客户的需求制定计划 未来的水需求。这项计划的一个关键方面 预测短期（峰值）需水量并优化 供水系统必须满足这些需求。杰恩和奥姆斯比 评估了八个模型（四个常规模型和四个人工模型） 用于预测短期需水量的智能[AI]）。模型 使用每日需水量、每日 最高气温和每日总降雨量。 人工智能模型的表现优于传统模型，并预测 “正常”和干旱条件下的短期需水量 相对准确。这些人工智能模型可以使用商业软件运行 软件，如果系统操作员有使用经验 模型，或简单的基于规则的人工智能模型可以很容易地开发 并使用历史需求、天气数据和标准 电子表格程序。作者鼓励公用事业运营商或 管理者需要将短期水需求预测纳入其中 模拟他们对未来用水需求的预测。 包括25个参考文献、表格和图表。

A water utility's primary purpose is to provide safe drinking water to its customers, but it must also plan for its customers' future water needs. A critical aspect of this planning is predicting short-term (peak) water demands and optimizing the water supply system to meet these demands. Jain and Ormsbee evaluated eight models (four conventional and four artificial intelligence [AI]) to forecast short-term water demand. The models were developed and tested using daily water demand, daily maximum air temperature, and daily total rainfall. AI models outperformed the conventional models and predicted short-term water demands during both "normal" and drought conditions relatively accurately. These AI models can be run using commercial software, if the system operator is experienced in using models, or simple rule-based AI models can be easily developed and applied using historical demand, weather data, and a standard spreadsheet program. The authors encourage utility operators or managers to incorporate a short-term water-demand forecasting model into their predictions of future water needs. Includes 25 references, tables, figures.