Most pattern classification systems are usually developed based on the training of historical data, and as a result, the performance of these models relies heavily on the amount of collected information. However, in many cases, such data collection process is relatively costly, which eventually limits the efficiency as well as the widespread implementation of the final developed model. In this context, the paper focuses on presenting an advanced universal water management system, which could interface with both water consumers and utilities via smart phone and web application. Originally, Autoflow©, a prototype tool that is used to disaggregate total water consumption into each end-use category was developed, which achieved an accuracy ranging from 74 to 94%. However, a drawback of this model was that it was trained with data collected from only Australia; therefore, accuracy reductions would likely be observed when this system is implemented in different countries having very different water using appliances and behaviour patterns. To avoid the costly data collection process for model calibration when operating in new regions, this research study introduces an enhanced model, namely AutoflowU (i.e. U stands for Universal). This new tool can be applied in residential properties globally to autonomously disaggregate water consumption into the seven main water end-use categories, namely: shower, toilet, tap, clothes washer, dishwasher, evaporative air cooler and irrigation, without the need for collecting new regional end-use data for model calibration. In order to develop this new tool, Decision Trees, Dynamic Time Warping (DTW), Self Organising Map (SOM) and Hidden Markov Model (HMM) techniques were utilised. The test results obtained from 230 properties in both Australia and the US showed that the AutoflowU achieved 72–93% accuracy.

中文翻译：

---

水资源最终用途的自主监测和管理的自适应模型

大多数模式分类系统通常是基于对历史数据的训练而开发的，因此，这些模型的性能在很大程度上取决于收集的信息量。但是，在许多情况下，这样的数据收集过程相对昂贵，最终限制了效率以及最终开发模型的广泛实施。在这种情况下，本文重点介绍了一种先进的通用水管理系统，该系统可以通过智能手机和Web应用程序与用水者和公用事业部门对接。最初，Autoflow©是一种原型工具，用于将总耗水量分解为每个最终用途类别，其精度达到74％至94％。然而，这种模式的一个缺点是仅使用从澳大利亚收集的数据进行了训练。因此，当在使用水具和行为方式差异很大的不同国家/地区实施该系统时，可能会发现精度下降。为了避免在新地区运行时进行模型校准所需的昂贵数据收集过程，本研究引入了一种增强模型，即AutoflowU（即U代表通用）。此新工具可在全球范围内应用于住宅物业，以将用水量自动分类为七个主要的最终用水类别，即：淋浴，厕所，水龙头，洗衣机，洗碗机，蒸发式空气冷却器和灌溉，而无需收集新的用于模型校准的区域最终用途数据。为了开发这种新工具，决策树，利用了动态时间规整（DTW），自组织映射（SOM）和隐马尔可夫模型（HMM）技术。从澳大利亚和美国的230家物业获得的测试结果表明，AutoflowU达到了72–93％的准确度。