Dailisan Damian, Liponhay Marissa, Alis Christian, Monterola Christopher
Analytics, Computing, and Complex Systems Laboratory (ACCeSs@AIM), Asian Institute of Management, Makati City, National Capital Region, Philippines.
PLoS One. 2022 Mar 18;17(3):e0265771. doi: 10.1371/journal.pone.0265771. eCollection 2022.
Anticipating the increase in water demand in an urban area requires us to properly understand daily human movement driven by population size, land use, and amenity types among others. Mobility data from phones can capture human movement, but not only is this hard to obtain, but it also does not tell where the population is going. Previous studies have shown that amenity types can be used to predict people's movement patterns; thus, we propose using crowd-sourced amenity data and other open data sources as reasonable proxies for human mobility. Here we present a framework for predicting water consumption in areas with established service water connections and generalize it to underserved areas. Our work used features such as geography, population, and domestic consumption ratio and compared the prediction performance of various machine learning algorithms. We used 44 months of monthly water consumption data from January 2018 to July 2021, aggregated across 1790 district metering areas (DMAs) in the east service zone of Metro Manila. Results show that amenity counts reduce the mean absolute error (MAE) of predictions by 1,440 m3/month or as much as 5.73% compared to just using population and topology features. Predicted consumption during the pandemic also improved by as much as 1,447 m3/month or nearly 16% compared to just using population and topology features. We find that Gradient Boosting Trees are the best models to handle the data and feature set used in this work. Finally, the developed model is robust to disruptions in human mobility, such as lockdowns, indicating that amenities are sufficient to predict water consumption.
预测城市地区的用水需求增长，需要我们正确理解由人口规模、土地利用和便利设施类型等因素驱动的日常人类活动。手机的移动性数据可以捕捉人类活动，但这不仅难以获取，而且无法告知人口的去向。先前的研究表明，便利设施类型可用于预测人们的活动模式；因此，我们建议使用众包的便利设施数据和其他开放数据源作为人类流动性的合理替代指标。在此，我们提出一个框架，用于预测已建立供水连接地区的用水量，并将其推广到服务不足的地区。我们的工作使用了地理、人口和家庭消费比率等特征，并比较了各种机器学习算法的预测性能。我们使用了2018年1月至2021年7月期间44个月的月度用水量数据，这些数据是马尼拉大都会东部服务区1790个分区计量区（DMA）的汇总数据。结果表明，与仅使用人口和拓扑特征相比，便利设施数量可将预测的平均绝对误差（MAE）降低1440立方米/月，降幅高达5.73%。与仅使用人口和拓扑特征相比，疫情期间的预测用水量也提高了1447立方米/月，增幅近16%。我们发现梯度提升树是处理这项工作中使用的数据和特征集的最佳模型。最后，所开发的模型对人类流动性的干扰（如封锁）具有鲁棒性，这表明便利设施足以预测用水量。
