Space-time forecasting using soft geostatistics: a case study in forecasting municipal water demand for Phoenix, Arizona
Space-time forecasting using soft geostatistics: a case study in forecasting municipal water demand for Phoenix, Arizona
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DOI：
10.1007/s00477-009-0317-z
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发表时间：
2010-02-01
影响因子：
4.2
通讯作者：
Gober, Patricia
中科院分区：
文献类型：
--
作者：
Lee, Seung-Jae;Wentz, Elizabeth A.;Gober, Patricia
面对不确定性，管理环境和社会系统需要对未来状况作出尽可能好的预测。我们使用历史数据和未来人口密度预测的时空变化，以提高预测亚利桑那州凤凰城的居民用水需求。我们未来的水资源估计是使用因变量用水量和自变量人口密度之间的一阶和二阶统计矩得出的。自变量是预测在未来的点，仍然是不确定的。我们使用调整后的统计矩，涵盖自变量中的投影误差，并提出了一种方法来生成信息丰富的未来估计。这些更新的估计数在贝叶斯最大熵（BME）中进行处理，生成到2030年的估计用水量地图。将不确定性估计集成到时空预测过程中，比不同化不确定性估计的其他时空映射方法提高了预测精度高达43.9%。进一步的验证研究表明，BME比整合无误差自变量的协同克里金法更准确，但与处理不确定估计的测量误差克里金法具有相似的准确性。我们提出的预测方法受益于对未来的不确定性估计，提供了最新的用水预测，并可以适应其他社会经济和环境应用。
Managing environmental and social systems in the face of uncertainty requires the best possible forecasts of future conditions. We use space-time variability in historical data and projections of future population density to improve forecasting of residential water demand in the City of Phoenix, Arizona. Our future water estimates are derived using the first and second order statistical moments between a dependent variable, water use, and an independent variable, population density. The independent variable is projected at future points, and remains uncertain. We use adjusted statistical moments that cover projection errors in the independent variable, and propose a methodology to generate information-rich future estimates. These updated estimates are processed in Bayesian Maximum Entropy (BME), which produces maps of estimated water use to the year 2030. Integrating the uncertain estimates into the space-time forecasting process improves forecasting accuracy up to 43.9% over other space-time mapping methods that do not assimilate the uncertain estimates. Further validation studies reveal that BME is more accurate than co-kriging that integrates the error-free independent variable, but shows similar accuracy to kriging with measurement error that processes the uncertain estimates. Our proposed forecasting method benefits from the uncertain estimates of the future, provides up-to-date forecasts of water use, and can be adapted to other socio-economic and environmental applications.