NDLI: Space–time forecasting using soft geostatistics: a case study in forecasting municipal water demand for Phoenix, Arizona

Content Provider	Springer Nature Link
Author	Lee, Seung Jae Wentz, Elizabeth A. Gober, Patricia
Copyright Year	2009
Abstract	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.
Starting Page	283
Ending Page	295
Page Count	13
File Format	PDF
ISSN	14363240
Journal	Stochastic Environmental Research and Risk Assessment
Volume Number	24
Issue Number	2
e-ISSN	14363259
Language	English
Publisher	Springer-Verlag
Publisher Date	2009-05-26
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Water use Forecasting Soft data Statistical moments Bayesian Maximum Entropy Waste Water Technology / Water Pollution Control / Water Management / Aquatic Pollution Computational Intelligence Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Mathematical Applications in Earth Sciences Probability Theory and Stochastic Processes Math. Application in Environmental Science
Content Type	Text
Resource Type	Article
Subject	Environmental Chemistry Environmental Engineering Water Science and Technology Safety, Risk, Reliability and Quality

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Space and time dynamics of urban water demand in Portland, Oregon and Phoenix, Arizona

Combining categorical and continuous spatial information within the Bayesian maximum entropy paradigm

A spatiotemporal dengue fever early warning model accounting for nonlinear associations with hydrological factors: a Bayesian maximum entropy approach

Entropy-based parameter estimation for extended Burr XII distribution

Application of minimum relative entropy theory for streamflow forecasting

Minimum/maximum autocorrelation factors applied to grade estimation

Compositional Bayesian indicator estimation

Mass fraction spatiotemporal geostatistics and its application to map atmospheric polycyclic aromatic hydrocarbons after 9/11

A regional Bayesian hierarchical model for flood frequency analysis

Space–time forecasting using soft geostatistics: a case study in forecasting municipal water demand for Phoenix, Arizona

Similar Documents

Space and time dynamics of urban water demand in Portland, Oregon and Phoenix, Arizona

Combining categorical and continuous spatial information within the Bayesian maximum entropy paradigm

A spatiotemporal dengue fever early warning model accounting for nonlinear associations with hydrological factors: a Bayesian maximum entropy approach

Entropy-based parameter estimation for extended Burr XII distribution

Application of minimum relative entropy theory for streamflow forecasting

Minimum/maximum autocorrelation factors applied to grade estimation

Compositional Bayesian indicator estimation

Mass fraction spatiotemporal geostatistics and its application to map atmospheric polycyclic aromatic hydrocarbons after 9/11

A regional Bayesian hierarchical model for flood frequency analysis

Space–time forecasting using soft geostatistics: a case study in forecasting municipal water demand for Phoenix, Arizona