Application of Data Fusion for Uncertainty and Sensitivity Analysis of Water Quality in the Shenandoah River

Mbongowo Joseph Mbuh
Additional contact information
Mbongowo Joseph Mbuh: University of North Dakota, Grand Forks, USA

International Journal of Applied Geospatial Research (IJAGR), 2018, vol. 9, issue 3, 31-54

Abstract: This article is aimed at demonstrating the feasibility of combining water quality observations with modeling using data fusion techniques for efficient nutrients monitoring in the Shenandoah River (SR). It explores the hypothesis; “Sensitivity and uncertainty from water quality modeling and field observation can be improved through data fusion for a better prediction of water quality.” It models water quality using water quality simulation programs and combines the results with field observation, using a Kalman filter (KF). The results show that the analysis can be improved by using more observations in watersheds where minor variations to the analysis result in large differences in the subsequent forecast. Analyses also show that while data fusion was an invaluable tool to reduce uncertainty, an improvement in the temporal scales would also enhance results and reduce uncertainty. To examine how changes in the field observation affects the final KF analysis, the fusion and lab analysis cross-validation showed some improvement in the results with a very high coefficient of determination.

Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/IJAGR.2018070103 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jagr00:v:9:y:2018:i:3:p:31-54

Access Statistics for this article

International Journal of Applied Geospatial Research (IJAGR) is currently edited by Donald Patrick Albert

More articles in International Journal of Applied Geospatial Research (IJAGR) from IGI Global
Bibliographic data for series maintained by Journal Editor ().