| Content Provider |
World Health Organization (WHO)-Global Index Medicus |
| Author |
Woznicki, Sean A. Wang, Lizhu Nejadhashemi, A. Pouyan Ross, Dennis M. Esfahanian, Abdol-Hossein Zhang, Zhen |
| Spatial Coverage |
Michigan |
| Description |
Country affiliation: United States Author Affiliation: Woznicki SA ( Department of Biosystems and Agricultural Engineering, 524 S. Shaw Lane, Room 216, Michigan State University, East Lansing, MI 48824, USA.); Nejadhashemi AP ( Department of Biosystems and Agricultural Engineering, 524 S. Shaw Lane, Room 216, Michigan State University, East Lansing, MI 48824, USA. Electronic address: pouyan@msu.edu.); Ross DM ( Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.); Zhang Z ( Physical Sciences Division, Department of Statistics, University of Chicago, Chicago, IL 60637, USA.); Wang L ( International Joint Commission, Great Lakes Office, Windsor, ON N9A 6T3, Canada.); Esfahanian AH ( Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.) |
| Abstract |
Variable selection is a critical step in development of empirical stream health prediction models. This study develops a framework for selecting important in-stream variables to predict four measures of biological integrity: total number of Ephemeroptera, Plecoptera, and Trichoptera (EPT) taxa, family index of biotic integrity (FIBI), Hilsenhoff biotic integrity (HBI), and fish index of biotic integrity (IBI). Over 200 flow regime and water quality variables were calculated using the Hydrologic Index Tool (HIT) and Soil and Water Assessment Tool (SWAT). Streams of the River Raisin watershed in Michigan were grouped using the Strahler stream classification system (orders 1-3 and orders 4-6), k-means clustering technique (two clusters: C1 and C2), and all streams (one grouping). For each grouping, variable selection was performed using Bayesian variable selection, principal component analysis, and Spearman's rank correlation. Following selection of best variable sets, models were developed to predict the measures of biological integrity using adaptive-neuro fuzzy inference systems (ANFIS), a technique well-suited to complex, nonlinear ecological problems. Multiple unique variable sets were identified, all which differed by selection method and stream grouping. Final best models were mostly built using the Bayesian variable selection method. The most effective stream grouping method varied by health measure, although k-means clustering and grouping by stream order were always superior to models built without grouping. Commonly selected variables were related to streamflow magnitude, rate of change, and seasonal nitrate concentration. Each best model was effective in simulating stream health observations, with EPT taxa validation R2 ranging from 0.67 to 0.92, FIBI ranging from 0.49 to 0.85, HBI from 0.56 to 0.75, and fish IBI at 0.99 for all best models. The comprehensive variable selection and modeling process proposed here is a robust method that extends our understanding of watershed scale stream health beyond sparse monitoring points. |
| ISSN |
00489697 |
| Volume Number |
511 |
| e-ISSN |
18791026 |
| Journal |
Science of The Total Environment |
| Language |
English |
| Publisher |
Elsevier |
| Publisher Date |
2015-04-01 |
| Publisher Place |
Netherlands |
| Access Restriction |
One Nation One Subscription (ONOS) |
| Subject Keyword |
Environmental Monitoring Methods Rivers Water Pollution Statistics & Numerical Data Animals Bayes Theorem Biodiversity Ecosystem Fishes Hydrology Insects Michigan Models, Theoretical Analysis Journal Article Research Support, U.s. Gov't, Non-p.h.s. Discipline Environmental Science |
| Content Type |
Text |
| Resource Type |
Article |
| Subject |
Environmental Chemistry Waste Management and Disposal Pollution Environmental Engineering |
