A Learning Vector Quantization Based Geospatial Modeling Approach for Inland WQ Remote Prediction

Authors

Publication Date

2018

Keywords

LVQ, Universal Kriging, geotechnology (remote sensing, GIS, GPS), chlorophyll, suspended matter

Abstract

Quick and accurate quantification of lake water quality (WQ) is essential for its management and improvement. Use of geotechnology (remote sensing, GIS, and GPS) applications is a step forward in improving our ability to effectively quantify and manage the WQ of ungauged lakes. Beaver Reservoir, a drinking water source for over 280,000 people in northwest Arkansas, is facing increased chlorophyll-a (Chl-a) and suspended matter (SM) content in the lake. This study is designed to qualitatively predict the Chl-a and SM content in the lake on a spatial basis from Landsat-TM image digital information. A Learning Vector Quantization (LVQ) classification neural model was used to predict the qualitative (oligotrophic, moderately oligotrophic, and mildly trophic) classes for several spatial positions in the lake. The geostatistical tool in ArcGIS was used to spatially map the Chl-a and SM extent around the lake. The LVQ classification model predicted the Chl-a extent with more than 90% accuracy having only one point misclassified out of 14 testing points. The LVQ model prediction for SM resulted in four misclassified points out of 14 testing points with prediction accuracy of 72%. The final spatial zoning maps for Chl-a and SM extent in the entire lake could be used to help water use managers and end users design management strategies, and also demonstrates a relatively low cost WQ prediction mechanism that can be applied in developing countries and elsewhere when detailed in-situ monitoring is not feasible.

BYU ScholarsArchive Citation

(2018) "A Learning Vector Quantization Based Geospatial Modeling Approach for Inland WQ Remote Prediction," Journal of Spatial Hydrology: Vol. 14: No. 2, Article 1.
Available at: https://scholarsarchive.byu.edu/josh/vol14/iss2/1