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Application of machine learning for soil survey updates: A case study in southeastern Ohio

Subburayalu, Sakthi Kumaran
http://rave.ohiolink.edu/etdc/view?acc_num=osu1199992659

Abstract Details

2008, Doctor of Philosophy, Ohio State University, Soil Science.
Machine learning techniques were used to build predictive soil-landscape models for two counties (Monroe and Noble) in southeastern Ohio. Twenty five different environmental correlates including 10m resolution raster coverages of terrain and its derivatives, climate, geology, and historic vegetation were used as predictor variables for soil class. Randomly sampled points proportionate to the area of the different soil classes from the published soil survey of Monroe County (SSURGO) were used to train the soil-landscape model. Since map units can contain more than one component soil series, each sample point within a map unit can possibly belong to any one of them. Hence there is ambiguity in labeling of the training instances with appropriate soil series. A kNN-based heuristic approach was used to disambiguate the training set labels. The training sets were further preprocessed for removal of outliers and for selection of fewer attributes. Modeling was performed using two learning algorithms namely J48 classification tree and Random Forest (RF). The map models were then evaluated for the quality of prediction using two prediction rate measures and two landscape fragmentation statistics. Generally Random Forest recorded a higher prediction rate and greater contiguity when compared to J48. However, Random Forest over predicted soils such as Gilpin, Guernsey, Zanesville and Captina Series which occupy large areas, at the cost of prediction accuracy of soils which occurred in smaller proportions. The results showed that the highest prediction rate based on the dominant soil series (> 0.5) and higher values of contiguity index (0.83) and aggregation index (84.2) for RF was observed in the model built using the training set preprocessed for disambiguation. This suggests an improvement in the quality of predicted maps as a result of disambiguation. The model predictions were helpful in locating many individual component series in soil consociations and associations. The maps were useful in identifying areas of uncertainty such as misplacement of polygon boundaries, incorrect labeling and disparity along the county edges, which could serve as a guide for further field investigations. The predicted models also provided valuable information for rationalizing the mapping intensity for adjacent SSURGO maps.
Brian Slater (Advisor)
135 p.
Agriculture, Soil Science
machine learning; data mining; soil survey; SSURGO updates; soil-landscape modeling; predictive soil modeling; Random Forest

Recommended Citations

Citations

  • Subburayalu, S. K. (2008). Application of machine learning for soil survey updates: A case study in southeastern Ohio [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1199992659

    APA Style (7th edition)

  • Subburayalu, Sakthi Kumaran. Application of machine learning for soil survey updates: A case study in southeastern Ohio. 2008. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1199992659.

    MLA Style (8th edition)

  • Subburayalu, Sakthi Kumaran. "Application of machine learning for soil survey updates: A case study in southeastern Ohio." Doctoral dissertation, Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1199992659

    Chicago Manual of Style (17th edition)

osu1199992659
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© 2008, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.