Open Access
Knowledge discovery from soil maps using inductive learning (vol 17, pg 771, 2003)
81
TL;DR: In this paper, a knowledge discovery procedure for extracting knowledge of soil-landscape models from a soil map is developed, which consists of four major steps: data preparation, data preprocessing, pattern extraction, and knowledge consolidation.
read more
Abstract: This paper develops a knowledge discovery procedure for extracting knowledge of soil-landscape models from a soil map. It has broad relevance to knowledge discovery from other natural resource maps. The procedure consists of four major steps: data preparation, data preprocessing, pattern extraction, and knowledge consolidation. In order to recover true expert knowledge from the error-prone soil maps, our study pays specific attention to the reduction of representation noise in soil maps. The data preprocessing step has exhibited an important role in obtaining greater accuracy. A specific method for sampling pixels based on modes of environmental histograms has proven to be effective in terms of reducing noise and constructing representative sample sets. Three inductive learning algorithms, the See5 decision tree algorithm, Naïve Bayes, and artificial neural network, are investigated for a comparison concerning learning accuracy and result comprehensibility. See5 proves to be an accurate method and produces the most comprehensible results, which are consistent with the rules (expert knowledge) used in producing the soil map. The incorporation of spatial information into the knowledge discovery process is found not only to improve the accuracy of the extracted knowledge, but also to add to the explicitness and extensiveness of the extracted soil-landscape model.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Extrapolating regional soil landscapes from an existing soil map: Sampling intensity, validation procedures, and integration of spatial context
TL;DR: In this paper, the authors investigated the potential of using soil-landscape pattern extracted from a soil map to predict soil distribution at unvisited location using stochastic gradient boosting.
209
Early season monitoring of corn and soybeans with TerraSAR-X and RADARSAT-2
TL;DR: It is demonstrated that TerraSAR-X can deliver acreage estimates of these two crops early enough to assist with in-season production forecasting, and application of this filtering approach could accelerate delivery of a crop inventory for this region of Canada.
165
Disaggregating and harmonising soil map units through resampled classification trees
TL;DR: In this article, the authors developed an approach called "Disaggregation and Harmonization of Soil Map Units Through Resampled Classification Trees" (DSMART), which samples the polygons of a legacy soil map and uses classification trees to generate a number of realisations of the potential soil class distribution.
154
Application of remote-sensing data and decision-tree analysis to mapping salt-affected soils over large areas.
A. A. Elnaggar,Jay S. Noller +1 more
TL;DR: DTA is a promising approach for mapping soil salinity in more productive and accurate ways compared to only using remote-sensing analysis.
149
References
•Book
Fuzzy sets
Lotfi A. Zadeh
- 01 Aug 1996
TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
53.2K
•Book
C4.5: Programs for Machine Learning
J. Ross Quinlan
- 15 Oct 1992
TL;DR: A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.
27.2K
Induction of Decision Trees
TL;DR: In this paper, an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail, is described, and a reported shortcoming of the basic algorithm is discussed.
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
A framework for representing knowledge
Marvin Minsky
- 01 Jun 1974
TL;DR: The enormous problem of the volume of background common sense knowledge required to understand even very simple natural language texts is discussed and it is suggested that networks of frames are a reasonable approach to represent such knowledge.