The Use of Artificial Neural Networks in a Geographical Information System for Agricultural Land-Suitability Assessment
Bibliographic Data
| ID | 4972833 |
|---|---|
| Authors | F Wang (0000-0003-2479-1276, University of Guelph, corresponding author) |
| Year | 1994 |
| Volume | 26 |
| Issue | 2 |
| Pages | 265-284 |
| Publication date | 1994-02-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environment and Planning A Economy and Space (JOURNAL) |
| Journal identifiers | ISSN: 0308-518X • E-ISSN: 1472-3409 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1068/a260265 |
| OpenAlex | W2023201792 |
| Language | EN |
| Citations received | 8 |
| References cited | 5 |
Agricultural land-suitability assessment involves the analysis of a large variety and amount of physiographic data. Geographical information systems (GISs) may facilitate suitability assessment in data collection. To generate accurate results from the data, appropriate suitability-assessment methods are required. However, the assessment methods which can currently be used with GISs, such as that developed by the United Nations Food and Agriculture Organization and the statistical pattern-classification method, have limitations which may lead to inaccurate assessment. An artificial neural network is an effective tool for pattern analysis. A neural network allows decision rules of greater complexity to be applied in pattern classification. By formulating the land-suitability-assessment problem into a pattern-classification problem, neural networks can be used to achieve results of greater accuracy. In this paper, a neural-network-based method for land-suitability assessment is discussed, and a set of neural networks is described. The integration between the neural networks and a GIS is addressed, and some experimental results are presented and analyzed
Artificial neural network · Data mining · Data set · Land use · Machine learning · Computer Science · Data Management and Algorithms · Engineering · Smart Agriculture and AI · Soil and Land Suitability Analysis · Artificial Intelligence
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| Citations per year | 0.28 |
|---|---|
| Citation span | 1997 - 2020 (24) |
| Highly cited | No |