Environment and Planning A Economy and Space · 1994 · 147 citations · 7 references
ComputingGeographyGeoinformaticsGeographic Information ScienceGeographic Information SystemsSpatial AnalysisLand Suitability AnalysisPattern RecognitionSpatial Pattern AnalysisDecision RulesFood And Agriculture OrganizationGeographic Information System (Gis) AnalysisGeographic Information System-based Land AssessmentGeographic Information System-based Suitability AnalysisLand Suitability MappingNeural Network Modeling
Agricultural land‑suitability assessment requires analyzing extensive physiographic data, and while GISs aid data collection, existing GIS‑compatible methods such as the FAO and statistical pattern‑classification approaches have limitations that can reduce accuracy; artificial neural networks offer a more powerful pattern‑analysis tool capable of applying complex decision rules. The paper proposes a neural‑network‑based method for land‑suitability assessment and describes a set of neural networks. The authors formulate land‑suitability assessment as a pattern‑classification problem, integrate neural networks with a GIS, and present experimental results demonstrating improved accuracy. Experimental results show that integrating neural networks with a GIS yields improved land‑suitability assessment accuracy.
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.
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A framework for land evaluation
Rogier van den Brink, Anthony Young · Socio-Environmental Systems Modeling · 1977 · 1.3K citations · Full text
Soil Survey and Land Evaluation.
B. S. Kear, David Dent, Anthony Young · Journal of Ecology · 1982 · 285 citations