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The Use of Artificial Neural Networks in a Geographical Information System for Agricultural Land-Suitability Assessment

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  • F Wang

    (Department of Computing and Information Science, University of Guelph, Guelph, Ontario N1G 2W1, Canada)

Abstract

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.

Suggested Citation

  • F Wang, 1994. "The Use of Artificial Neural Networks in a Geographical Information System for Agricultural Land-Suitability Assessment," Environment and Planning A, , vol. 26(2), pages 265-284, February.
    • Handle: RePEc:sae:envira:v:26:y:1994:i:2:p:265-284
    DOI: 10.1068/a260265
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    Cited by:

    1. Cheng Han & Shengbo Chen & Yan Yu & Zhengyuan Xu & Bingxue Zhu & Xitong Xu & Zibo Wang, 2021. "Evaluation of Agricultural Land Suitability Based on RS, AHP, and MEA: A Case Study in Jilin Province, China," Agriculture, MDPI, vol. 11(4), pages 1-23, April.
    2. Vinay Kumar Gautam & Chaitanya B. Pande & Kanak N. Moharir & Abhay M. Varade & Nitin Liladhar Rane & Johnbosco C. Egbueri & Fahad Alshehri, 2023. "Prediction of Sodium Hazard of Irrigation Purpose using Artificial Neural Network Modelling," Sustainability, MDPI, vol. 15(9), pages 1-17, May.
    3. Yi Lu & Shawn Laffan & Chris Pettit & Min Cao, 2020. "Land use change simulation and analysis using a vector cellular automata (CA) model: A case study of Ipswich City, Queensland, Australia," Environment and Planning B, , vol. 47(9), pages 1605-1621, November.
    4. Lin, Huiyan & Lu, Kang Shou & Espey, Molly & Allen, Jeffery, 2005. "Modeling Urban Sprawl and Land Use Change in a Coastal Area-- A Neural Network Approach," 2005 Annual meeting, July 24-27, Providence, RI 19364, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    5. Heng Liu & Lu Zhou & Diwei Tang, 2022. "Urban Expansion Simulation Coupled with Residential Location Selection and Land Acquisition Bargaining: A Case Study of Wuhan Urban Development Zone, Central China’s Hubei Province," Sustainability, MDPI, vol. 15(1), pages 1-20, December.
    6. Inés Santé Riveira & Rafael Crecente Maseda, 2006. "A Review of Rural Land-Use Planning Models," Environment and Planning B, , vol. 33(2), pages 165-183, April.
    7. Xiaoli Hu & Xin Li & Ling Lu, 2018. "Modeling the Land Use Change in an Arid Oasis Constrained by Water Resources and Environmental Policy Change Using Cellular Automata Models," Sustainability, MDPI, vol. 10(8), pages 1-14, August.
    8. Akpoti, Komlavi & Kabo-bah, Amos T. & Zwart, Sander J., 2019. "Agricultural land suitability analysis: State-of-the-art and outlooks for integration of climate change analysis," Agricultural Systems, Elsevier, vol. 173(C), pages 172-208.
    9. Xiaoteng Cao & Chaofu Wei & Deti Xie, 2021. "Evaluation of Scale Management Suitability Based on the Entropy-TOPSIS Method," Land, MDPI, vol. 10(4), pages 1-17, April.
    10. Xia Li & Anthony Gar-On Yeh, 2001. "Calibration of Cellular Automata by Using Neural Networks for the Simulation of Complex Urban Systems," Environment and Planning A, , vol. 33(8), pages 1445-1462, August.

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