Abstract
This work addresses the application of data mining to obtain artificial neural network based models for the application in water management during crops irrigation. This problem is very important in the zone of the South-East of Spain, as there is an important lack of rainfall there. These intelligent analysis techniques are used in order to optimize the consumption of such an appreciated and limited resour
Work supported by the European Comission through the FEDER 1FD97-0255-C03-01 project
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References
J.A. Botía, A.F. Gomez-Skarmeta, M. Valdés, and Gracia Sánchez. Soft Computing Applied to Irrigation in Farming Environments. In Conference Proceedings of the FUZZ-IEEE., volume 1, pages 505–512, San Antonio, Texas, May 2000.
Juan A. Botia, A.F.G. Skarmeta, Juan R. Velasco, and Mercedes Garijo. A proposal for Meta-learning through Multi-agent Systems. In Tom Wagner and Omer F. Rana, editors, Lecture Notes in Artificial Inteligence (to appear). Springer, 2000.
Carla E. Brodley and Padhraic Smyth. The Process of Applying Machine Learning Algorithms. In Workshop on Applying Machine Learning in Practice at IMLC-95. 1995.
Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth. Data Mining and Its Applications: A General Overview. In Jiawei Han Evangelos Simoudis and Usama Fayyad, editors, The Second International Conference on Knowledge Discovery & Data Mining. AAAI Press, August 1996.
HR. Ingleby and T.G. Crowe. Neural network models for predicting organic matter content in saskatchewan soils. Canadian BioSystems Engineering, 43(7), 2001.
J.S.R. Jang, C.T. Sun, and E. Mizutani, editors. Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice-Hall, 1997.
Tom M. Mitchell. Machine Learning. McGraw-Hill, 1997.
M.L. Stone and G.A. Kranzler. Artificial Neural Networks in Agricultural Machinery Applications. In ASAE Paper AETC 95052, Chicago, Illinois, 1995. ASAE.
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Botía, J.A., Gómez-Skarmeta, A.F., Valdés, M., Padilla, A. (2001). Data Mining Applied to Irrigation Water Management. In: Mira, J., Prieto, A. (eds) Bio-Inspired Applications of Connectionism. IWANN 2001. Lecture Notes in Computer Science, vol 2085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45723-2_66
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DOI: https://doi.org/10.1007/3-540-45723-2_66
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