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Genetic learning of the irrigation cycle for water flow in cropped soils

  • Conference paper
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Simulated Evolution and Learning (SEAL 1996)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1285))

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Abstract

A genetic algorithm is defined to learn the irrigation cycle for the simulated water flow in cropped soils. It is shown that the genetic learning provides an appropriate method to defining irrigation on and irrigation off switching to maintain a desired moisture content at a predetermined depth in the soil.

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References

  1. Janz, T., Stonier, R.J.: Modelling Water Flow in Cropped Soils: Simulating the Irrigation Cycle. Proceedings of the International Congress on Modelling and Simulation Perth WA Australia 3 (1993) 931–936.

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  2. Janz, T., Stonier, R.J.: Modelling Water Flow in Cropped Soils: Water Uptake by Plant Roots. Environment International 21 1 (1995) 705–709.

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  3. Molz, F.J., Remson, I.: Extraction Term Models of soil Moisture Use by Transpiring Plants. Water Resources Research 6 5 (1970) 1346–1356.

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  4. Clapp, R.B., Hornberger, G.M.: Empirical Equations for Some Soil Hydraulic Properties. Water Resources Research 14 4 (1978) 601–604.

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  5. Goldberg, D.: Genetic Algorithms in Search, Optimisation and Machine Learning. Addison-Wesley (1989).

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Xin Yao Jong-Hwan Kim Takeshi Furuhashi

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© 1997 Springer-Verlag Berlin Heidelberg

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Stonier, R., Sturgess, D. (1997). Genetic learning of the irrigation cycle for water flow in cropped soils. In: Yao, X., Kim, JH., Furuhashi, T. (eds) Simulated Evolution and Learning. SEAL 1996. Lecture Notes in Computer Science, vol 1285. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0028525

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  • DOI: https://doi.org/10.1007/BFb0028525

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63399-0

  • Online ISBN: 978-3-540-69538-7

  • eBook Packages: Springer Book Archive

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