Modeling waterbird diversity in irrigation ponds of Taoyuan, Taiwan using an artificial neural network approach

Wei Ta Fang, Hone Jay Chu, Bai You Cheng

Research output: Contribution to journalArticle

14 Citations (Scopus)


The study develops an approach adopted by artificial neural networks (ANN) to model the relationship between pondscape and waterbird diversity. Study areas with thousands of irrigation ponds are unique geographic features from the original functions of irrigation converted to waterbird refuges. The model considers pond shape and size, neighboring farmlands, and constructed areas in calculating parameters pertaining to the interactive influences on avian diversity, among them the Shannon-Wiener diversity index. Results indicate that irrigation ponds adjacent to farmland benefited waterbird diversity. On the other hand, urban development leads to the reduction of pond numbers, which reduces waterbird diversity. By running the ANN model, the resulting index shows a good-fit prediction of bird diversity against pond size, shape, neighboring farmlands, and neighboring developed areas with a correlation coefficient (r) of 0. 72, in contrast to the results from a linear regression model (r < 0.28).

Original languageEnglish
Pages (from-to)209-216
Number of pages8
JournalPaddy and Water Environment
Issue number3
Publication statusPublished - 2009 Aug 1



  • Artificial neural network (ANN)
  • Irrigation pond
  • Landscape ecology
  • Taiwan
  • Waterbird

ASJC Scopus subject areas

  • Environmental Engineering
  • Agronomy and Crop Science
  • Water Science and Technology

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