Simulation of spring flows from a karst aquifer with an artificial neural network

Caihong Hu, Yonghong Hao, Tian Chyi J. Yeh, Bo Pang, Zening Wu

Research output: Contribution to journalArticlepeer-review

68 Scopus citations


In China, 9.5% of the landmass is karst terrain and of that 47,000 km2 is located in semiarid regions. In these regions the karst aquifers feed many large karst springs within basins of thousands of square kilometres. Spring discharges reflect the fluctuation of ground water level and variability of ground water storage in the basins. However, karst aquifers are highly heterogeneous and monitoring data are sparse in these regions. Therefore, for sustainable utilization and conservation of karst ground water it is necessary to simulate the spring flows to acquire better understanding of karst hydrological processes. The purpose of this study is to develop a parsimonious model that accurately simulates spring discharges using an artificial neural network (ANN) model. The karst spring aquifer was treated as a non-linear input/output system to simulate the response of karst spring flow to precipitation and applied the model to the Niangziguan Springs, located in the east of Shanxi Province, China and a representative of karst springs in a semiarid area. Moreover, the ANN model was compared with a previous time-lag linear model and it was found that the ANN model performed better.

Original languageEnglish (US)
Pages (from-to)596-604
Number of pages9
JournalHydrological Processes
Issue number5
StatePublished - Feb 29 2008


  • Artificial neural network
  • China
  • Ground water flow
  • Karst hydrology
  • Niangziguan Springs Basin

ASJC Scopus subject areas

  • Water Science and Technology


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