A Novel Soil Moisture Predicting Method Based on Artificial Neural Network and Xinanjiang Model
| Periodical | Advanced Materials Research (Volumes 121 - 122) |
|---|---|
| Main Theme | Nanotechnology and Computer Engineering |
| Edited by | Donald C. Wunsch II, Honghua Tan, Dehuai Zeng, Qi Luo |
| Pages | 1028-1032 |
| DOI | 10.4028/www.scientific.net/AMR.121-122.1028 |
| Citation | Jing Wen Xu et al., 2010, Advanced Materials Research, 121-122, 1028 |
| Online since | June, 2010 |
| Authors | Jing Wen Xu, Jun Fang Zhao, Wan Chang Zhang, Xiao Xun Xu |
| Keywords | Artificial Neural Network (ANN), Linyi Watershed, Soil Moisture Prediction, Xinanjiang Model |
| Price | US$ 28,- |
Soil moisture plays an important role in agricultural drought predicting, therefore there is an increasing demand for detailed predictions of soil moisture, especially at basin scales. However, so far soil moisture predictions are usually obtained as a by-product of climate and weather prediction models coupled with a land surface parameterization scheme, and there has been little dedicated work to meet this urgent need at basin scales. In order to improve the basin hydrological models’ performance in the soil moisture forecasting, an integrated soil moisture predicting model based on Artificial Neural Network (ANN) and Xinanjiang model is proposed and presented in this paper. The performance of the new integrated soil moisture predicting model was tested in the Linyi watershed with a drainage area of 10040 km2, located in the semi-arid area of the eastern China. The results suggest that the soil moisture simulated by the integrated ANN-Xinanjiang model is more agree with the observed ones than that simulated by Xinanjiang, and that the simulated soil moisture by both the models has the similar trend and temporal change pattern with the observed one.
