...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Indian journal of agricultural research >Comparative Study between Wavelet Artificial Neural Network (WANN) and Artificial Neural Network (ANN) Models for Groundwater Level Forecasting
【24h】

Comparative Study between Wavelet Artificial Neural Network (WANN) and Artificial Neural Network (ANN) Models for Groundwater Level Forecasting

机译：小波人工神经网络（WANN）和人工神经网络（ANN）地下水位预测模型的比较研究

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Groundwater level fluctuation modeling is a prime need for effective utilization and planning the conjunctive use in any basin,The application of Artificial Neural Network (ANN) and hybrid Wavelet ANN (WANN) models was investigated in predicting Groundwater level fluctuations. The RMSE of ANN model during calibration and validation were found to be 0.2868 and 0,3648 respectively,! whereas for1 the WANN model the respective values were 0.1946 and 0,1695. Efficiencies during calibration and validationfor ANN model were 0.8862 per cent and 0.8465 per cent respectively, whereas for WANN model were found to be much higher with the respective values of 0 9436 per cent and 0.9568 per cent indicating substantial improvement in the model performance. Hencehybrid , ANN model is the promising tool to predict water table fluctuation as compared to ANN model.
机译：地下水位波动建模是有效利用率的主要需求，并规划任何盆中的联合用途，研究了人工神经网络（ANN）和混合小波ANN（WANN）模型的应用，以预测地下水位波动。 发现校准和验证期间的ANN模型的RMSE分别为0.2868和0,3648，！ 然而，虽然如此，WANN模型各个值为0.1946和0,1695。 校准期间的效率和ANN模型分别为0.8862％，分别为0.8465％，而WANN模型则被发现，随着0 9436％的相应值，0.9568％，表明模型性能大幅提高。 HelyberdBrid，Ann模型是与ANN模型相比预测水位波动的有前途的工具。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号