• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Hydrology >Forecasting surface water temperature in lakes: A comparison of approaches
【24h】

Forecasting surface water temperature in lakes: A comparison of approaches

机译：湖泊中表面水温预测：方法比较

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

Accurate water temperature forecasting in lake systems is important for environmental impact assessment and fisheries management, among others. In this study, two models are developed and applied for water temperature forecasting in lake systems: (1) the multi-layer perceptron neural network (MLPNN) model; and (2) the wavelet transform and MLPNN integrated model (WT_MLPNN). The models are applied to forecast daily lake surface water temperature (LSWT) of eight lowland Polish lakes. Long-term daily LSWT from eight lakes and daily air temperatures from seven meteorological stations are used for daily LSWT forecasting. The results from the two models are compared with those obtained from two other widely used models: the physically-statistically based hybrid air2water model and a non-linear regression model (S-curve). The modelling results show that the air2water model performs the best, followed by the WT_MLPNN, MLPNN, and the non-linear regression model. Overall, the air2water, WT_MLPNN, and MLPNN models reproduce well the seasonal and inter-annual variations of the LSWT dynamics in the eight lakes. The non-linear regression model, although providing the lowest accuracy, can still provide good preliminary estimates of the LSWT for the eight lakes. The outcomes of the present research can provide references for forecasting lake surface water temperature and sustainable management of lake ecosystems.
机译：湖泊系统的准确水温预测对于环境影响评估和渔业管理是重要的。在这项研究中，开发了两种型号并施加了湖泊系统的水温预测：（1）多层Perceptron神经网络（MLPNN）模型; （2）小波变换和MLPNN集成模型（WT_MLPNN）。该模型适用于八个低地波兰湖泊的日常湖面水温（LSWT）。来自八个湖泊的长期每日LSWT来自七个气象站的日常空气温度用于每日LSWT预测。将这两种模型的结果与来自另外两个广泛使用的模型获得的结果进行比较：物理统计基于的混合Air2水模型和非线性回归模型（S曲线）。建模结果表明，Air2Water模型执行最佳，其次是WT_MLPNN，MLPNN和非线性回归模型。总的来说，Air2water，WT_MLPNN和MLPNN模型再现八个湖泊LSWT动态的季节性和年度际变化。非线性回归模型虽然提供最低精度，但仍然可以为八个湖泊提供LSWT的良好初步估计。本研究的结果可以提供预测湖面水温和湖泊生态系统可持续管理的参考。

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