Earth Science Informatics ( IF 2.8 ) Pub Date : 2021-03-09 , DOI: 10.1007/s12145-021-00599-1 Mohammad Zounemat-Kermani , Amin Mahdavi-Meymand , Reinhard Hinkelmann
This study appraises different types of conventional (e.g., GRNN, RBNN, & MLPNN) and modern neural networks (e.g., integrative, inclusive, hybrid, & recurrent) in forecasting daily flow in the Thames River located in the United Kingdom. The models are mathematically, statistically, and diagnostically compared based on the forecasted results for ten different time-series assortments. The results indicate that all the neural network models acceptably forecasted the daily flow rate, with mean values of R2 > 0.92 and RMSE < 18.6 m3/s. Despite the fact that the integrative neural network models slightly acted better in forecasting flow rate (mean values of R2 > 0.94 and RMSE < 15.3 m3/s), they were not as computationally effective as the other applied models.
中文翻译:
常规和现代神经网络的全面调查:在河流流量预测中的应用
这项研究评估了不同类型的常规(例如GRNN,RBNN和MLPNN)和现代神经网络(例如集成,包容,混合和递归),以预测英国泰晤士河的日流量。基于十种不同时间序列分类的预测结果,对模型进行数学,统计和诊断比较。结果表明,所有神经网络模型都可以合理地预测日流量,其平均值R 2 > 0.92,RMSE <18.6 m 3 / s。尽管集成神经网络模型在预测流量方面表现稍佳(R 2 > 0.94,RMSE <15.3 m)3 / s),它们的计算效率不如其他应用模型。