Metaheuristic evolutionary deep learning model based on temporal convolutional network, improved aquila optimizer and random forest for rainfall-runoff simulation and multi-step runoff prediction

Author:

Qiao Xiujie,Peng TianORCID,Sun Na,Zhang Chu,Liu Qianlong,Zhang Yue,Wang Yuhan,Shahzad Nazir Muhammad

Funder

Natural Science Foundation of Jiangsu Province

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference39 articles.

1. Evaluation of artificial intelligence models for flood and drought forecasting in arid and tropical regions;Adikari;Environmental Modelling & Software,2021

2. Deep Residual Network in Network;Alaeddine;Computational Intelligence and Neuroscience,2021

3. Optimized ANFIS Model Using Aquila Optimizer for Oil Production Forecasting;AlRassas;Processes,2021

4. Assessing the value of seasonal hydrological forecasts for improving water resource management: Insights from a pilot application in the UK;Andres;Hydrology and Earth System Sciences,2020

5. Bai, S., Kolter, J.Z., Koltun, V., 2018. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271.

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