Developing an Artificial Intelligence-Based Monthly Streamflow Forecasting Model
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-3485-0_28
Reference18 articles.
1. Duc HN, Rivett K, MacSween K et al (2017) Association of climate drivers with rainfall in New South Wales, Australia, using Bayesian model averaging 127:169–185
2. Humphrey GB, Gibbs MS, Dandy GC et al (2016) A hybrid approach to monthly streamflow forecasting: integrating hydrological model outputs into a Bayesian artificial neural network 540:623–640
3. Kirono DG, Chiew FH, Kent DMJHPAIJ (2010) Identification of best predictors for forecasting seasonal rainfall and runoff in Australia 24:1237–1247
4. Sahoo A, Samantaray S, Ghose DKJPCS (2019) Stream flow forecasting in Mahanadi river basin using artificial neural networks 157:168–174
5. Niu W-J, Feng Z-K, Yang W-F et al (2020) Short-term streamflow time series prediction model by machine learning tool based on data preprocessing technique and swarm intelligence algorithm 65:2590–2603
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