Table 1 Summary of hybrid artificial intelligence methods by parameter optimization for flow forecasting.
From: Short-term streamflow modeling using data-intelligence evolutionary machine learning models
Reference | Case study | Hybrid model |
|---|---|---|
Iraq | Differential evolution integrated into multivariate adaptive regression spline (MARS-DE) | |
Brazil | Echo state network and multi-objective optimization design (ESN-MOB) | |
Malaysia | Firefly optimization algorithm and adaptive neuro-fuzzy inference systems (ANFIS-FFA) | |
Pakistan | Particle swarm optimization algorithm and neuro-fuzzy inference systems (ANFIS-PSO) | |
Egypt | support vector regression with grey wolf optimization (GWO-SVR) | |
Algeria | Wavelet support vector regression with grey wolf optimization (GWO-WSVR) | |
India | Harris Hawks optimization and support vector regression (SVR-HHO) | |
China | Extreme learning machine with flower pollination algorithm (ELM-FPA) | |
Pakistan | Particle swarm optimization and gray wolf optimization with extreme learning machine (ELM-PSOGWO) | |
Iran | Support vector regression optimized by grasshopper optimization algorithm (GOA) with LASSO input selection | |
Iraq | Extreme learning machine model with salp swarm algorithm (SSA-ELM) | |
Turkey | Gated recurrent unit with grey wolf algorithm (GWO-GRU) | |
Malaysia | Particle swarm optimization and support vector machine (PSO-SVM) | |
Tukey | Hybrid particle swarm optimization and gravitational search algorithms with feed-forward neural network (FFN-PSOGSA) | |
Iran | Adaptive neuro-fuzzy inference systems with grey wolf optimization algorithm (GWO-ANFIS) | |
Pakistan | ANFIS with gradient-based optimization (GBO) (GBO-ANFIS) | |
Iran | ANFIS with GWO |
