Two hybrid Artificial Intelligence approaches for modeling rainfall–runoff process

By Vahid Nourani, Ozgur Kisi, M. Komasi. 2011

345 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/8cbbd1649aafb5b31ccb772ac80b2c4335315f4d

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In Environmental Engineering

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Citations in the corpus

Forecasting of meteorological drought using Wavelet-ANFIS hybrid model for different time steps (case study: southeastern part of east Azerbaijan province, Iran) (2013, result): “This result is in accordance with Nourani et al. (2011) who evaluated some artificial intelligence methods to model rainfall-runoff process.”
Design and evaluation of SVR, MARS and M5Tree models for 1, 2 and 3-day lead time forecasting of river flow data in a semiarid mountainous catchment (2018, background): “Over the past few decades, EC and W-AI models have been greatly dedicated to the river flow forecasting problems (Dumedah 2015; Friedel 2011; Kisi 2010; Liu et al. 2014; Maier et al. 2014; Mehr et al. 2013; Nourani et al. 2011; Tran et al. 2015; Whigham and Crapper 2001; Zimmer et al. 2015).”
Rainfall-runoff modeling in hilly watershed using heuristic approaches with gamma test (2018, methodology): “multi-layer perceptron neural network (MLPNN), adaptive neuro-fuzzy inference system (ANFIS), co-active neuro-fuzzy inference system (CANFIS), generalized regression neural network (GRNN), radial basis neural network (RBNN), selforganizing mapping neural network (SOMNN), genetic algorithm (GA), wave”
Forecasting monthly inflow with extreme seasonal variation using the hybrid SARIMA-ANN model (2017, methodology): “Nourani et al. (2011) used a type of this hybrid model to predict and analyze daily rainfall-runoff.”
Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review (2014, methodology): “In a further study, Nourani et al. (2011) investigated the rainfall–runoff process using two hybrid wavelet–AI models (i.e., WANN and WANFIS) and found that considering seasonality effects extracted through wavelet decomposition, the hybrid WANFIS model outperformed individual AI-based models.”