Simulation of Rainfall- Runoff using Novel Learning Machine in Shaharchay River Basin

In this paper, the precipitation and runoff time-series data of the Shaharchay River basin from 2000 to 2017 were simulated by using a novel hybrid artificial intelligence (AI) technique. In order to develop this AI model, the extreme learning machine (ELM), differential evolution (DE) and wavelet transform (WT) are combined and then the SAELM and WASAELM hybrid models are provided. Initially, the most effective lags of the time-series data are distinguished using the autocorrelation function. After that, using these lags, seven artificial intelligence models are defined for each of the SAELM and the WSAELM models. Additionally, 70% of the observational data are employed for training the artificial intelligence models and the rest (30%) for testing them. For WSAELM7 as the best model, the values of R2, the scatter index (SI), and the Nash-Sutcliff efficiency coefficient (NSC) for simulating precipitation are yielded 0.967, 0.208 and 0.965, respectively. Furthermore, a sensitivity analysis exhibits that the lags (t-1), (t-2) and (t-12) are regarded as the most effective input lags. Ultimately, an uncertainty analysis is carried out for the superior models.