Parameter calibration of the conceptual rainfall–runoff model based on improved quadratic interpolation optimization

Flood forecasting is regarded as the most important basic non-engineering measure, and its accuracy is the key to scientific flood control and regulation. The conceptual rainfall–runoff model (CRR) is widely applied to flood forecasting. The major difficulty associated with the use of CRR models in hydrology is their calibration since most of these models involve a large number of parameters. In order to calibrate the parameters of the CRR model, an improved quadratic interpolation optimization algorithm (IQIO) was proposed. The tent chaos mapping was used to initialize the population, adaptive optimizer probability based on individual adaptation value was used to balance algorithm’s global exploration and local exploitation ability. Thirteen mathematical benchmark functions were used to test the IQIO algorithm. The results showed that the IQIO algorithm exhibited strong exploration capability and fast convergence speed. The CRR model parameters optimized by the IQIO algorithm exhibited high performance, with Nash–Sutcliffe efficiency (NSE) values reaching 0.951 during the calibration period and 0.913 during the validation period. The relative error of runoff in each year was less than 20%, which satisfied the calculation accuracy requirements.