Monthly streamflow forecasting based on meteorological data from a nearby station

Monthly streamflow forecasting is critical for improving water resource management. In this study, several base-classifier data-mining algorithms – conjunctive rule (CR), isotonic regression (ISOR), sequential minimal optimization regression (SMOR) – as well as several hybrid data-mining techniques – disjoint aggregating or dagging (DA)-CR, DA-ISOR, and DA-SMOR – that combine dagging with these algorithms were developed and applied to forecasting streamflow 3 and 12 months into the future (i.e., Qt+3 and Qt+12) based on meteorological data from a nearby station. Thirty years of data (from 1988 to 2018) that included precipitation, minimum relative humidity, maximum relative humidity, evaporation, hours of sunshine, maximum temperature, minimum temperature, wind speed, and streamflow were collected at the Kermanshah synoptic station were input to develop and evaluate several models. Varying combinations of the input data were tested to find the optimal set to employ. The models were validated and compared using several quantitative statistical indices. Though all satisfactorily predicted monthly streamflow, the hybrid models (DA-CR, DA-ISOR, and DA-SMOR) outperformed the base-classifier models (CR, ISOR, and SMOR), proving that dagging improved data-mining models significantly. Of the hybrid models, D-SMOR was the best. The models developed in this study are cost-effective tools for quick and accurate monthly streamflow forecasting. HIGHLIGHTS Streamflow is forecasting based on meteorological data from a nearby station.; Forecasting streamflow is for 3 and 12 months ahead (i.e., Qt+3 and Qt+12).; Conjunctive rule (CR), isotonic regression (ISOR), sequential minimal optimization regression (SMOR) – disjoint aggregating or dagging (DA)-CR, DA-ISOR, and DA-SMOR models are developed.; The DA-SMOR model has the highest prediction power.; Minimum and maximum temperature are the two most effective variables in Q forecasting.;