Runoff Simulation Under Future Climate Change Conditions: Performance Comparison of Data-Mining Algorithms and Conceptual Models

Water resources in arid and semi-arid regions are susceptible to alteration in hydro-climatic variables, especially under climate change which makes runoff simulations more challenging. This study aims to simulate input runoff to a dam reservoir in an arid region under changing climatic conditions using three data-mining algorithms, including Artificial Neural Networks (ANNs), Support Vector Machine (SVM), Genetic Expression Programming (GEP), and the conceptual HYMOD model. Three parameters containing precipitation and maximum and minimum temperature were simulated from 30 Coupled Model Intercomparison Project Phase 5 (CMIP5) and Global Climate Models (GCMs) for the future period (2020–2040) under the high-end RCP8.5 scenario. The Long Ashton Research Station Weather Generator (LARS-WG) was selected as a downscaling method. The Gamma and M tests (This is an exam to determine whether an infinite series of functions will converge uniformly and absolutely or not) were applied to detect the best combinations and number of input parameters for the models, respectively. Among 29 defined input parameters for the models, the gamma test identified 11 parameters with the best functionality to simulate runoff. Based on the reliability estimates of model error variance by the M test, the data were partitioned as 75% for learning and the other 25% for test verification. A comparison of the runoff simulations of the models revealed a remarkable performance of the SVM model by 3, 5, and 14% compared to ANNs, GEP, and HYMOD models, respectively. The SVM model forecasted a 25% decrease in the mean runoff input to the dam reservoir for the 2020–2040 period compared to the study period (2000–2019). This result illustrates necessitating the implementation of sustainable adaptation strategies to protect future water resources in the basin.