Ali Danandeh MehrMir Jafar Sadegh SafariDanandeh Mehr, AliMehr, Ali DanandehSafari, Mir Jafar Sadegh2025-10-06202015732959, 016763690167-63691573-295910.1007/s10661-019-7991-12-s2.0-85076389966https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076389966&doi=10.1007%2Fs10661-019-7991-1&partnerID=40&md5=b3ccbb80ba5bd7c19483407bd26448a4https://gcris.yasar.edu.tr/handle/123456789/9310https://doi.org/10.1007/s10661-019-7991-1It is well documented that standalone machine learning methods are not suitable for rainfall forecasting in long lead-time horizons. The task is more difficult in arid and semiarid regions. Addressing these issues the present paper introduces a hybrid machine learning model namely multiple genetic programming (MGP) that improves the predictive accuracy of the standalone genetic programming (GP) technique when used for 1-month ahead rainfall forecasting. The new model uses a multi-step evolutionary search algorithm in which high-performance rain-borne genes from a multigene GP solution are recombined through a classic GP engine. The model is demonstrated using rainfall measurements from two meteorology stations in Lake Urmia Basin Iran. The efficiency of the MGP was cross-validated against the benchmark models namely standard GP and autoregressive state-space. The results indicated that the MGP statistically outperforms the benchmarks at both rain gauge stations. It may reduce the absolute and relative errors by approximately up to 15% and 40% respectively. This significant improvement over standalone GP together with the explicit structure of the MGP model endorse its application for 1-month ahead rainfall forecasting in practice. © 2020 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessGenetic Programming, Hybrid Models, Rainfall, Stochastic Modelling, Arid Regions, Genetic Algorithms, Machine Learning, Rain, Rain Gages, Stochastic Models, Stochastic Systems, Weather Forecasting, Arid And Semi-arid Regions, Evolutionary Search Algorithms, Hybrid Machine Learning, Hybrid Model, Machine Learning Methods, Predictive Accuracy, Rainfall Forecasting, Rainfall Measurements, Genetic Programming, Rain, Forecasting Method, Genetic Algorithm, Machine Learning, Modeling, Numerical Method, Precipitation Assessment, Rainfall, Stochasticity, Absolute Error, Analytical Error, Article, Artificial Neural Network, Autoregressive State Space, Controlled Study, Evolutionary Algorithm, Forecasting, Mathematical Model, Measurement Accuracy, Multigene Family, Multiple Genetic Programming, Predictive Value, Relative Error, Statistical Analysis, Stochastic Model, Time Series Analysis, Algorithm, Biological Model, Iran, Meteorology, Procedures, Lake Urmia, Algorithms, Forecasting, Meteorology, Models GeneticArid regions, Genetic algorithms, Machine learning, Rain, Rain gages, Stochastic models, Stochastic systems, Weather forecasting, Arid and semi-arid regions, Evolutionary search algorithms, Hybrid machine learning, Hybrid model, Machine learning methods, Predictive accuracy, Rainfall forecasting, Rainfall measurements, Genetic programming, rain, forecasting method, genetic algorithm, machine learning, modeling, numerical method, precipitation assessment, rainfall, stochasticity, absolute error, analytical error, Article, artificial neural network, autoregressive state space, controlled study, evolutionary algorithm, forecasting, mathematical model, measurement accuracy, multigene family, multiple genetic programming, predictive value, relative error, statistical analysis, stochastic model, time series analysis, algorithm, biological model, Iran, meteorology, procedures, Lake Urmia, Algorithms, Forecasting, Meteorology, Models GeneticGenetic ProgrammingHybrid ModelsRainfallStochastic ModellingArticle