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Multiple genetic programming: a new approach to improve genetic-based month ahead rainfall forecasts

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Abstract

It 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.

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Authors and Affiliations

  1. Department of Civil Engineering, Antalya Bilim University, Antalya, Turkey

    Ali Danandeh Mehr

  2. Department of Civil Engineering, Yaşar University, Izmir, Turkey

    Mir Jafar Sadegh Safari

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  1. Ali Danandeh Mehr
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  2. Mir Jafar Sadegh Safari
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Correspondence to Ali Danandeh Mehr.

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Danandeh Mehr, A., Safari, M.J.S. Multiple genetic programming: a new approach to improve genetic-based month ahead rainfall forecasts. Environ Monit Assess 192, 25 (2020). https://doi.org/10.1007/s10661-019-7991-1

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