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Advanced Machine Learning Model for Prediction of Drought Indices using Hybrid SVR-RSM

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Abstract

Drought, as a phenomenon that causes significant damage to agriculture and water resources, has increased across the globe due to climate change. Hence, scientists are attracted to developing drought prediction models for mitigation strategies. Different drought indices (DIs) have been proposed for drought monitoring during the past few decades, most of which are probabilistic, highly stochastic, and non-linear. The present study inspected the capability of various machine learning (ML) models, including artificial neural network (ANN) and support vector regression (SVR) as original predictive models and optimized by two selected algorithms, namely, particle swarm optimization (SVR-PSO) and response surface method (SVR-RSM) to predict the meteorological drought indices of standardized precipitation index (SPI), percentage of normal precipitation (PN), effective drought index (EDI), and modified China-Z index (MCZI) on a monthly time scale. A novel model named SVR-RMS is introduced by using two calibrating processes given from RSM with two inputs and the SVR by predicted data handled with RSM given from the first calibrating procedure. For evaluating the models, different meteorological input variables in the period 1981–2020 were considered from 11 synoptic stations in arid and semi-arid climates of Iran, which frequently experience droughts. The SPI showed the highest and lowest correlation with MCZI (0.71) and EDI (0.34), respectively. The results of testing dataset (2011–2020) indicated that the SVR-RSM produced superior abilities for both accuracy and tendency compared to other models, while the SVR-PSO model is better than the ANN and SVR. The worst results of drought prediction were obtained for EDI. However, all models provided the acceptable EDI prediction in the high-temperature station of Ahvaz in the south of the country. Application of SVR-RSM as a novel hybrid model can be suggested for predicting the DIs on a short time scale in arid and semi-arid areas.

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Authors and Affiliations

  1. Department of Water Engineering, Faculty of Water and Soil, University of Zabol, Zabol, Iran

    Jamshid Piri

  2. Department of Water Engineering, College of Aburaihan, University of Tehran, Tehran, Iran

    Mohammad Abdolahipour

  3. Department of Civil Engineering, Faculty of Engineering, University of Zabol, Zabol, Iran

    Behrooz Keshtegar

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  1. Jamshid Piri
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  2. Mohammad Abdolahipour
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  3. Behrooz Keshtegar
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Contributions

B. Keshtegar designed and developed the theoretical formulations. Data collection and analysis were performed by M. Abdolahipour. The computations and modeling were done by J. Piri. The first draft of the manuscript was written by M. Abdolahipour and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Mohammad Abdolahipour.

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Highlights

• Using different meteorological input variables, the capability of four machine learning models was evaluated for prediction of short-term drought indices.

• A novel hybrid model is proposed for prediction of drought indices.

• The SVR-RSM showed superior performance in prediction of monthly drought indices.

• For arid and semi-arid areas, the hybrid SVR models showed more accurate results.

•A higher correlation between meteorological drought indices was observed in drier conditions.

Appendix

Appendix

Table 5 Statistics of input monthly climatic parameters at study stations.
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Table 6 Statistics of observed monthly drought indices at study stations.
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Piri, J., Abdolahipour, M. & Keshtegar, B. Advanced Machine Learning Model for Prediction of Drought Indices using Hybrid SVR-RSM. Water Resour Manage 37, 683–712 (2023). https://doi.org/10.1007/s11269-022-03395-8

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