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Imputation of missing monthly rainfall data using machine learning and spatial interpolation approaches in Thale Sap Songkhla River Basin, Thailand

  • Resilient and Sustainable Water Management in Agriculture
  • Published:
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Abstract

Missing rainfall data has been a prevalent issue and primarily interested in hydrology and meteorology. This research aimed to examine the capability of machine learning (ML) and spatial interpolation (SI) methods to estimate missing monthly rainfall data. Six ML algorithms (i.e. multiple linear regression (MLR), M5 model tree (M5), random forest (RF), support vector regression (SVR), multilayer perceptron (MLP), genetic programming (GP)) and four SI methods (i.e. arithmetic average (AA), inverse distance weighting (IDW), correlation coefficient weighted (CCW), normal ratio (NR)) were investigated and compared in their performance. The twelve rainfall stations, located in the Thale Sap Songkhla river basin and nearby basins, were considered as a study case. Tuning hyper-parameters for each ML method was conducted to get the most suitable model for the data sets considered. Three performance criteria matrices (i.e. NSE, OI, and r) were chosen, and the sum of those three performance criteria matrices was introduced for methods’ performance comparison. The experimental results pointed out that selecting neighbouring stations were essential when applying SI methods, but not for the ML method. The overall performance showed ML better imputed missing monthly rainfall than SI due to overcoming spatial constraints. GP provided the highest performance by giving NSE = 0.825, OI = 0.877, and r = 0.909 for the training stage. Those values for the testing stage were 0.796, 0.852, and 0.902, respectively. It was followed by SVR-rbf, SVR-poly, and RF. NR provided the best performance among four SI methods, followed by CCW, AA, and IDW. When applying SI methods, it should contemplate a correlation between the target and neighbouring stations greater than 0.80.

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Data availability

The datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request.

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Acknowledgements

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University, Abha, Kingdom of Saudi Arabia, for funding this work through Large Groups RGP.2/43/43. The authors would like to express gratitude to College of Graduate Studies, Walailak University, for providing Walailak University Master’s Degree Excellence Scholarships under Contract No. ME04/2021.

Funding

This work was supported by Deanship of Scientific Research at King Khalid University, Abha, Kingdom of Saudi Arabia, through Large Groups RGP.2/43/43 and Walailak University Master’s Degree Excellence Scholarships under Contract No. ME04/2021. The author (Mohd. Abul Hasan) has received research support from King Khalid University, Abha, Kingdom of Saudi Arabia, and Pakorn Ditthakit has received research support from College of Graduate Studies, Walailak University, Thailand.

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Authors and Affiliations

  1. Center of Excellence in Sustainable Disaster Management, School of Engineering and Technology, Walailak University, Nakhon Si Thammarat, 80161, Thailand

    Sirimon Pinthong, Pakorn Ditthakit & Nureehan Salaeh

  2. Civil Engineering Department, College of Engineering, King Khalid University, Abha, 61421, Saudi Arabia

    Mohd Abul Hasan & Saiful Islam

  3. Faculty of Natural Resources and Environment, Vietnam National University of Agriculture, Hanoi, 100000, Vietnam

    Cao Truong Son

  4. Institute of Applied Technology, Thu Dau Mot University, Thủ Dầu Một, Binh Duong province, Vietnam

    Nguyen Thi Thuy Linh

  5. Faculty of Science and Technology, Madhyanchal Professional University, Ratibad, Bhopal, 462044, India

    Krishna Kumar Yadav

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  1. Sirimon Pinthong
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Contributions

Pakorn Ditthakit contributed to the conceptualization, methodology, and supervision. Material preparation, data collection, and analysis were performed by Sirimon Pinthong and Nureehan Salaeh. The first draft of the manuscript was written by Sirimon Pinthong, Nureehan Salaeh, and Krishna Kumar Yadav. Nguyen Thi Thuy Linh and Saiful Islam reviewed and edited the previous version of the manuscript. Mohd Abul Hasan and Cao Truong Son proofread the text and helped in structuring the publication. All authors read and approved the final manuscript.

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Correspondence to Pakorn Ditthakit.

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Pinthong, S., Ditthakit, P., Salaeh, N. et al. Imputation of missing monthly rainfall data using machine learning and spatial interpolation approaches in Thale Sap Songkhla River Basin, Thailand. Environ Sci Pollut Res (2022). https://doi.org/10.1007/s11356-022-23022-8

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