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Imputation of missing daily rainfall data; A comparison between artificial intelligence and statistical techniques.

Angkool Wangwongchai1, Muhammad Waqas2,3, Porntip Dechpichai1

  • 1Department of Mathematics, Faculty of Science, King Mongkut's University of Technology Thonburi (KMUTT), Bangkok 10140, Thailand.

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Summary
This summary is machine-generated.

This study assessed statistical and artificial intelligence techniques for imputing missing daily rainfall data in Thailand. Multiple Linear Regression (MLR) is recommended for its accuracy and transparency in mountainous regions.

Keywords:
AITs for Imputation missing daily rainfall dataArtificial intelligenceDeep learningImputationMachine learningMissing dataNeural networksRainfall

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Area of Science:

  • Hydrology
  • Data Science
  • Environmental Modeling

Background:

  • Missing data is a significant challenge in hydrological modeling.
  • Accurate imputation of daily rainfall is crucial for reliable water resource management, especially in complex terrains.

Purpose of the Study:

  • To evaluate statistical techniques (STs) and artificial intelligence techniques (AITs) for imputing missing daily rainfall data.
  • To recommend an effective imputation methodology for the mountainous terrain of northern Thailand.

Main Methods:

  • Collected 30 years of daily rainfall data from 20 stations in northern Thailand.
  • Deleted 25-35% of data from four stations based on spatial correlation.
  • Developed and evaluated imputation models using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), R², and correlation coefficient (r).
  • Compared STs (Arithmetic Averaging, Multiple Linear Regression, Normal-Ratio, NIPALS, Linear Interpolation) with AITs (LSTM-RNN, M5 Model Tree, MLPNN, SVR).

Main Results:

  • The Multiple Linear Regression (MLR) model showed strong performance with an average MAE of 0.98, RMSE of 4.52, and R² of 79.6%.
  • The M5 Model Tree (M5-MT) also performed well, with an average MAE of 0.91, RMSE of 4.52, and R² of 79.8%.
  • MLR was identified as a recommended approach due to its balance of accuracy, transparency, and ease of use.

Conclusions:

  • Both MLR and M5-MT are effective for imputing missing daily rainfall data.
  • MLR is recommended for practical application in northern Thailand's mountainous terrain due to its transparency and minimal prerequisites.
  • The study provides a valuable framework for addressing missing data in hydrological studies.