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A methodology for treating missing data applied to daily rainfall data in the Candelaro River Basin (Italy)
Rossella Lo Presti1, Emanuele Barca, Giuseppe Passarella
1Water Research Institute of the National Research Council, Department of Bari, Viale F. De Blasio, 5 70123, Bari, Italy.
Environmental Monitoring and Assessment
|December 20, 2008
Summary
This study introduces a novel two-stage method for estimating missing environmental data by identifying similar monitoring stations and applying regression. This approach combines simplicity with the power of advanced techniques for reliable data gap filling.
Area of Science:
- Environmental Science
- Data Science
- Hydrology
Background:
- Environmental time series frequently contain missing data, necessitating robust estimation methods.
- Existing techniques range from simple mean imputation to complex multiple imputation.
Purpose of the Study:
- To propose a novel methodology for missing data estimation in environmental time series.
- To combine the ease of implementation of simple methods with the accuracy of advanced techniques.
Main Methods:
- A two-stage approach: 1. Identifying "most similar" neighboring monitoring stations using a similarity coefficient. 2. Applying regression methods to estimate missing values.
- Comparison of four different regression techniques for gap filling.
Main Results:
- The study evaluates the reliability of different regression methods for estimating missing rainfall data.
- The proposed methodology aims to provide a more accurate and practical solution for handling missing environmental data.
Conclusions:
- The developed two-stage method offers a promising approach for addressing missing data in environmental monitoring.
- Further research will determine the most reliable regression technique for specific applications.
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