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A new approach for processing climate missing databases applied to daily rainfall data in Soummam watershed, Algeria
Amir Aieb1,2, Khodir Madani1, Marco Scarpa3
1Laboratoire de Biomathématiques, Biophysique, Biochimie, et Scientométrie (L3BS), Université de Bejaia, 06000 Bejaia, Algérie.
Heliyon
|March 20, 2019
Summary
This study introduces an advanced imputation algorithm to address missing climatological data, crucial for hydrological studies and water management. The new method enhances data reliability, improving the accuracy of water resource assessments.
Area of Science:
- Climatology
- Hydrology
- Data Science
Background:
- Missing data is a frequent challenge in climatology, impacting hydrological studies and water resource management.
- Existing imputation methods have limitations in reliability, especially with high percentages of missing data or complex geographical factors.
Purpose of the Study:
- To propose and evaluate a novel imputation algorithm for addressing missing climatological data.
- To optimize regression-based imputation methods for improved accuracy in hydrological applications.
- To assess the influence of geographical factors on data correlation and imputation performance.
Main Methods:
- Development of a new imputation algorithm optimizing regression methods (hot deck, k-NN, weighted k-NN, multiple imputation, linear regression, simple average).
- Application of statistical tests for method selection and validation.
- Utilizing Principal Component Analysis (PCA) to analyze the impact of geographical variables (altitude, latitude, longitude) on station correlation.
- Evaluating the algorithm's performance using Root Mean Square Error (RMSE) across various missing data percentages (4%, 8%, 12%, 16%).
Main Results:
- The proposed algorithm demonstrated validity and performance across different missing data scenarios.
- Principal Component Analysis revealed a positive impact of altitude, latitude, and longitude on the correlation index between stations.
- The study identified challenges in station correlation within the Soummam watershed due to data loss and geological factors.
Conclusions:
- The developed imputation algorithm offers a reliable solution for handling missing climatological data.
- Geographical factors significantly influence spatial data correlation, which is important for selecting neighboring stations in imputation.
- The approach is effective for improving data quality in hydrological studies and water resource management.
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