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A new alpha logarithmic-generated class to model precipitation data with theory and inference.
1Department of Mathematical Sciences, College of Science, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
Heliyon
|October 9, 2023
Summary
A new alpha logarithmic-generated (NAL-G) distribution class, including the NAL-Exponential (NAL-Exp) model, offers superior rainfall prediction. This innovative approach enhances hydrological modeling accuracy over traditional methods.
Area of Science:
- Hydrology and statistical modeling.
- Development of novel probability distributions for meteorological data analysis.
Background:
- Precipitation modeling is crucial for Earth's systems and life sustainability.
- Existing statistical distributions often fail to accurately capture complex rainfall patterns.
Purpose of the Study:
- Introduce the novel alpha logarithmic-generated (NAL-G) class of distributions.
- Evaluate the mathematical properties and performance of the NAL-Exp distribution for precipitation modeling.
Main Methods:
- Mathematical analysis of NAL-G class properties (moments, quantile function, entropy, order statistics).
- Application and simulation of six classical estimation methods.
- Comparative performance analysis using four real-world rainfall datasets.
Main Results:
- The NAL-Exp distribution demonstrates high adaptability and superior performance in modeling rainfall data.
- Outperforms commonly used distribution models in accuracy and reliability.
- Identified the most effective estimation method through extensive simulations.
Conclusions:
- The NAL-Exp distribution offers a significant advancement for hydrological modeling.
- The NAL-G class provides a versatile and flexible framework for enhanced rainfall prediction.
- This novel approach promises to improve the accuracy and reliability of future meteorological forecasts.
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