A new alpha logarithmic-generated class to model precipitation data with theory and inference
Precipitation, or rainfall, is a central feature of the weather cycle and plays a vital role in sustaining life on Earth. However, existing models such as the Poisson, exponential, normal, log-normal, generalized Pareto, gamma, generalized extreme value, lognormal, beta, Gumbel, Weibull, and Pearson...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-09-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023067695 |
| _version_ | 1797669816994103296 |
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| author | Aned Al Mutairi |
| author_facet | Aned Al Mutairi |
| author_sort | Aned Al Mutairi |
| collection | DOAJ |
| description | Precipitation, or rainfall, is a central feature of the weather cycle and plays a vital role in sustaining life on Earth. However, existing models such as the Poisson, exponential, normal, log-normal, generalized Pareto, gamma, generalized extreme value, lognormal, beta, Gumbel, Weibull, and Pearson type III distributions used for predicting precipitation are often inadequate for precisely representing the complex pattern of rainfall. This study proposes a novel and innovative approach to address these limitations through the new alpha logarithmic-generated (NAL-G) class of distributions. The study authors thoroughly examine the NAL-G class and a unique model, the NAL-Exponential (NAL-Exp) distribution, with a focus on analyzing mathematical properties such as moments, quantile function, entropy, order statistics, and more. Six recognized classical estimation methods are employed, and extensive simulations are conducted to determine the best one. The NAL-Exp distribution demonstrates its high adaptability and value through its superior performance in modeling four distinct rainfall data sets. The results show that the NAL-Exp distribution outperforms other commonly used distribution models, highlighting its potential as a valuable tool in hydrological modeling and analysis. The increased versatility and flexibility of this new approach hold great potential for enhancing the accuracy and reliability of future rainfall predictions. |
| first_indexed | 2024-03-11T20:50:16Z |
| format | Article |
| id | doaj.art-1732f9c1c5a24f75b1399e79954213f1 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-11T20:50:16Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-1732f9c1c5a24f75b1399e79954213f12023-10-01T06:00:08ZengElsevierHeliyon2405-84402023-09-0199e19561A new alpha logarithmic-generated class to model precipitation data with theory and inferenceAned Al Mutairi0Department of Mathematical Sciences, College of Science, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaPrecipitation, or rainfall, is a central feature of the weather cycle and plays a vital role in sustaining life on Earth. However, existing models such as the Poisson, exponential, normal, log-normal, generalized Pareto, gamma, generalized extreme value, lognormal, beta, Gumbel, Weibull, and Pearson type III distributions used for predicting precipitation are often inadequate for precisely representing the complex pattern of rainfall. This study proposes a novel and innovative approach to address these limitations through the new alpha logarithmic-generated (NAL-G) class of distributions. The study authors thoroughly examine the NAL-G class and a unique model, the NAL-Exponential (NAL-Exp) distribution, with a focus on analyzing mathematical properties such as moments, quantile function, entropy, order statistics, and more. Six recognized classical estimation methods are employed, and extensive simulations are conducted to determine the best one. The NAL-Exp distribution demonstrates its high adaptability and value through its superior performance in modeling four distinct rainfall data sets. The results show that the NAL-Exp distribution outperforms other commonly used distribution models, highlighting its potential as a valuable tool in hydrological modeling and analysis. The increased versatility and flexibility of this new approach hold great potential for enhancing the accuracy and reliability of future rainfall predictions.http://www.sciencedirect.com/science/article/pii/S2405844023067695Exponential distributionGenerated classesMomentsEntropyPrecipitation (rainfall)Classical estimation techniques |
| spellingShingle | Aned Al Mutairi A new alpha logarithmic-generated class to model precipitation data with theory and inference Heliyon Exponential distribution Generated classes Moments Entropy Precipitation (rainfall) Classical estimation techniques |
| title | A new alpha logarithmic-generated class to model precipitation data with theory and inference |
| title_full | A new alpha logarithmic-generated class to model precipitation data with theory and inference |
| title_fullStr | A new alpha logarithmic-generated class to model precipitation data with theory and inference |
| title_full_unstemmed | A new alpha logarithmic-generated class to model precipitation data with theory and inference |
| title_short | A new alpha logarithmic-generated class to model precipitation data with theory and inference |
| title_sort | new alpha logarithmic generated class to model precipitation data with theory and inference |
| topic | Exponential distribution Generated classes Moments Entropy Precipitation (rainfall) Classical estimation techniques |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023067695 |
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