Unraveling water monitoring association towards weather attributes for response proportions data: A unit-Lindley learning.
Learning techniques involve unraveling regression structures, which aim to analyze in a probabilistic frame the associations across variables of interest. Thus, analyzing fraction and/or proportion data may not be adequate with standard regression procedures, since the linear regression models gener...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0275841 |
| _version_ | 1811337497692602368 |
|---|---|
| author | Paulo H Ferreira Anderson O Fonseca Diego C Nascimento Estefania Bonnail Francisco Louzada |
| author_facet | Paulo H Ferreira Anderson O Fonseca Diego C Nascimento Estefania Bonnail Francisco Louzada |
| author_sort | Paulo H Ferreira |
| collection | DOAJ |
| description | Learning techniques involve unraveling regression structures, which aim to analyze in a probabilistic frame the associations across variables of interest. Thus, analyzing fraction and/or proportion data may not be adequate with standard regression procedures, since the linear regression models generally assume that the dependent (outcome) variable is normally distributed. In this manner, we propose a statistical model called unit-Lindley regression model, for the purpose of Statistical Process Control (SPC). As a result, a new control chart tool was proposed, which targets the water monitoring dynamic, as well as the monitoring of relative humidity, per minute, of Copiapó city, located in Atacama Desert (one of the driest non-polar places on Earth), north of Chile. Our results show that variables such as wind speed, 24-hour temperature variation, and solar radiation are useful to describe the amount of relative humidity in the air. Additionally, Information Visualization (InfoVis) tools help to understand the time seasonality of the water particle phenomenon of the region in near real-time analysis. The developed methodology also helps to label unusual events, such as Camanchaca, and other water monitoring-related events. |
| first_indexed | 2024-04-13T17:55:46Z |
| format | Article |
| id | doaj.art-0340374f54ce4b86abe391e801c89683 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-13T17:55:46Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-0340374f54ce4b86abe391e801c896832022-12-22T02:36:30ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-011710e027584110.1371/journal.pone.0275841Unraveling water monitoring association towards weather attributes for response proportions data: A unit-Lindley learning.Paulo H FerreiraAnderson O FonsecaDiego C NascimentoEstefania BonnailFrancisco LouzadaLearning techniques involve unraveling regression structures, which aim to analyze in a probabilistic frame the associations across variables of interest. Thus, analyzing fraction and/or proportion data may not be adequate with standard regression procedures, since the linear regression models generally assume that the dependent (outcome) variable is normally distributed. In this manner, we propose a statistical model called unit-Lindley regression model, for the purpose of Statistical Process Control (SPC). As a result, a new control chart tool was proposed, which targets the water monitoring dynamic, as well as the monitoring of relative humidity, per minute, of Copiapó city, located in Atacama Desert (one of the driest non-polar places on Earth), north of Chile. Our results show that variables such as wind speed, 24-hour temperature variation, and solar radiation are useful to describe the amount of relative humidity in the air. Additionally, Information Visualization (InfoVis) tools help to understand the time seasonality of the water particle phenomenon of the region in near real-time analysis. The developed methodology also helps to label unusual events, such as Camanchaca, and other water monitoring-related events.https://doi.org/10.1371/journal.pone.0275841 |
| spellingShingle | Paulo H Ferreira Anderson O Fonseca Diego C Nascimento Estefania Bonnail Francisco Louzada Unraveling water monitoring association towards weather attributes for response proportions data: A unit-Lindley learning. PLoS ONE |
| title | Unraveling water monitoring association towards weather attributes for response proportions data: A unit-Lindley learning. |
| title_full | Unraveling water monitoring association towards weather attributes for response proportions data: A unit-Lindley learning. |
| title_fullStr | Unraveling water monitoring association towards weather attributes for response proportions data: A unit-Lindley learning. |
| title_full_unstemmed | Unraveling water monitoring association towards weather attributes for response proportions data: A unit-Lindley learning. |
| title_short | Unraveling water monitoring association towards weather attributes for response proportions data: A unit-Lindley learning. |
| title_sort | unraveling water monitoring association towards weather attributes for response proportions data a unit lindley learning |
| url | https://doi.org/10.1371/journal.pone.0275841 |
| work_keys_str_mv | AT paulohferreira unravelingwatermonitoringassociationtowardsweatherattributesforresponseproportionsdataaunitlindleylearning AT andersonofonseca unravelingwatermonitoringassociationtowardsweatherattributesforresponseproportionsdataaunitlindleylearning AT diegocnascimento unravelingwatermonitoringassociationtowardsweatherattributesforresponseproportionsdataaunitlindleylearning AT estefaniabonnail unravelingwatermonitoringassociationtowardsweatherattributesforresponseproportionsdataaunitlindleylearning AT franciscolouzada unravelingwatermonitoringassociationtowardsweatherattributesforresponseproportionsdataaunitlindleylearning |