Analysis and prediction of climate forecasts in Northern Morocco: application of multilevel linear mixed effects models using R software
For many years, the application of mixed-effects modeling has received much attention for predicting scenarios in the fields of theoretical and applied sciences. In this study, a “new” Multilevel Linear Mixed-Effects (LME) model is proposed to analyze and predict multiply-nested and hierarchical dat...
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| Format: | Article |
| Language: | English |
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Elsevier
2020-10-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S240584402031937X |
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| author | Mohamed Beroho Hamza Briak Rachid El Halimi Abdessalam Ouallali Imane Boulahfa Rachid Mrabet Fassil Kebede Khadija Aboumaria |
| author_facet | Mohamed Beroho Hamza Briak Rachid El Halimi Abdessalam Ouallali Imane Boulahfa Rachid Mrabet Fassil Kebede Khadija Aboumaria |
| author_sort | Mohamed Beroho |
| collection | DOAJ |
| description | For many years, the application of mixed-effects modeling has received much attention for predicting scenarios in the fields of theoretical and applied sciences. In this study, a “new” Multilevel Linear Mixed-Effects (LME) model is proposed to analyze and predict multiply-nested and hierarchical data. Temperature and rainfall observation were carried out successively between 1979-2014 and 1984–2018; and the data input was organized on monthly basis for each year. Besides, a daily observation was made for “Dar Chaoui” zone of Northern Morocco. However, we chose in the first time a simple linear regression model, but the estimation has been just for fixed effects and ignoring the random effect. On the other hand, in multilevel linear mixed effects models, once the model has been formulated, methods are needed to estimate the model parameters. In this section, we first deal with the joint estimation of the fixed effects (β), random effects (ui) and then with estimation of the variance parameters (γ, ρ and σ2). The study revealed that the predicted values are very close to the real value. Besides, this model is capable of modelling the error, fixed and random parts of the sample. Moreover, in this range, the results showed that there is three standard deviations measures for fixed and random effects, also the variance measure, which demonstrate us a great prediction. In conclusion, this model gives a decisive precision of results that can be exploited in studies for forecast of water balance and/or soil erosion. These results can also be used to inhibit the risk of erosion with possible arrangements for the environment and human security. |
| first_indexed | 2024-12-14T16:06:01Z |
| format | Article |
| id | doaj.art-b0f836d6ea3c4be2b83dd2ace6fddaaa |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-12-14T16:06:01Z |
| publishDate | 2020-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-b0f836d6ea3c4be2b83dd2ace6fddaaa2022-12-21T22:55:06ZengElsevierHeliyon2405-84402020-10-01610e05094Analysis and prediction of climate forecasts in Northern Morocco: application of multilevel linear mixed effects models using R softwareMohamed Beroho0Hamza Briak1Rachid El Halimi2Abdessalam Ouallali3Imane Boulahfa4Rachid Mrabet5Fassil Kebede6Khadija Aboumaria7Department of Earth Sciences, Faculty of Sciences and Techniques of Tangier (FST), Abdelmalek Essaadi University (UAE), Morocco; Departmentof Environment and Natural Resources, Scientific Division, National Institute for Agricultural Research of Rabat (INRA), Morocco; Corresponding author.Center of Excellence for Soil and Fertilizer Research in Africa (CESFRA), Mohammed VI Polytechnic University (UM6P), MoroccoDepartment of Mathematics and Statistics, Faculty of Sciences and Techniques of Tangier (FST), Abdelmalek Essaadi University (UAE), MoroccoDepartment of Earth Sciences, Faculty of Sciences of Tetouan (FS), Abdelmalek Essaadi University (UAE), MoroccoDepartment of Earth Sciences, Faculty of Sciences and Techniques of Tangier (FST), Abdelmalek Essaadi University (UAE), MoroccoDepartmentof Environment and Natural Resources, Scientific Division, National Institute for Agricultural Research of Rabat (INRA), MoroccoCenter of Excellence for Soil and Fertilizer Research in Africa (CESFRA), Mohammed VI Polytechnic University (UM6P), MoroccoDepartment of Earth Sciences, Faculty of Sciences and Techniques of Tangier (FST), Abdelmalek Essaadi University (UAE), MoroccoFor many years, the application of mixed-effects modeling has received much attention for predicting scenarios in the fields of theoretical and applied sciences. In this study, a “new” Multilevel Linear Mixed-Effects (LME) model is proposed to analyze and predict multiply-nested and hierarchical data. Temperature and rainfall observation were carried out successively between 1979-2014 and 1984–2018; and the data input was organized on monthly basis for each year. Besides, a daily observation was made for “Dar Chaoui” zone of Northern Morocco. However, we chose in the first time a simple linear regression model, but the estimation has been just for fixed effects and ignoring the random effect. On the other hand, in multilevel linear mixed effects models, once the model has been formulated, methods are needed to estimate the model parameters. In this section, we first deal with the joint estimation of the fixed effects (β), random effects (ui) and then with estimation of the variance parameters (γ, ρ and σ2). The study revealed that the predicted values are very close to the real value. Besides, this model is capable of modelling the error, fixed and random parts of the sample. Moreover, in this range, the results showed that there is three standard deviations measures for fixed and random effects, also the variance measure, which demonstrate us a great prediction. In conclusion, this model gives a decisive precision of results that can be exploited in studies for forecast of water balance and/or soil erosion. These results can also be used to inhibit the risk of erosion with possible arrangements for the environment and human security.http://www.sciencedirect.com/science/article/pii/S240584402031937XEnvironmentMathematicsClimate forecastMultilevel linear mixed-effectsHierarchical modelR software |
| spellingShingle | Mohamed Beroho Hamza Briak Rachid El Halimi Abdessalam Ouallali Imane Boulahfa Rachid Mrabet Fassil Kebede Khadija Aboumaria Analysis and prediction of climate forecasts in Northern Morocco: application of multilevel linear mixed effects models using R software Heliyon Environment Mathematics Climate forecast Multilevel linear mixed-effects Hierarchical model R software |
| title | Analysis and prediction of climate forecasts in Northern Morocco: application of multilevel linear mixed effects models using R software |
| title_full | Analysis and prediction of climate forecasts in Northern Morocco: application of multilevel linear mixed effects models using R software |
| title_fullStr | Analysis and prediction of climate forecasts in Northern Morocco: application of multilevel linear mixed effects models using R software |
| title_full_unstemmed | Analysis and prediction of climate forecasts in Northern Morocco: application of multilevel linear mixed effects models using R software |
| title_short | Analysis and prediction of climate forecasts in Northern Morocco: application of multilevel linear mixed effects models using R software |
| title_sort | analysis and prediction of climate forecasts in northern morocco application of multilevel linear mixed effects models using r software |
| topic | Environment Mathematics Climate forecast Multilevel linear mixed-effects Hierarchical model R software |
| url | http://www.sciencedirect.com/science/article/pii/S240584402031937X |
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