Parallel investigations of remote sensing and ground-truth Lake Chad's level data using statistical and machine learning methods
Lake Chad is facing critical situations since the 1960s due to the effects of climate change and anthropogenic activities. The statistical analyses of remote sensing climate variables (i.e., evapotranspiration, specific humidity, soil temperature, air temperature, precipitation, soil moisture) and r...
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Format: | Article |
Language: | English |
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Elsevier
2023-12-01
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Series: | Applied Computing and Geosciences |
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Online Access: | http://www.sciencedirect.com/science/article/pii/S2590197423000241 |
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author | Kim-Ndor Djimadoumngar |
author_facet | Kim-Ndor Djimadoumngar |
author_sort | Kim-Ndor Djimadoumngar |
collection | DOAJ |
description | Lake Chad is facing critical situations since the 1960s due to the effects of climate change and anthropogenic activities. The statistical analyses of remote sensing climate variables (i.e., evapotranspiration, specific humidity, soil temperature, air temperature, precipitation, soil moisture) and remote sensing and ground-truth lake level applied to the period 1993–2012 reveal that remote sensing data has a skewed distribution; ground-truth data has a symmetrical distribution. Linear Regression (LR), Support Vector Regression (SVR), Regression Tree (RT), Random Forest Regression (RF), and Deep Learning (DL) methods show that (i) RF and LR, with the highest R2 and EVS and least MAE, MSE, RMSE and, CVMSE values seem the best models to further investigate remote sensing and ground-truth lake level data and (ii) the remote sensing data based models outperform the ground-truth data based models based on their MAE, MSE, RMSE, and CVMSE values. The most useful variables to predict lake level are precipitation and air temperature. The data analysis methodology reported here is of fundamental importance for the perspectives of an integrated and forward-looking water management system for connecting climate change, vulnerability, and human activities in the Lake Chad human-environment system. Corroboration studies are needed when more ground-truth data eventually are obtainable. |
first_indexed | 2024-03-08T21:49:07Z |
format | Article |
id | doaj.art-659071fb06c24b02bb3f8382bd94d2bc |
institution | Directory Open Access Journal |
issn | 2590-1974 |
language | English |
last_indexed | 2024-03-08T21:49:07Z |
publishDate | 2023-12-01 |
publisher | Elsevier |
record_format | Article |
series | Applied Computing and Geosciences |
spelling | doaj.art-659071fb06c24b02bb3f8382bd94d2bc2023-12-20T07:36:38ZengElsevierApplied Computing and Geosciences2590-19742023-12-0120100135Parallel investigations of remote sensing and ground-truth Lake Chad's level data using statistical and machine learning methodsKim-Ndor Djimadoumngar0B.P. 280, Yaounde, CameroonLake Chad is facing critical situations since the 1960s due to the effects of climate change and anthropogenic activities. The statistical analyses of remote sensing climate variables (i.e., evapotranspiration, specific humidity, soil temperature, air temperature, precipitation, soil moisture) and remote sensing and ground-truth lake level applied to the period 1993–2012 reveal that remote sensing data has a skewed distribution; ground-truth data has a symmetrical distribution. Linear Regression (LR), Support Vector Regression (SVR), Regression Tree (RT), Random Forest Regression (RF), and Deep Learning (DL) methods show that (i) RF and LR, with the highest R2 and EVS and least MAE, MSE, RMSE and, CVMSE values seem the best models to further investigate remote sensing and ground-truth lake level data and (ii) the remote sensing data based models outperform the ground-truth data based models based on their MAE, MSE, RMSE, and CVMSE values. The most useful variables to predict lake level are precipitation and air temperature. The data analysis methodology reported here is of fundamental importance for the perspectives of an integrated and forward-looking water management system for connecting climate change, vulnerability, and human activities in the Lake Chad human-environment system. Corroboration studies are needed when more ground-truth data eventually are obtainable.http://www.sciencedirect.com/science/article/pii/S2590197423000241Parallel investigationsRemote sensingGround-truthLake Chad's levelStatisticalMachine learning |
spellingShingle | Kim-Ndor Djimadoumngar Parallel investigations of remote sensing and ground-truth Lake Chad's level data using statistical and machine learning methods Applied Computing and Geosciences Parallel investigations Remote sensing Ground-truth Lake Chad's level Statistical Machine learning |
title | Parallel investigations of remote sensing and ground-truth Lake Chad's level data using statistical and machine learning methods |
title_full | Parallel investigations of remote sensing and ground-truth Lake Chad's level data using statistical and machine learning methods |
title_fullStr | Parallel investigations of remote sensing and ground-truth Lake Chad's level data using statistical and machine learning methods |
title_full_unstemmed | Parallel investigations of remote sensing and ground-truth Lake Chad's level data using statistical and machine learning methods |
title_short | Parallel investigations of remote sensing and ground-truth Lake Chad's level data using statistical and machine learning methods |
title_sort | parallel investigations of remote sensing and ground truth lake chad s level data using statistical and machine learning methods |
topic | Parallel investigations Remote sensing Ground-truth Lake Chad's level Statistical Machine learning |
url | http://www.sciencedirect.com/science/article/pii/S2590197423000241 |
work_keys_str_mv | AT kimndordjimadoumngar parallelinvestigationsofremotesensingandgroundtruthlakechadsleveldatausingstatisticalandmachinelearningmethods |