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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Main Author: Kim-Ndor Djimadoumngar
Format: Article
Language:English
Published: Elsevier 2023-12-01
Series:Applied Computing and Geosciences
Subjects:
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.
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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