scholarly journals Delineation of groundwater potential zones by means of ensemble tree supervised classification methods in the Eastern Lake Chad basin

2021 ◽  
pp. 1-21
Author(s):  
Víctor Gómez-Escalonilla ◽  
Marie-Louise Vogt ◽  
Elisa Destro ◽  
Moussa Isseini ◽  
Giaime Origgi ◽  
...  
Earth ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 340-356
Author(s):  
Forrest W. Black ◽  
Jejung Lee ◽  
Charles M. Ichoku ◽  
Luke Ellison ◽  
Charles K. Gatebe ◽  
...  

The present study investigated the effect of biomass burning on the water cycle using a case study of the Chari–Logone Catchment of the Lake Chad Basin (LCB). The Chari–Logone catchment was selected because it supplies over 90% of the water input to the lake, which is the largest basin in central Africa. Two water balance simulations, one considering burning and one without, were compared from the years 2003 to 2011. For a more comprehensive assessment of the effects of burning, albedo change, which has been shown to have a significant impact on a number of environmental factors, was used as a model input for calculating potential evapotranspiration (ET). Analysis of the burning scenario showed that burning grassland, which comprises almost 75% of the total Chari–Logone land cover, causes increased ET and runoff during the dry season (November–March). Recent studies have demonstrated that there is an increasing trend in the LCB of converting shrubland, grassland, and wetlands to cropland. This change from grassland to cropland has the potential to decrease the amount of water available to water bodies during the winter. All vegetative classes in a burning scenario showed a decrease in ET during the wet season. Although a decrease in annual precipitation in global circulation processes such as the El Niño Southern Oscillation would cause droughts and induce wildfires in the Sahel, the present study shows that a decrease in ET by the human-induced burning would cause a severe decrease in precipitation as well.


2007 ◽  
Vol 3 ◽  
pp. 117693510700300 ◽  
Author(s):  
Nadège Dossat ◽  
Alain Mangé ◽  
Jérôme Solassol ◽  
William Jacot ◽  
Ludovic Lhermitte ◽  
...  

A key challenge in clinical proteomics of cancer is the identification of biomarkers that could allow detection, diagnosis and prognosis of the diseases. Recent advances in mass spectrometry and proteomic instrumentations offer unique chance to rapidly identify these markers. These advances pose considerable challenges, similar to those created by microarray-based investigation, for the discovery of pattern of markers from high-dimensional data, specific to each pathologic state (e.g. normal vs cancer). We propose a three-step strategy to select important markers from high-dimensional mass spectrometry data using surface enhanced laser desorption/ionization (SELDI) technology. The first two steps are the selection of the most discriminating biomarkers with a construction of different classifiers. Finally, we compare and validate their performance and robustness using different supervised classification methods such as Support Vector Machine, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Neural Networks, Classification Trees and Boosting Trees. We show that the proposed method is suitable for analysing high-throughput proteomics data and that the combination of logistic regression and Linear Discriminant Analysis outperform other methods tested.


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