scholarly journals Using self-organizing maps to infill missing data in hydro-meteorological time series from the Logone catchment, Lake Chad basin

Author(s):  
E. Nkiaka ◽  
N. R. Nawaz ◽  
J. C. Lovett
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.


2016 ◽  
Vol 37 (9) ◽  
pp. 3553-3564 ◽  
Author(s):  
E. Nkiaka ◽  
N. R. Nawaz ◽  
J. C. Lovett

2021 ◽  
Vol 13 (15) ◽  
pp. 8295
Author(s):  
Patricia Melin ◽  
Oscar Castillo

In this article, the evolution in both space and time of the COVID-19 pandemic is studied by utilizing a neural network with a self-organizing nature for the spatial analysis of data, and a fuzzy fractal method for capturing the temporal trends of the time series of the countries considered in this study. Self-organizing neural networks possess the capability to cluster countries in the space domain based on their similar characteristics, with respect to their COVID-19 cases. This form enables the finding of countries that have a similar behavior, and thus can benefit from utilizing the same methods in fighting the virus propagation. In order to validate the approach, publicly available datasets of COVID-19 cases worldwide have been used. In addition, a fuzzy fractal approach is utilized for the temporal analysis of the time series of the countries considered in this study. Then, a hybrid combination, using fuzzy rules, of both the self-organizing maps and the fuzzy fractal approach is proposed for efficient coronavirus disease 2019 (COVID-19) forecasting of the countries. Relevant conclusions have emerged from this study that may be of great help in putting forward the best possible strategies in fighting the virus pandemic. Many of the existing works concerned with COVID-19 look at the problem mostly from a temporal viewpoint, which is of course relevant, but we strongly believe that the combination of both aspects of the problem is relevant for improving the forecasting ability. The main idea of this article is combining neural networks with a self-organizing nature for clustering countries with a high similarity and the fuzzy fractal approach for being able to forecast the times series. Simulation results of COVID-19 data from countries around the world show the ability of the proposed approach to first spatially cluster the countries and then to accurately predict in time the COVID-19 data for different countries with a fuzzy fractal approach.


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