The Centre of Excellence in Mathematics and ICT (CEA-MITIC) hosted by Gaston Berger University (UGB) of Saint-Louis in Senegal actively develops human capital through its Masters and PhD ICT degree programmes and short courses, strengthening research capacities in the areas of secure networks and systems with mobility (including the Internet of Things); modeling of complex systems; materials-components-systems; mathematics and modeling; and computer systems and knowledge (including artificial intelligence). MITIC aims to develop strong and relevant research activities that can solve developmental challenges impacting Africa, produce knowledge and innovative solutions connected with the productive sectors of agriculture, environment, health, and the digital economy.
MITIC is spearheading the Saint-Louis Digital 2025 project. The project engaged all departments at UGB, as well as local authorities in the Saint Louis region, to develop the city as an industrial center based on digital technology.
Green technology and climate have also incresingly become key areas of research pursued at MITIC. In an article published on the EARTH.ORG website, green technology is defined as “the type and use of technology that are considered environmentally friendly based on its production process or its supply chain, which as a result reduces our carbon footprint“.
The April 2022 climate change report on mitigation of climate change by the Working Group III of the Intergovernmental Panel on Climate Change’s appropriately emphasized that: “Digital technologies can promote large increases in energy efficiency through coordination and an economic shift to services …”.
MITIC has produced several impactful research outputs toward promoting green technology and climate change adaptation and mitigation.
- MITIC is involved in the following green technology research: reduction of energy consumption of electronic devices through implementation of biodegradable electronic circuits and devices; energy recovery from agricultural residues by gasification for electricity production; and has installed a pseudo-gasification reactor to champion waste to energy technologies.
- The case of incomplete meteorological data: To mitigate climate change and green technology MITIC analysed missing meteorological data from the Senegal databases. Climate change studies and mitigation require complete and reliable meteorological databases to analyse climate indications, monitor its evolution, and accurately predict future variations. MITIC evaluated 5 methods and found that that the missForest method was able to reconstruct temperature data most accurately. The significance of this study on green technology and climate change mitigation was that the Senegal meteorological data from 1973 to 2020 could be reconstructed to support the readiness of Senegal to alleviate climate change impacts.
- Malaria community-based early-warning systems and adaptation strategies:
Illnesses that are transmitted by organisms that act as routes such as mosquitoes, flies, ticks are sensitive to climate and weather conditions. MITIC examined malaria data from the Senegal National Malaria Control Program and outputs from climate data and compared these data sets. The findings revealed that seasonal malaria transmission was closely associated with the variation of the rainfall. This study revealed that the peak of malaria takes place from September to October, with a lag of around one month from the peak of rainfall in Senegal. These results indicated that the southern part of Senegal was at the highest risk of malaria epidemics. The conclusions in the paper are projected to guide community-based early-warning systems and adaptation strategies in Senegal. These strategies would strengthen the Senegal national malaria prevention, response strategies, and care strategies that are tailored to the needs of local communities.
- Weather forecasting using the Ensemble machine learning model.
Machine Learning is one of the technologies used in agriculture for weather forecasting, crop disease detection and other applications. Machine learning entails computers learning from data provided so that they carry out certain tasks. MITIC conducted research to develop Machine Learning-based models designed to handle daily weather forecasting for rainfall, relative humidity, and maximum and minimum temperature in Senegal. In this research, MITIC compared ten Machine Learning Regressors with their Ensemble Model. These models were evaluated based on mean absolute error, mean squared error, root mean squared error and coefficient of determination. The results showed that the Ensemble Model performed better than the other models. The importance of this study affirmed that the Ensemble machine learning model could support the protection of the environment through accurate weather forecasting in Senegal.
- An IoT based system for pollution prediction and assessment.
MITIC developed a distributed and intelligent system to assess and predict pollution in Southern Senegal. The Internet of Things (IoT) intelligent platform assesses the impact of incineration in public dumps of households and similar waste, as well as the impact of burning sugar cane on the health of populations. The system collects data on the type of atmospheric pollutants resulting from the incineration of garbage in the communities of Saint Louis and Richard Toll. The research also analysed the possible links between types of pollutants (that is, CO, CO2, NO, NO2, PM10, PM2.5, PM1, black carbon, and volatile organic compounds) and respiratory diseases (Asthma, Acute Respiratory Infections, and Meningitis). The platform is an IoT Fog/Edge network that distributes computation, communication, control, and storage closer to the end users along the cloud-to-things continuum. The relevance of fog/edge is entrenched in both the inadequacy of the traditional cloud and the emergence of new opportunities for the Internet of Things, fifth generation cellular network standards (5G) and embedded artificial intelligence. This MITIC study demonstrates the use of high-end computer science technologies to address pollution challenges and associated health challenges.
- Energy Efficiency related research
MITIC is also involved in research on “low energy consumption” by studying the reduction of energy consumption of electronic devices through implementation of biodegradable electronic circuits and devices. MITIC is also working on energy recovery from agricultural residues by gasification and the evaluation of the potential of different crop residues. The research also evaluated the gasification systems for electricity production and tested / optimized the selected models. MITIC has installed a pseudo-gasification reactor to champion waste to energy technologies. MITIC has optimized biogas production from residues obtained after the processing of fish products. This research aims to solve the problem related to waste management, in particular fish product residues. The goal is to develop a biogas production industry from fish waste.
The research by MITIC clearly demonstrates their leadership in the areas of green technologies and climate change mitigation through high-end research. Green technologies based on internet of things, machine learning and artificial intelligence have been developed by MITIC to improve weather forecasting, assess pollution and develop energy efficient devices. Through quality research MITIC has also supported the reconstruction of Senegal’s meteorological data and developed malaria early warning systems.