http://197.159.135.214/jspui/handle/123456789/22 2026-04-04T02:41:03Z 2026-04-04T02:41:03Z Kitegi, Mawunyo Simon Pierre http://197.159.135.214/jspui/handle/123456789/981 2024-09-09T08:43:45Z 2021-07-16T00:00:00Z

Potential for Green Hydrogen Production from Biomass, Solar and Wind for the rising of Green Hydrogen Economy in Togo Kitegi, Mawunyo Simon Pierre Potential of green hydrogen producing from biomass, solar and wind in Togo has been performed. The availability of all these three resources has been depicted with maps showing them per cantons in Togo, thus, by using the datasets from ESA Biomass Climate Change Initiative, the global solar atlas and the global wind atlas. In order to evaluate the potential of green hydrogen from all these resources these conversions rates were used: for solar resource, 3% of land were allocated for the analysis after removing the exclusions with a conversion rate of 52.5 kWh/kg of hydrogen; for biomass hydrogen, the conversion rate of 13.4 kg BS/kg H2 was assumed. Wind resources at 50 m above ground were not sufficient to evaluate the potential as it is lower than class 3 winds. QGIS version 3.6.4 and R version 4.0.4 were used. Finally, biomass is the leading resource for producing green hydrogen from renewable energy resource; with good impact in these two cantons: Bassar, Gobe/Eketo/Gbadi N'Kugna. This study has shown that biomass is the lead source of energy but is still decreasing and in some cantons it is null. A Thesis submitted to the West African Science Service Center on Climate Change and Adapted Land Use and Université Joseph KI-ZERBO, Burkina Faso in partial fulfillment of the requirements for the Master of Science Degree in Informatics for Climate Change

2021-07-16T00:00:00Z Kiribou, Abdou-Razakou Issaka http://197.159.135.214/jspui/handle/123456789/924 2024-09-03T14:45:53Z 2021-07-13T00:00:00Z

Greenhouse Gas Emission in road Transport and Urban Mobility in Ouagadougou: Evaluation and Modelling of its Effect on Air Pollution Kiribou, Abdou-Razakou Issaka The transport and urban mobility sector in Burkina Faso is one of the sectors that strongly contributes to greenhouse gases (GHG) emissions, as well as atmospheric pollutants. The aim of this study was to assess and model road transport and urban mobility contribution to greenhouse gas emissions and fossil fuel demand. Thus, the methodology of is consisted of the assessment of road transport legislation related to environment protection in Burkina Faso. The development of a bottom-up model to estimate the historical trends of vehicle fleet growth and energy demand using the Long-Range Energy Alternative Planning (LEAP) software. It assesses greenhouse gases by establishing the specific emission factors and takes the city of Ouagadougou as a case study which represents 48% of the urban population of the country and 83.67% of the vehicle fleet. The results show a significant air pollutants and GHG emissions, which coupling with the high vehicle fleet growth resulting in fuel consumption of more than 89% of the fossil fuel sold in Burkina Faso. Gasoline occupies 10% and is 98% consumed by two-wheelers. Diesel occupies 90% and is 80% consumed by trailers and trucks. Thus a total of 0.47 GtCO2eq/year of Carbone dioxide (CO2) is emitted, diesel vehicles contribute to 87.49% and gasoline vehicles for 12.51%. The most air pollutants are CO2 (4734851.666 tons), NOx (60879.311 tons), CO (23076.198 tons) and some trace of NO2, SO2 etc. The CO2 emission is led by tractor and truck vehicle, respectively 55.20% and 20.25%, flowed by Motorbike (8.48%), Vans (7.42%), Private vehicle (5.02%) and Bus (3.5%) for CO2. The CO emission is dominated by the private cars, 73.84%, with gasoline and by truck vehicle with diesel 72.4%. The NOx is dominated by motorbike with gasoline fuel 77.76% and by truck with diesel 72.40%. The stratospheric ozone is mostly affected in the central area of the city. Thus, this project showed the need of an incentives of environmental regulations and climate change mitigation for sustainable mobility. It may assist the policymakers in achieving lower emission in both, air pollutant and GHG emission in road transport. A Thesis submitted to the West African Science Service Center on Climate Change and Adapted Land Use and Université Joseph KI-ZERBO, Burkina Faso in partial fulfillment of the requirements for the Master of Science Degree in Informatics for Climate Change

2021-07-13T00:00:00Z Adounkpe, Julien Yise Peniel http://197.159.135.214/jspui/handle/123456789/923 2024-09-03T14:13:21Z 2021-01-01T00:00:00Z

Predicting discharge in catchment outlet using deep learning methods: case study of Niamey in the Ansongo-Niamey basin Adounkpe, Julien Yise Peniel Hydrological models are one of the key challenges in hydrology. Their goal is to understand, predict and manage water resources. Most of the hydrological models so far were physically based and conceptual models. But in the past two decades, fully data-driven (empirical) models started to emerge with the breakthroughs of novel deep learning methods in runoff prediction. The breakthrough was mostly favored by the large volume, variety and velocity of water-related data. Long Short-Term Memory networks (LSTMs), particularly achieved the outstanding milestone of outperforming classic hydrological models in less than a decade. Moreover, they have the potential to change the way hydrological modeling is performed. In this study, we used precipitation, maximum and minimal temperature at the Ansongo-Niamey basin mixed with the discharge at Ansongo and Kandadji to predict the discharge at Niamey using LSTM neural networks. After data prepossessing and hyperparameter optimization, the LSTM model performed well with a R2 of 0.897, a NSE of 0.852 and a RMSE of 229.158. This performance matches those of well-known physically based models used to simulate Niamey’s discharge and therefore demonstrates the efficiency of deep learning methods in a West African context. A Thesis submitted to the West African Science Service Center on Climate Change and Adapted Land Use and Université Joseph KI-ZERBO, Burkina Faso in partial fulfillment of the requirements for the Master of Science Degree in Informatics for Climate Change

2021-01-01T00:00:00Z Elh Maman Garba, Ibrahim http://197.159.135.214/jspui/handle/123456789/922 2024-09-03T13:51:43Z 2021-07-19T00:00:00Z

Evaluate the effectiveness of local rainfall forecast application for farmers making decision Elh Maman Garba, Ibrahim In Niger rainfall forecasts are provided at large scale and by administrative region which does not support farmer for strategic decisions on crop management and risk reduction strategies. This study evaluate effectiveness of two local rainfall forecasts Application (Accuweather and Weather channel) for agricultural decision making in the regions of Zinder and Tahoua in Niger. The applications were installed in the smartphone of 10 fields technicians based in 10 communes, respectively. Results showed that the best forecast effectiveness was 67% with Accuweather against 73% for weather channel and with rainfall occurrence above 91% and 81%, respectively. For both applications, the higher is forecast, and the greater is the rainfall occurrence. We infer that forecast percentage threshold is important for users for decision making. For example, with a forecast above 70% indicates high rainfall occurrence. In this context, farmers can trust forecasts and schedule their agricultural activities. However, there was a maximum number of forecasts within 21-30% interval with an effectiveness below 25%. Here, it needs not trust the forecasts of these two applications for making agricultural decisions. In this study, many farmers were interested in using these applications. At this stage, it is highly recommended to repeat the experimentation while focusing on acceptance threshold, which could better serve farmers in their decision making. A Thesis submitted to the West African Science Service Center on Climate Change and Adapted Land Use and Université Joseph KI-ZERBO, Burkina Faso in partial fulfillment of the requirements for the Master of Science Degree in Informatics for Climate Change

2021-07-19T00:00:00Z