http://197.159.135.214/jspui/handle/123456789/49 2026-04-23T15:08:06Z 2026-04-23T15:08:06Z Adounkpe, Julien Yise Peniel Alamou, Eric Adechina Diallo, Belko Abdoul Aziz Ali, Abdou http://197.159.135.214/jspui/handle/123456789/536 2023-01-30T12:09:44Z 2021-01-01T00:00:00Z

Predicting Discharge in Catchment Outlet Using Deep Learning: Case Study of the Ansongo-Niamey Basin Adounkpe, Julien Yise Peniel; Alamou, Eric Adechina; Diallo, Belko Abdoul Aziz; Ali, Abdou 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 either physical or 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. These breakthroughs were mostly favored by the large volume, variety and velocity of water-related data. Long Short-Term Memory and Gated Recurrent Unit neural networks, 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, precipitation, minimal and maximum temperature at the Ansongo-Niamey basin combined with the discharge at Ansongo and Kandadji were used to predict the discharge at Niamey using artificial neural networks. After data preprocessing and hyperparameter optimization, the deep learning models performed well with the LSTM and GRU respectively scoring a Nash-Sutcliffe Efficiency of 0.933 and 0.935. 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, especially in Niamey which has been facing severe floods due to climate change. Research Article

2021-01-01T00:00:00Z Kitegi, Mawunyo Simon Pierre Lare, Yendoube Coulibaly, Ousmane http://197.159.135.214/jspui/handle/123456789/535 2023-01-30T12:08:55Z 2022-02-01T00:00:00Z

Potential for Green Hydrogen Production from Biomass, Solar and Wind in Togo Kitegi, Mawunyo Simon Pierre; Lare, Yendoube; Coulibaly, Ousmane Potential of green hydrogen producing from biomass, solar and wind in Togo has been performed. The availability of 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. The conversions rates used were: for solar resource, 3% of land was 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. Results showed that biomass is the leading resource for producing green hydrogen from renewable energy resources; with good impact in these two cantons: Bassar, Gobe/ Eketo/Gbadi N’Kugna. However, this resource is still decreasing and in some cantons it is null. Research Article

2022-02-01T00:00:00Z