The collected data are stored, analysed and modelled using machine learning algorithms to make prediction of water consumption throughout a period of two weeks, therefore informing water suppliers about the amount of water that shall be produced in the future. To make this system more intelligent, machine learning data trains to be conducted on the system by the use of data sensed in different periods of the two weeks from Meteo Rwanda , the consideration of garden watering timetables and schedules to be considered too.
Nowadays, Rwanda is becoming the country where the technology is integrated in agriculture and livestock domains to easily enhance the life conditions of every Rwandan for wellbeing. With the technology development, cow’s health and early diagnosis of disease have gained the importance in agri...
Read more>>The main objective of this research is to assess the importance of using an automated streetlight management and monitoring system. This research seeks to contribute in REG’s management and monitoring of streetlight. The information obtained through the research will assist different organizat...
Read more>>Water is a vital resource in practically every aspect of our lives, including our homes, where water is used for cleaning, drinking, and a variety of other activities. Governments have already put in place strategic measures to supply this natural resource in urban and rural areas where people dwell...
Read more>>Biogas is produced from biological process of mixed organic materials with the help of bacteria that facilitate the anaerobic digestion process. Biogas can be produced from manure agricultural waste or from other biomass resources available almost everywhere. Many developing countries especially in...
Read more>>Floods and landslides are among Rwanda's biggest calamities, having a significant impact on human lives, human development, property, infrastructure, and the environment as a result of topographical factors, excessive rain, and mining activities. The design and implementation of IoT-based floods...
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