Flood is the most common type of natural disaster in sub-Saharan Africa, and causes tremendous damage to life, economy and agriculture. Effective and accurate means of predicting flood and its magnitude are still lacking to a large extent. This study is aimed at designing a concise and efficient model that can determine or predict flood and its magnitude using available data from areas prone to flood events. The study was conducted and data was gathered through interviews, observation and secondary sources. A flood magnitude prediction model that uses artificial neural network feed forward mechanism was developed. The data collected was used to test the model through a simulation process using MATLAB, SIMULINK and SPSS. The simulated rainfall and temperature results for the past years 2012–2019 were very similar to the original values collected from NiMet during the study. An R value 0f 0.8 and above based on the results obtained implied a better prediction with very small error margin. The model predicted both future rainfall runoff and temperature for the twelve months of the years 2020 and 2021. The results show that a high flood index of 140 mm and above will occur in the months of July, August and September in the years 2020 and 2021 within the study area. There is a significant improvement in the predictions from the model when compared to existing models and predictions. With these results, agencies of government responsible for emergency management can begin to plan and take appropriate decisions on how to manage the flood disaster in the years to come if it occurs within the study area.

洪水是撒哈拉以南非洲最常见的自然灾害类型，对生命，经济和农业造成巨大破坏。仍然在很大程度上缺乏有效的，准确的预测洪水及其规模的手段。这项研究旨在设计一个简洁有效的模型，该模型可以使用容易发生洪灾的地区的可用数据来确定或预测洪灾及其规模。进行了研究，并通过访谈，观察和二手资料收集了数据。建立了基于人工神经网络前馈机制的洪水预报模型。收集的数据用于通过使用MATLAB，SIMULINK和SPSS的仿真过程来测试模型。2012-2019年过去几年的模拟降雨和温度结果与研究期间从NiMet收集的原始值非常相似。一个根据获得的结果，R值0f 0.8及更高意味着更好的预测，误差容限很小。该模型预测了2020年和2021年的十二个月的未来降雨径流和温度。结果表明，在2020年和2021年的七月，八月和九月将发生140毫米及以上的高洪水指数。在研究区域内。与现有模型和预测相比，该模型的预测有显着改进。有了这些结果，负责应急管理的政府机构可以开始计划并就在未来几年内发生在研究区域内的洪水灾害如何管理做出适当的决定。