Backpropagation Algorithm for Predicting Rainfall in Anyigba, Kogi State, Nigeria

Rainfall remains the primary supply of moisture for agricultural activities in Nigeria. Accurate and timely rainfall prediction is also essential for food security, better flood control, water resource management, and the wellbeing of the people. This research proposes a method for rainfall prediction based on metrological data and a machine learning technique. The machine learning technique is a hybrid of Levenberg-Marquardt (LM) back propagation and Artificial Neural Network (ANN) used to construct the rain fall forecasting model. Anyigba, in Dekina Local Government Area, Kogi State, Nigeria was used as a case study. The database from six years (2011-2016) of meteorological parameters made up of air temperature, relative humidity, and pressure were obtained from the Tropospheric Data Acquisition Network (TRODAN) of the Centre for Atmospheric Research, National Space Research and Development Agency (CAR-NASRDA) and used. The rainfall prediction model was trained using part of the data collected. The performance of the model was evaluated using metrics such as precision, recall, F1-score, and confusion matrix. The model achieved an accuracy of 0.88, indicating its robustness and reliability in predicting rainfall patterns. The high accuracy of the model demonstrates its potential application in real-time weather prediction, which can significantly benefit local farmers, water resource managers, and disaster response teams. The study identifies several limitations, including the dependency on the quality and availability of metrological data, and the potential impact of climate change on predictive accuracy. Future research could explore the integration of additional meteorological parameters, the use of ensemble methods, and the adaptation of the model to other regions with similar climatic conditions. This research presents a promising approach to rainfall prediction in Anyigba using the back propagation algorithm, offering a valuable tool for mitigating the adverse effects of unpredictable rainfall and enhancing the decision-making processes in agriculture and water management.