Applying neural network for rain forecast with wireless sensor network - Trần Kim Toại; Dương Cao Trọng Nhân; Võ Minh Huân (Journal of Technical Education Science, 2021)
In recent times, machine learning algorithms are widely applied to solve complex nonlinear problems including weather forecasting. With outstanding advantages such as flexibility, high accuracy, variety of applications, data processing with time fluctuations, machine learning algorithms are objective and meet many practical requirements than the previous methods. The research aims to design a rain forecasting system based on artificial neural networks combined with wireless sensor networks. The neural network processes the environmental parameters collected from the sensor network to make the rain event forecast. A neural network model will be built and selected suitable parameters based on prediction errors. Predictive error is the difference between the real value and the forecasted value to assess the quality or suitability of the forecasting model. The performance of the weather forecasting system with the built neural network model will be verified through an experimental process with the amount of data collected from reality. The system can continuously update the environmental parameters in many locations. The database will always be constantly updated with various real-time and diverse parameters as data will be collected at multiple sensor nodes installed in a large network deployment area to added reliability for forecast results. At the same time, the database will also be based on parameters taken from weather history storage websites as the basis for the training set of the system model. In addition, the forecast results of the system will be of upcoming weather events according to the classification model (rain or no rain) instead of weather parameters, so it will be easier for users or systems to make a decision because if the forecast results are weather parameters, we have to analyze these predictive parameters before sending them to the user. From the user side, the automated systems can make decisions based on forecasting the rain event whether to execute tasks such as irrigation or misting and assurance of agro-products in agricultural applications.