A Machine Learning Approach to Weather Prediction in Wireless Sensor Networks

Weather prediction is the key requirement to save many lives from environmental disasters like landslides, earthquake, flood, forest fire, tsunami etc. Disaster monitoring and issuing forewarning to people, living in disaster-prone places, can help protect lives. In this paper, the Multiple Linear Regression (MLR) model is proposed for humidity prediction. After exploratory data analysis and outlier treatment, Multiple Linear Regression technique was applied to predict humidity. Intel lab dataset, collected by deploying 54 sensors, to form a wireless sensor network, an advanced networking technology that existed in the frontier of computer networks, is used for solution build. Inputs to the model are various meteorological variables, for predicting weather precisely. The model is evaluated using metrics -Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). From experimentation, the applied method generated results with a minimum error of 11%, hence the model is statistically significant and predictions more reliable than other methods.