Development of Improved Acoustic Disdrometer Through Utilization of Machine Learning Algorithm

Philippines is a tropical country and every year, the country is experiencing typhoons, thunderstorms and excessive rainfalls because of climate change. Since then, the government continuously provides efforts to mitigate natural disasters through numerously growing researches that are inclined with the meteorological processes happening in our country. There are many researches with regards to the methods of quantifying the amount of rainfall but based on those studies, the acoustic disdrometer is rendered useless because of its inability to classify ambient noise from rain types. With this, the main purpose of this study is to develop an improved acoustic disdrometer by adding a capability in which it will categorize the intensity of the amount of rainfall from ambient noise using machine learning algorithm. The proposed methodology is applied by developing a prototype with four piezoelectric sensors, Arduino microcontroller and ZigBee transmitter. Also, the K-nearest neighbors (KNN) predictive model will be established. The obtained results show that the accuracy of the predictive model is 89.95%.