ADAPTIVE PARTICLE FILTER (APF) BY USING NOISE AND SAMPLE SIZE FOR MOBILE FLOOD LEVEL PREDICTION - Wan Mohd Yaakob Wan Bejuri; Abdul Karim Mohamad (Journal of Islamic, Social, Economics and Development, 2022)
Urban flooding causing annual deaths and financial losses. Various technique hasbeen applied to estimate the flood level. One of the low-cost solutions, which is using an initialsensor in the smartphone, can be used to predict the flood level, and respond to the warningmessage to the community by sending it to the cloud. However, in the challenging situationduring flood especially low internet connectivity, the processed particle data might be sufferedfrom the sample impoverishment phenomenon, caused by sensor sensitivity and real-timetransmission, which is lead to inaccuracy of flood level prediction. In this paper, a new particlefilter will be designed and implemented in the mobile flood level prediction system by adaptingnoise and sample size from particle data input. This is for optimizing the data input, thus, canprovide better flood level prediction and finally can share the alert information with otherusers. To accomplish this, numerous volunteers will take mobile sensor readings as more than10000 readings in different depth levels, then, can be used to train machine learning models.Simulation results show that proposed particle filter achieved better performance comparedwith other variant when applied the flood level prediction system. In conclusion, the adaptiveproposed.