Q-learning based forecasting early landslide detection in internet of thing wireless sensor network

Abstract

The issue of climate modification and human actions terminates in a chain of hazardous developments, comprehensive of landslides. The traditional approaches of observing the environmental attributes that is actually obtaining rainfall data from places can be cruel and suppressing supervising necessitated for careful infliction. Thus, landslide forecasting and early notice is a significant application via wireless sensor networks (WSN) to reduce loss of life and property. Because of the heavy preparation of sensors in landslide prostrate regions, clustering is a resourceful method to minimize unnecessary transmission. In this article we introduce Q-learning based forecasting early landslide detection (Q-LFD) in internet of things (IoT) WSN. The Q-LFD mechanism utilizes a dingo optimization algorithm (DOA) to choose the best cluster head (CH). Furthermore, the Q-learning algorithm forecast the landslide by soil water capacity, soil layer, soil temperature, Seismic vibrations, and rainfall. Experimental results illustrate the Q-LFD mechanism raises the landslide detection accuracy. In addition, it minimizes the false positive, false negative ratio.