A Watershed-Based Debris Flow Early Warning System Using Sensor Web Enabling Techniques in Heterogeneous Environments

Abstract

Debris flow disasters have increased in Taiwan due to various environmental factors. These disasters often bring a lot of rock and mud, causing a threat to the lives and properties of residents in the affected areas. The weather is changeable due to more and more extreme rainfall events. A monitoring system is needed to provide early-warning of debris flow disasters to reduce the loss of life and property. The number of installed precipitation stations is not adequate for the current early-warning system. Rainfall patterns are greatly affected by the variation in the topography. Therefore, the current system cannot fully integrate basin-wide rainfall data and lacks information on spatial dependency between rainfall stations. This paper proposes a watershed-based debris flow early-warning system that applies the OGC SWE standards to design its architecture. The standardized data exchange mechanism is used to integrate and share heterogeneous monitoring resources. A hierarchical architecture is proposed to build a wide range of precipitation stations. The system presents high density debris-flow-prone area monitoring. We propose dependency aggregation and SWE integration schemes that enable the system to collect data from upriver under dependency relationship of debrisflow-prone streams and achieve automated early-warning of debris flows. We use the SWE open source provided by 52North to implement the proposed watershed-based debris flow early-warning system. We develop a simulator using real rainfall data in Taiwan to compare to the current system. The experimental results demonstrate that our system can improve the sensing data problem and efficiently advance the warnings issue time.

A Remote Sensing-Based Approach for Debris-Flow Susceptibility Assessment Using Artificial Neural Networks and Logistic Regression Modeling

“…The variables that determine the debris-flow hazard of an area can be grouped into two general categories: 1) the intrinsic or static variables that contribute to debris-flow susceptibility, such as geology, slope gradient, slope aspect, longterm drainage patterns, elevation, and material cohesion; and 2) the dynamic or extrinsic variables that tend to trigger debris flows in an area of given susceptibility, such as heavy rainfall events and earthquakes [3], [5], [12], [50]. In this study, we are carrying on a debris-flow susceptibility investigation that is by definition the spatial analysis of the intrinsic factors; it expresses the likelihood of debris-flow occurrences at each spatial element [21].…”

Tracking Pandemics: A MEC-Enabled IoT Ecosystem with Learning Capability

“…In fact, the SensorThings API is specifically designed for resource-constrained IoT devices. These standardized interfaces permit the proliferation of new high-value services with decreased development overhead, wider reach, ease of sharing, and lower cost for sensor and gateway providers [2][3][4].…”

Design of Remote Acquisition System for Debris Flow Data Based on STM32

“…The research on debris flow disasters began in the middle of the 20th Century, but the factors of debris flow occurrence is so complex that its formation mechanism is now still in research by relevant research institutions at home and abroad. Debris flow area monitoring is the major method to study the formation of debris flow, as well as an important measure to prevent debris flow disasters [4][5][6]. Traditional mudslide monitoring usually sets simple monitoring points in the field, and mainly by manual monitoring.…”