# Web3 DGIS-Based System for Reservoir Landslide Monitoring and Early Warning
## 1 Introduction  
In recent years, landslides, debris flows, and other natural disasters have been attributed to changes in the global climate, heavy localized rainfall, strong tropical windstorms, and other extreme weather situations. In China, official statistics from the Geographical Research Bureau show that in 2014 there were 10,907 natural disasters, causing 349 deaths and 5.41 billion yuan of damage in direct economic loss. Among these events, landslides were the most frequent, comprising -74.5% of the total number of natural disasters, and the most severe in terms of damage. Given the threat to human life and infrastructure, minimizing the impact of landslides remains critical. Solutions that have been proposed include transforming the shapes of landslide-risk slopes, structural supports for soil and rock features in landslide areas, and reducing the effects of groundwater and surface water on slope stability. However, while these approaches could decrease the occurrence of landslides, large-scale control remains difficult owing to limitations from construction, available technologies, and cost. Therefore, the ability to conduct real-time monitoring and disaster early warning is critical. Landslide stability is affected by both internal and external factors, whose mechanisms vary concurrently; therefore, the main approach to real-time monitoring involves monitoring the state of these inducing factors. Monitoring is focused on either slope surface or subsurface conditions (e.g., groundwater levels and physical composition). Global Positioning System (GPS) techniques were first applied to monitor landslide deformation in the 1990s [1; 2; 3; 4], and [PERSON] in Jet Propulsion Laboratory put forward the precise point positioning (PPP) technique, anoptimized GPS technique. The PPP technique has taken a dominating role in landslide monitoring [5; 6; 7]. In the mid-1990s, [PERSON], [PERSON], and others, introduced Time Domain Reflectometry (TDR) techniques into obtaining landslide deformation data by burying coaxial cables in landslide areas and measuring their changing state [8; 9]. In the late 1990s and early 2000s, Interferometric synthetic aperture radar, close range photogrammetry measurements, and other techniques were also applied to surface deformation monitoring [10; 11; 12; 13]. Accompanied with the development of monitoring techniques, the monitoring sensors experienced rapid development, from simple and relatively independent monitoring instruments (e.g., rainfall gauge, wire extensometers) to professional landslide sensors, which can transfer monitoring data to scene sites set around the landslides (e.g., osmometer, inclinometer), and then to the multi-sensor networks with the ability of real-time remote data transmission [14; 15]. The efficiency and accuracy of monitoring sensors have been further improved to provide a sound statistical basis for the development of landslide early warning systems. In addition, modern mathematical methods, including grey system and fuzzy math, have also been gradually applied to landslide early warning endeavors [16; 17; 18; 19]. The development of geographical information systems (GIS), and network and sensor techniques has provided strong support for the integration, management, and analysis of spatial statistical data regarding landslides. By integrating the above techniques with landslide early warning models, real-time early warning of landslides becomes possible [20; 21; 22; 23; 24; 25], as does the design and development of practical monitoring systems [26; 27; 28] and disaster handling decision-making and support systems [29; 30; 31; 32].