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Natural disasters cause enormous damage and losses every year, both economic and in terms of human lives. It is essential to develop systems to predict disasters and to generate and disseminate timely warnings. Recently, technologies such as the Internet of Things solutions have been integrated into alert systems to provide an effective method to gather environmental data and produce alerts. This work reviews the literature regarding Internet of Things solutions in the field of Early Warning for different natural disasters: floods, earthquakes, tsunamis, and landslides. The aim of the paper is to describe the adopted IoT architectures, define the constraints and the requirements of an Early Warning system, and systematically determine which are the most used solutions in the four use cases examined. This review also highlights the main gaps in literature and provides suggestions to satisfy the requirements for each use case based on the articles and solutions reviewed, particularly stressing the advantages of integrating a Fog/Edge layer in the developed IoT architectures.

Landslides are highly destructive geological disasters, often preceded by changes in surface displacement. This study explores the effectiveness of real aperture radar (RAR) in landslide monitoring and early warning. Using the three-stage creep theory, the short-term simple moving average velocity (SMA) and long-term simple moving average velocity (LMA) were used to determine the end point of the constant displacement stage, while the hypothesis test method was used to confirm its starting point. Based on these points, the average velocity was calculated. A five-level warning model and criteria were established by integrating acceleration, velocity change, and tangent angle. Three landslide cases in Northeast China were analyzed to evaluate the dynamic application of this model. The results indicate that: (1) RAR effectively identifies abnormal displacement region; (2) Successful warnings in the three cases validate the effectiveness of the warning model and criteria; (3) The five-level warning model and criteria of RAR demonstrate strong application potential in the dynamic monitoring and early warning of landslide disasters.

This paper summarizes the main flash flood early-warning systems of America, Europe, Japan, and Taiwan China and discusses their advantages and disadvantages. The latest development in flash flood prevention is also presented. China’s flash flood prevention system involves three stages. Herein, the warning methods and achievements in the first two stages are introduced in detail. Based on the worldwide experience of flash flood early-warning systems, the general research idea of the third stage is proposed from the viewpoint of requirements for flash flood prevention and construction progress of the next stage in China. Real-time dynamic warning systems can be applied to the early-warning platform at four levels (central level, provincial level, municipal level, and county level) . Through this, soil moisture, peak flow, and water level can be calculated in real-time using distributed hydrological models, and then flash flood warning indexes can be computed based on defined thresholds of runoff and water level. A compound warning index (CWI) can be applied to regions where rainfall and water level are measured by simple equipment. In this manner, flash-flood-related factors such as rainfall intensity and antecedent and cumulative rainfall depths can be determined using the CWI method. The proposed methodology for the third stage could support flash flood prevention measures in the 13th 5-Year Plan for Economic and Social Development of the People’s Republic of China (2016–2020). The research achievements will serve as a guidance for flash flood monitoring and warning as well as flood warning in medium and small rivers.

Two successive landslides within a month started in October 11, 2018, and dammed twice the Jinsha River at the border between Sichuan Province and Tibet in China. Both events had potential to cause catastrophic flooding that would have disrupted lives of millions and induced significant economic losses. Fortunately, prompt action by local authorities supported by the deployment of a real-time landslide early warning system allowed for quick and safe construction of a spillway to drain the dammed lake. It averted the worst scenario without loss of life and property at least one order of magnitude less to what would have been observed without quick intervention. Particularly, the early warning system was able to predict the second large-scale slope failure 24 h in advance, along with minor rock falls during the spillway construction, avoiding false alerts. This paper presents the main characteristics of both slope collapses and damming processes, and introduces the successful landslide early warning system. Furthermore, we found that the slope endured cumulative creeping displacements of > 40 m in the past decade before the first event. Twenty-five meter displacement occurred in the year immediately before. The deformation was measured by the visual interpretation of multitemporal satellite images, which agrees with the interferometry synthetic aperture radar (InSAR) measurement. If these had been done before the emergency, economic losses could have been reduced further. Therefore, our findings strengthen the case for the deployment of systematic monitoring of potential landslide sites by integrating earth observation methods (i.e., multitemporal satellite or UAV images) and in situ monitoring system as a way to reduce risk. It is expected that this success story can be replicated worldwide, contributing to make our society more resilient to landslide events.

The topic of rainfall thresholds for landslide occurrence was thoroughly investigated, producing abundance of case studies at different scales of analysis and several technical and scientific advances. We reviewed the most recent papers published in scientific journals, highlighting significant advances and critical issues. We collected and grouped all the information on rainfall thresholds into four categories: publication details, geographical distribution and uses, dataset features, threshold definition. In each category, we selected descriptive information to characterize each one of the 115 rainfall threshold published in the last 9 years. The main improvements that stood out from the review are the definition of standard procedures for the identification of rainfall events and for the objective definition of the thresholds. Numerous advances were achieved in the cataloguing of landslides too, which can be defined as one of the most important variables, together with rainfall data, for drawing reliable thresholds. Another focal point of the reviewed articles was the increased definition of thresholds with different exceedance probabilities to be employed for the definition of warning levels in landslide early warning systems. Nevertheless, drawbacks and criticisms can be identified in most part of the recent literature on rainfall thresholds. The main issues concern the validation process, which is seldom carried out, and the very frequent lack of explanations for the rain gauge selection procedure. The paper may be used as a guide to find adequate literature on the most used or the most advanced approaches followed in every step of the procedure for defining reliable rainfall thresholds. Therefore, it constitutes a guideline for future studies and applications, in particular in early warning systems. The paper also aims at addressing the gaps that need to be filled to further enhance the quality of the research products in this field. The contribution of this manuscript could be seen not only as a review of the state of the art, but also an effective method to disseminate the best practices among scientists and stakeholders involved in landslide hazard management.

Flash drought often leads to devastating effects in multiple sectors and presents a unique challenge for drought early warning due to its sudden onset and rapid intensification. Existing drought monitoring and early warning systems are based on various hydrometeorological variables reaching thresholds of unusually low water content. Here, we propose a flash drought early warning approach based on spaceborne measurements of solar-induced chlorophyll fluorescence (SIF), a proxy of photosynthesis that captures plant response to multiple environmental stressors. Instead of negative SIF anomalies, we focus on the subseasonal trajectory of SIF and consider slower-than-usual increase or faster-than-usual decrease of SIF as an early warning for flash drought onset. To quantify the deviation of SIF trajectory from the climatological norm, we adopt existing formulas for a rapid change index (RCI) and apply the RCI analysis to spatially downscaled 8-d SIF data from GOME-2 during 2007–2018. Using two well-known flash drought events identified by the operational US Drought Monitor (in 2012 and 2017), we show that SIF RCI can produce strong predictive signals of flash drought onset with a lead time of 2 wk to 2 mo and can also predict drought recovery with several weeks of lead time. While SIF RCI shows great early warning potential, its magnitude diminishes after drought onset and therefore cannot reflect the current drought intensity. With its long lead time and direct relevance for agriculture, SIF RCI can support a global early warning system for flash drought and is especially useful over regions with sparse hydrometeorological data.

Background Global pandemics have occurred with increasing frequency over the past decade reflecting the sub-optimum operationalization of surveillance systems handling human health data. Despite the wide array of current surveillance methods, their effectiveness varies with multiple factors. Here, we perform a systematic review of the effectiveness of alternative infectious diseases Early Warning Systems (EWSs) with a focus on the surveillance data collection methods, and taking into consideration feasibility in different settings. Methods We searched PubMed and Scopus databases on 21 October 2022. Articles were included if they covered the implementation of an early warning system and evaluated infectious diseases outbreaks that had potential to become pandemics. Of 1669 studies screened, 68 were included in the final sample. We performed quality assessment using an adapted CASP Checklist. Results Of the 68 articles included, 42 articles found EWSs successfully functioned independently as surveillance systems for pandemic-wide infectious diseases outbreaks, and 16 studies reported EWSs to have contributing surveillance features through complementary roles. Chief complaints from emergency departments’ data is an effective EWS but it requires standardized formats across hospitals. Centralized Public Health records-based EWSs facilitate information sharing; however, they rely on clinicians’ reporting of cases. Facilitated reporting by remote health settings and rapid alarm transmission are key advantages of Web-based EWSs. Pharmaceutical sales and laboratory results did not prove solo effectiveness. The EWS design combining surveillance data from both health records and staff was very successful. Also, daily surveillance data notification was the most successful and accepted enhancement strategy especially during mass gathering events. Eventually, in Low Middle Income Countries, working to improve and enhance existing systems was more critical than implementing new Syndromic Surveillance approaches. Conclusions Our study was able to evaluate the effectiveness of Early Warning Systems in different contexts and resource settings based on the EWSs’ method of data collection. There is consistent evidence that EWSs compiling pre-diagnosis data are more proactive to detect outbreaks. However, the fact that Syndromic Surveillance Systems (SSS) are more proactive than diagnostic disease surveillance should not be taken as an effective clue for outbreaks detection.

The health monitoring system of a bridge is an important guarantee for the safe operation of the bridge and has always been a research hotspot in the field of civil engineering. This paper reviews the latest progressions in bridge health monitoring over the past five years. This paper is organized according to the various links of the bridge health monitoring system. Firstly, the literature on monitoring technology is divided into two categories, sensor technology and computer vision technology, for review. Secondly, based on the obtained monitoring data, the data processing methods including preprocessing, noise reduction, and reconstruction are summarized. Then, the technical literature on abnormal data early warning systems is summarized. The recent advances in vibration-based and non-destructive testing-based damage identification methods are reviewed in the next section. Finally, the advantages and disadvantages of the existing research and the future research directions are summarized. This review aims to provide a clear framework and some reliable methods for future research.

We show the feasibility of a real‐time estimation of waveforms and coseismic displacements, within a few centimeters in accuracy, with a stand‐alone dual‐frequency Global Positioning System (GPS) receiver using a so‐called “variometric” approach. The approach is based on time single‐differences of carrier phase observations collected at a high‐rate (1 Hz or more) using a stand‐alone receiver, and on standard GPS broadcast products (orbits and clocks), which are ancillary information routinely available in real time. In the approach, first, the time series of epoch‐by‐epoch displacements are estimated. Then, provided that the collected observations are continuous, they can be summed over the interval (limited to a few minutes) over which an earthquake occurs. Since epoch‐by‐epoch displacements divided by the interval between consecutive epochs are essentially equal to the epoch‐by‐epoch velocities, this is equivalent to saying that we are using the GPS receiver as a velocimeter. Estimation biases, due to the possible mismodeling of various intervening effects (such as multipath, residual clock errors, orbit errors, and atmospheric errors), accumulate over time and display their signature as a trend in coseismic displacements. The trend can be considered linear and easily removed, at least for short intervals. Since the proposed approach (named VADASE (Variometric Approach for Displacements Analysis Stand‐alone Engine)) does not require either additional technological complexity or a centralized data analysis, in principle it can be embedded into GPS receiver firmware, thereby providing a significant contribution to tsunami warning and other hazard assessment systems. After a preliminary test using a simulated example, the effectiveness of this approach was proven using real data. We analyzed the 1 Hz GPS data recorded by the International Global Navigation Satellite Systems Service station BREW during the Denali Fault, Alaska, earthquake (Mw 7.9, 3 November, 2002, 22:12:41 UTC), as well as the 5 Hz data collected by some of the stations of the University NAVSTAR Consortium‐Plate Boundary Observatory network and the California Real Time Network during the Baja California, Mexico, earthquake (Mw 7.2, 4 April, 2010, 22:40:42 UTC). Comparisons of the results obtained using VADASE, as well as other already well‐established approaches, displayed agreement to within a few centimeters. Key Points RT GPS‐based coseismic displacements estimation at few cm accuracy is shown The proposed strategy can be embedded in the GPS receiver firmware It is useful for RT earthquakes source modeling and tsunami early‐warning system

Several natural hazards, including earthquakes, may trigger disasters and the presence of disaster drivers further lead to the massive loss of life and property, every year around the world. The earthquakes are unavoidable, as exact earthquake prediction in terms of date, and time is difficult. However, with the advancement in technology, earthquake early warning (EEW) has emerged as a life-saving guard in many earthquake-prone countries. Unlike other warning systems (where hours of warning are possible), only a few seconds of warning is possible in the EEW system, but this warning may be very helpful in saving human lives by taking the proper action. The concept of EEW relies on using the initial few seconds of information from nearby instruments, performing basic calculations, and issuing the warning to the farther areas. A dense network or enough network coverage is the backbone of an EEW system. Because of insufficient station coverage, the estimated earthquake location is error-prone, which in turn may cause problems for EEW in terms of estimating strong shaking for the affected areas. Seismic instrumentation for EEW has improved significantly in the last few years considering the station coverage, data quality, and related applications. Many countries including the USA, Mexico, Japan, Taiwan, and South Korea have developed EEW systems and are issuing a warning to the public and authorities. Several other countries, namely China, Turkey, Italy, and India are in process of developing and testing the EEW system. This article discusses the challenges and future EEW systems developed around the world along with different parameters used for EEW.Article HighlightsThis article aims to provide a comprehensive review related to the developmentThe explicit emphasis is on the scientific development of EEW parametersThe challenges and future scopes for the effective implementation of EEWS are discussed in terms of the correct location, the magnitude estimation, the region-specific use of ground motion prediction equations, communication technologies, and general public awareness