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Real-Time Monitoring and Early Warning System for Landslide Preventing in Myanmar
... With the development of computer technology, communication technology, systems science, nonlinear analysis theory, and intelligent technology, landslide early warning and prediction have evolved into a multidisciplinary integrated technology. This has attracted the attention of many researchers aiming to improve the accuracy of early warning and prediction, leading to the emergence of various models, methods, and systems for early warning and prediction [18]. In 2009, Qiang Xu proposed the tangent angle landslide early warning standard based on the Saito model. ...
With the gradual expansion of mining scale in open-pit coal mines, slope safety problems are increasingly diversified and complicated. In order to reduce the potential loss caused by slope sliding and reduce the major threat to the safety of life and property of residents in the mining area, this study selected two mining areas in Xinjiang as cases and focused on the relationship between phase noise and deformation. The study predicts the specific time point of slope sliding by analyzing the dynamic history correlation tangent angle between the two. Firstly, the time series data of the micro-variation monitoring radar are used to obtain the small deformation of the study area by differential InSAR (D-InSAR), and the phase noise is extracted from the radar echo in the sequence data. Then, the volume of the deformation body is calculated by analyzing the small deformation at each time point, and the standard deviation of the phase noise is calculated accordingly. Finally, the sliding time of the deformation body is predicted by combining the tangent angle of the ratio of the volume of the deformation body to the standard deviation of the phase noise. The results show that the maximum deformation rates of the deformation bodies in the studied mining areas reach 10.1 mm/h and 6.65 mm/h, respectively, and the maximum deformation volumes are 2,619,521.74 mm3 and 2,503,794.206 mm3, respectively. The predicted landslide time is earlier than the actual landslide time, which verifies the effectiveness of the proposed method. This prediction method can effectively identify the upcoming sliding events and the characteristics of the slope, provide more accurate and reliable prediction results for the slope monitoring staff, and significantly improve the efficiency of slope monitoring and early warning.
... [71,80] use tide gauge sensors; Refs. [81][82][83] rain gauge sensors; and [79,81] use temperature and humidity sensors, for monitoring the landslide and flood early warning systems. ...
Cities, and in particular those in coastal low-lying areas, are becoming increasingly susceptible to climate change, the impact of which is worsened by the tendency for population concentration in these areas. Therefore, comprehensive early warning systems are necessary to minimize harm from extreme climate events on communities. Ideally, such a system would allow all stakeholders to acquire accurate up-to-date information and respond effectively. This paper presents a systematic review that highlights the significance, potential, and future directions of 3D city modelling, early warning systems, and digital twins in the creation of technology for building climate resilience through the effective management of smart cities. In total, 68 papers were identified through the PRISMA approach. A total of 37 case studies were included, among which (n = 10) define the framework for a digital twin technology, (n = 14) involve the design of 3D virtual city models, and (n = 13) entail the generation of early warning alerts using the real-time sensor data. This review concludes that the bidirectional flow of data between a digital model and the real physical environment is an emerging concept for enhancing climate resilience. However, the research is primarily in the phase of theoretical concepts and discussion, and numerous research gaps remain regarding the implementation and use of a bidirectional data flow in a true digital twin. Nonetheless, ongoing innovative research projects are exploring the potential of digital twin technology to address the challenges faced by communities in vulnerable areas, which will hopefully lead to practical solutions for enhancing climate resilience in the near future.
... There are several studies related to monitoring through IoT. Monitoring system based on Raspberry Pi and using Video Camera [7], using SigFox Network [8], as well as simple using google sheet and kodular [9] With some of the considerations above, research related to the design of a prototype soil condition monitoring system based on telegram messenger bots was conducted. This system uses several sensors such as the MPU6050 sensor, the Capacitive Soil Moisture V1.2 sensor, buzzer, and the NodeMcu ESP8266 microcontroller. ...
Balikpapan City has a moderate level of vulnerability to landslides. This is because the city of Balikpapan has the triggering factors and causes of landslides. The trigger factor is a tropical climate with high rainfall of 2,887 mm/year. While the causative factor is that 85% of the Balikpapan City area consists of hilly areas. Therefore, it is necessary to conduct research monitoring soil conditions. This study uses several sensors such as the MPU6050 sensor, Capacitive Soil Moisture V1.2 sensor, buzzer, and the NodeMcu ESP8266 microcontroller. This prototype acts as an early warning system. The buzzer on the prototype will sound if the landslide hazard condition is identified. In addition, this prototype sends data on soil conditions via the Telegram Messenger Bot on the Telegram Messenger application. The results of data collection when the slope of the soil is 10o to 30o, the condition of the soil does not vibrate, and the soil moisture is dry is that there is no landslide. However, when the slope is 40o to 50o and the soil moisture is dry, the result is a landslide. For the data results when the slope is 10-40o, the soil condition does not vibrate, and the soil moisture is moist, there is no landslide. However, at a slope of 50o and moist soil moisture, landslides occur. For data results with a slope of 10o - 50o, the condition of the soil does not vibrate and the moisture of the wet soil is that there is no landslide. For the data results when water is flowed directly using a water hose at a slope of 10o to 30o and the ground does not vibrate is that there is no landslide. However, at a slope of 40o to 50o, the ground does not vibrate and landslides occur.
Disasters often led to economic and human losses. Early predictions corresponding to disaster can allow administration to take preventive and precautionary measures. Landslides are common and uncertain disasters that can occur due to disturbance in normal slope stability. Landslides often accompany earthquakes, rain, or eruptions. This research performed a performance analysis study on famous machine learning (ML) algorithms for an early warning system of landslide using cloud–fog model. Entire framework associated with the analysis approach consists of a sensor, fog, and cloud layer. Data acquisitions employed within the sensor layer collects the data about the soil and land through sensors. Furthermore, pre-processing will be performed at the sensor layer. Pre-processing mechanism removes noise present in the dataset. Fog layer contains a feature reduction mechanism that is used to reduce the size of data to conserve energy of sensors during transmission of data. Furthermore, predictor variables selected within the energy conservation mechanism will be used for exploratory data analysis (EDA). Main characteristics of data will be extracted using EDA. Furthermore, principal component analysis applied at the fog layer analyses the dependencies between the attributes. Dependencies are calculated using correlation. Negatively skewed attributes will be rejected thus dimensionality of the dataset is reduced further. All the gathered prime attributes are stored within the cloud layer. K-means clustering is applied to group the similar entities within the same cluster. This step will reduce the overall execution time of prediction. Formed clusters are fed into auto regressive integrated moving averages (ARIMA) for predictions. Relevant authorities can fetch the result by logging into the cloud. The effectiveness of different ML approaches is proved at different levels using metrics such as classification accuracy and F-score. Results with the ARIMA model are found to be better than without ARIMA by 5–6%.KeywordsFog computingLandslide predictionEnergy efficiencyK-means clusteringPCAARIMA
The threat of landslides often occurs during the rainy season in mountainous and hilly areas. Small displacement from the slope that occurs landslides will move slowly on a millimeter scale. To minimize the impact of material losses and fatalities, a landslide monitoring system was developed using uRAD based on FMCW (Frequency Modulated Continuous Wave) radar with landslide miniatures used in the simulation to prove the concept of the phase-detection method in small displacement detection and the self-designed CPD (Change Point Detection) method. The system designed is able to detect when the movement of the miniature landslide is detected by radar. The result shows that the phase-detection method used is suitable for detecting small displacement of the object and the self-designed CPD method can determine the change point of time is good and accurate, which is CPD time detected above the 3rd second this is in accordance with the experiments carried out at the time of data collection. Based on the results, the proposed system can help to detect landslides in simulations carried out with miniature landslides as an object.KeywordsSmall DisplacementuRAD RadarPhase-DetectionChange Point Detection
The surveillance of home or industrial places through sensors and the prevention of problems via prediction are of vital importance for the safety of these areas. This paper shows how to increase wireless sensor network (WSN) techniques by composing new design methods and improved a low-cost industrial and home safety systems. So as to guarantee and present accurate solutions to the system, not only temperature and humidity sensors but also flame and gas sensors were used in this study. The design of simple hardware circuit allows every user to utilize this wireless home safety system. A notification was used as a method of informing users related to system. The installed Arduino device which was programmed with Android Studio takes received gas, flame, the temperature, and humidity signals from the sensors. In order to pre-monitor the capability of occurrence of a fire, when it detects that the collected data with control levels exceed a predefined threshold it will enable the communication with WIFI network and send the notification alarm message to the mobile users.
Wireless sensor networks are one of the emerging areas which have equipped scientists with the capability of developing real-time monitoring systems. This paper discusses the development of a wireless sensor network(WSN) to detect landslides, which includes the design, development and implementation of a WSN for real time monitoring, the development of the algorithms needed that will enable efficient data collection and data aggregation, and the network requirements of the deployed landslide detection system. The actual deployment of the testbed is in the Idukki district of the Southern state of Kerala, India, a region known for its heavy rainfall, steep slopes, and frequent landslides.
Taiwan faces a serious challenge with an increasing frequency of drought in recent years. Therefore, it is important to utilize the state-of-the-art sensing and communication technologies to monitor and forecast effectively the drought, and then notify the relevant departments for taking preventive measures against this natural disaster. This paper proposed and developed a Drought Forecast and Alert System (DFAS), which is a 4-tier system framework composed of Mobile Users (MUs), Ecology Monitoring Sensors (EMSs), Integrated Service Server (ISS), and Intelligent Drought Decision System (ID2S). DFAS combines the wireless sensor networks, embedded multimedia communications and neural network decision technologies to effectively achieve the forecast and alert of the drought. DFAS analyzes the drought level of the coming 7 th day via the proposed drought forecast model derived from the Back-Propagation Network algorithm. The drought inference factors are the 30-day accumulated rainfall, daily mean temperature, and the soil moisture to improve the accuracy of forecasting drought. These inference factors are detected, collected and transmitted in real-time via the Mote sensors and mobile networks. Once a region with possible drought hazard is identified, DFAS sends altering messages to users' appliances. System implementation results reveal that DFAS provide the drought specialists and users with complete environment sensing data and images. DFAS makes it possible for the relevant personnel to take preventive measures, e.g., the adjustment of agricultural water, for a reduced loss.
The surveillance of home or industrial places through sensors and the prevention of problems via prediction are of vital importance for the safety of these areas. This paper shows how to increase wireless sensor network (WSN) techniques by composing new design methods and improved a low-cost industrial and home safety systems. So as to guarantee and present accurate solutions to the system, not only temperature and humidity sensors but also flame and gas sensors were used in this study. The design of simple hardware circuit allows every user to utilize this wireless home safety system. A notification was used as a method of informing users related to system. The installed Arduino device which was programmed with Android Studio takes received gas, flame, the temperature, and humidity signals from the sensors. In order to pre-monitor the capability of occurrence of a fire, when it detects that the collected data with control levels exceed a predefined threshold it will enable the communication with WIFI network and send the notification alarm message to the mobile users.
Landslides monitoring system Using IoT
- Arun Bhosale
- Pramod Nimbore
- Sachin Shitole
- Onkar Govindwar
Landslides monitoring system Using IoT
- bhosale