Safety Of Residents Research Articles

A Deep Learning Framework with Learning without Forgetting for Intelligent Surveillance in IoT-enabled Home Environments in Smart Cities

Background: Internet of Things (IoT) technology in smart urban homes has revolutionised sophisticated monitoring. This progress uses interconnected devices and systems to improve security, resource management, and resident safety. Smart cities use technology to improve efficiency, sustainability, and quality. Internet of Things-enabled intelligent monitoring technologies are key to this goal. Objectives: Intelligent monitoring in IoT-enabled homes in smart cities improves security, convenience, and quality of life from advanced technologies. Using live monitoring and risk identification tools to quickly discover and resolve security breaches and suspicious activity to protect citizens. Intelligent devices allow homeowners to remotely control lighting, security locks, and surveillance cameras. Using advanced technologies to regulate heating, cooling, and lighting based on occupancy and usage. Method: This study introduces a deep learning architecture that uses LwF (Learning without Forgetting) to keep patterns while absorbing new data. The authors use IoT devices to collect and analyse data in real-time for monitoring and surveillance. They use sophisticated data preprocessing to handle IoT devices' massive data. The authors train the deep learning model with historical and real-time data and cross-validation to ensure resilience. Result: The proposed model has been validated on two different Robloflow datasets of 7382 images. The proposed model gains an accuracy level of 98.27%. The proposed Yolo-LwF model outperforms both the original Yolo and LwF models in terms of detection speed and adaptive learning. Conclusion: By raising the bar for intelligent monitoring solutions in smart cities, the suggested system is ideal for real-time, adaptive surveillance in IoT-enabled households. By embracing adaptability and knowledge retention, authors envision heightened security and safety levels in urban settings.

Nurse-led framework to improve the safety and quality of residential aged care (HIRAID® Aged Care): protocol for a stepped-wedge cluster randomised controlled trial

BackgroundThe health issues experienced by older people can often be severe and complex, and an increasing number are using residential aged care services to meet their care needs. High-quality nursing care is fundamental to the health and safety of aged care residents and is contingent on nurses’ accurate assessment, informed decision-making, and delivery of timely interventions. However, the role of the aged care nurse is often challenging, impeded by factors such as understaffing, high workloads, and a lack of access to clinical infrastructure and resources. When these challenges mount, residents are put at greater risk of adverse outcomes, such as avoidable clinical deterioration and hospital transfers. This study describes the adaptation and implementation of the emergency nursing framework, HIRAID® (History including Infection risk, Red Flags, Assessment, Interventions, Diagnostics, reassessment, communication and plan)—a tool to assist nurses’ assessment, decision-making and care in residential aged care.MethodsThe HIRAID® framework will be adapted for residential aged care using a real-time Delphi and panel of aged care and nursing experts. The co-designed HIRAID® Aged Care framework will be trialled in 23 residential aged care homes in Sydney, Australia, in a modified stepped-wedge cluster randomised controlled trial design. All homes will be randomised into one of four clusters. Outcomes of interest include the rate of clinical deterioration events resulting from nurses’ actions, the rate of hospital transfers determined to be inappropriate, performance against the national mandatory aged care quality indicators, resident satisfaction with care, nurse and medical staff satisfaction with communication, and the quality of nursing documentation. These outcomes will be evaluated using a combination of qualitative and quantitative analysis of routinely collected resident data, expert assessments of facility documentation events against validated criteria, and pre- and post-intervention surveys of residents, family carers, nurses, and medical staff.DiscussionThis protocol describes a pragmatic trial that aims to translate an evidence-based framework from the emergency care context into residential aged care. The adapted HIRAID® Aged Care framework will be the first of its kind to standardise and guide holistic nursing assessment, decision-making, and documentation in residential aged care in Australia.Ethics and disseminationThis research has been approved by the Western Sydney Local Health District Human Research Ethics Committee: 2023/ETH00523. A waiver of consent has been approved to access resident health data and nursing documentation at each participating site.Trial registrationAustralian and New Zealand Clinical Trial Registry, ACTRN12623000481673. Registered 12 May 2023.Protocol versionVersion 1.0 6 February 2024.

Surgical Tracheostomies: A Retrospective Chart Analysis Comparing ENT Specialists and Residents.

The primary aim of our retrospective chart review is to compare open surgical (OS) tracheostomies performed by specialist and supervised resident surgeons, focusing on surgical time and post-surgical complications. The secondary objective was to compare these data based on the years of surgical experience for both specialists and residents. All patients undergoing surgical tracheostomy at the Unit of Otorhinolaryngology, Head & Neck Department, University of Verona between 1 January 2017, and 31 December 2022, were subjected to a retrospective analysis. A total of 385 OS tracheostomies were included in the study. Procedures conducted by supervised residents across various training years were compared with those performed by junior and senior specialists, focusing on surgical duration and postoperative complications. Subsequently, these data were stratified based on the surgical experience of subgroups: residents were categorized into 4 years of training, and specialists were categorized into junior (with less than 5 years of experience) and senior (with at least 5 years). Statistical analysis concerning patients' anatomy revealed a significant increase in operative times among those with no palpable neck landmarks (p = 0.006) and those with previous neck surgery (p = 0.039). Among patients' comorbidities, only anticoagulant or antiaggregant therapy was found to prolong operative time (p = 0.018), while the ASA score did not correlate with the duration of surgery. Finally, no statistically significant differences in surgical time were observed (p = 0.287), and no significant differences in complication frequency were reported between supervised residents and specialists (p = 0.908), regardless of years of experience. Under appropriate supervision, OS tracheostomy has demonstrated safety for residents as early as their first year of residency. Residents can perform tracheostomies without significantly prolonging the duration of the operation or increasing the risk of peri- and post-operative complications.

Improvement of water quality through coordinated multi-trophic level biomanipulations: Application to a subtropical emergency water supply lake

Artificial emergency water source lakes have been built in most cities in the middle and lower reaches of the Yangtze River, China, to ensure water safety for residents. However, these new ecosystems are prone to algal blooms or other degraded water quality conditions. A newly built water supply lake in the lower reaches of the Yangtze River was selected as a model system to test whether the coordinated manipulation of fish and submerged macrophyte communities could enhance ecosystem function and quality. The coordinated manipulations spanned a five-year period, aiming to enhance both top-down and bottom-up control of phytoplankton. As a result of these manipulations, the catch per unit effort of small-bodied zooplanktivorous fishes decreased by >95 % from year two and remained low. The coverage and biomass of submerged macrophytes increased year by year. Water transparency increased from 1.07 to 3.33 m. Total phosphorus and total nitrogen showed a decreasing trend (not significant though). The annual mean biomass of Cyanophyta, Chlorophyta and Bacillariophyta decreased from 2.99 to 0.03 mg/L, 3.90 to 0.16 mg/L, and 3.50 to 0.3 mg/L, respectively. The biomass of phytoplankton in different groups decreased in all four seasons. The annual mean biomass of Cladocera and Copepoda remained low. The biomass of Cladocera and Copepoda decreased in summer, fall, and winter. The Ecosystem Health Index - increased from 15.9 to 32.0. The pros and cons of the various top-down and bottom-up control measures employed are discussed. This research presents a valuable case study on the enhancement of ecosystem structure and function in newly constructed emergency water supply lakes and offers insights into the restoration of other subtropical shallow lakes.

Movable VOC Removal System

In this study, we develop a mobile VOCs gas removal system that can remove volatile organic compound (VOCs) emissions from petrochemical facilities, metal painting factories, and surroundings of life, and can efficiently cope with situations occurring in the field. Regular repairs to petrochemical plants and semiconductor plants are carried out for a month every four to five years, and work such as replacing old pipes and repairing aging facilities, cleaning, removing residual gas, checking pipe connections and valves, and improving processes are carried out while the plant is shut down. During regular maintenance, a large-scale accident occurs in which gas remaining in the facility leaks or explodes due to rising pressure, and even after internal gas discharge and purge work is performed, it is scattered by the characteristics of raw materials remaining inside the pipe and reactor and discharged into the atmosphere. Therefore, there is always a possibility of causing safety accidents, and such accidents have a great socioeconomic impact as they are directly connected to the safety of workers as well as the safety of local residents. This system is designed to be adaptable according to the size of a number of outlets, and it is proposed to minimize the impact of VOCs harmful gases on the surroundings by developing it as a mobile type. The efficiency of collecting scattered VOCs gas is more than 90%, and the integrated operating system is demonstrated for 50 CMM class movable VOCs adsorption device, 5 CMM class treatment gas recirculation type non-flame waste heat recovery adsorbent regeneration treatment device, and two or more business sites.

Ground subsidence prediction with high precision: a novel spatiotemporal prediction model with Interferometric Synthetic Aperture Radar technology

ABSTRACT As the extraction of mineral resources intensifies, ground subsidence in mining areas has escalated, posing substantial challenges to sustainable development and operational safety. This subsidence, resulting directly from mining activities, significantly compromises the safety of nearby residents by damaging residential structures and infrastructure. Thus, developing precise and dependable methods for predicting ground subsidence is crucial. This study introduces an innovative Cabs-Unet model, which enhances the U-Net architecture by integrating a Convolutional Block Attention Module (CBAM) and Depthwise Separable Convolutions (DSC). This model aims to predict the spatiotemporal dynamics of the Interferometric Synthetic Aperture Radar (InSAR) time series. Employing Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS InSAR) technology, we gathered and validated data on ground subsidence at the Pengzhuang coal mine from May 2017 to November 2021, covering 130 scenes, with its accuracy corroborated by levelling survey results. An empirical evaluation of the Cabs-Unet model in two distinct subsidence zones demonstrated superior performance over conventional methods like Convolutional Long Short-Term Memory (ConvLSTM) and Predictive Recurrent Neural Network (PredRNN), with Root Mean Square Error (RMSE) values of 1.44 and 1.70, respectively. These findings highlight the model’s efficacy in accurately predicting spatiotemporal InSAR ground subsidence. Further predictive analysis using InSAR data indicated an expected increase in subsidence, projecting cumulative declines of −457 mm in Area A and −1278 mm in Area B by 17 July 2022. Our model proves effective in assessing subsidence, promptly detecting potential risks and facilitating the rapid implementation of risk mitigation strategies.

Developing a Public Health Course to Train Undergraduate Student Health Messengers to Address Vaccine Hesitancy in an American Indian Community.

The purpose of the Diné Teachings and Public Health Students Informing Peers and Relatives about Vaccine Education (RAVE) project was to develop strategies for health communication that addressed COVID-19 vaccine safety for residents of the Navajo Nation. The RAVE project developed a 16-week course using the Diné Educational Philosophy as a framework to train Diné College (DC) public health undergraduate students (n = 16) as health messengers to share COVID-19 vaccine safety information with unvaccinated peers and relatives. An online community survey (n = 50) was used to assess DC community vaccination perceptions to guide course development. The two primary reasons survey participants got vaccinated were to protect the health of others [82% (n = 41)] and to protect their own health [76% (n = 38)]. A pretest/post-test and a retrospective pretest (n = 13) were implemented to determine course effectiveness. A finding approaching significance was related to student confidence in being health messengers (9.1% increase). RAVE offers the first example in the published literature of successfully training American Indian undergraduate students in the context of a public health course to contribute to the response workforce during a public health crisis.

Turning Waste into Treasure: Utilizing Environment-Pollutant Graphite Tailings as Photocatalyst for Photocatalytic Building Materials

Graphite tailings, as solid waste residuals from graphite extraction and refinement, pose serious risks to the surrounding ecological environment and the safety of residents’ lives. However, it is desirable but challenging how to rationalize the utilization of graphite tailings and further to develop their functionality. Herein, we demonstrate a "turning waste into treasure" strategy: capitalizing on the characteristics of graphite tailings in visible light absorption and rich metal catalytic sites, graphite tailings are employed as photocatalysts and fine aggregates to manufacture photocatalytic building materials, achieving sustainable use. Tailings powder from Luobei, Heilongjiang Province is selected as the photocatalyst, we discover that it exhibits photocatalytic degradation capability for methylene blue (MB) in water under visible-light irradiation: the degradation rates reach 70% within one hour and near-complete degradation within two hours, accompanying good recyclability. Structural analysis reveals that the graphite tailings are enriched with Fe2O3 and TiO2. They are capable of forming type-II heterojunction that enable efficient charge separation and transfer processes, transmitting photogenerated electrons to the active catalytic site to accomplish the photocatalytic degradation of MB. Moreover, other tailings, such as those from Jixi, also achieve photocatalytic degradation of MB, and cement specimens made with graphite tailings also bespoke photocatalytic degradation performance. This work is to provide solutions for the reuse of graphite tailings in the field of functional building materials, and to explore the feasibility of transforming graphite tailings from industrial solid waste to photocatalytic building materials.

PERANCANGAN SISTEM MONITORING JARAK JAUH PINTU PINTAR RUMAH INDEKOS BERBASIS IOT (INTERNET OF THINGS) MENGGUNAKAN PLATFORM BLYNK

Security systems are crucial for ensuring the safety of residents, particularly in boarding houses, which tend to be more vulnerable to criminal activities. In this study, a door security system based on the IoT (Internet of Things) was developed using the Blynk platform. The system is designed to allow boarding house owners and residents to monitor door access via a smartphone, enhancing security more effectively and efficiently. The system utilizes a NodeMCU microcontroller as the control center, which is connected to a door sensor, and provides real-time notifications through the Blynk application. Additionally, activity log features and remote control capabilities are integrated into the system. Testing was conducted through simulation scenarios to evaluate the system's response to various security conditions. The results showed that the system operates effectively and provides real-time notifications to users. This system is expected to serve as an alternative solution for improving boarding house security that is easy to implement and use.

Expert perspectives on disaster risk reduction strategies in the tourist area of Borobudur-Yogyakarta-Prambanan in Indonesia

Tourism plays a crucial role in the economic growth of many countries. However, tourist destinations located in disaster-prone areas face increased vulnerability in the event of natural disasters. It is essential for these areas to have specific interventions, plans, and policies that focus on reducing disaster risk, while ensuring the safety of residents and tourists. This study proposes strategies for minimizing disaster risk using Indonesia's Borobudur-Yogyakarta-Prambanan (BYP) region as a case study. The study used an Analytical Hierarchial Process in a Strengths Weaknesses Opportunities and Threats (SWOT-AHP) analysis to prioritize factors through pairwise comparisons and develop strategic alternatives based on expert perspectives. Primary data were collected through aSWOT-AHP questionnaire that was provided to 22 experts in tourism and disaster management in Central Java and Yogyakarta Special Provinces. Secondary data were obtained from government documents, specifically the Disaster Risk Reduction Plan for the BYP regions (2023–2027). The results indicate that “implementing land-use policies to promote tourism development while considering disasters” is the most important strategy, based on expert judgment. The BYP tourist destinations provided a case study to identify strategies to reduce disaster risk in tourism areas.

Effects of essential caregiver policies on COVID-19 and non-COVID-19 deaths in nursing homes.

Federal authorities banned nursing home visitation in the early days of the coronavirus disease 2019 (COVID-19) pandemic. However, there was growing concern that physical isolation may have unintended harms on nursing home residents. Thus, nursing homes and policymakers faced a tradeoff between minimizing COVID-19 outbreaks and limiting the unintended harms. Between June 2020 and January 2021, 17 states implemented Essential Caregiver policies (ECPs) allowing nursing home visitation by designated family members or friends under controlled circumstances. Using the Nursing Home COVID-19 Public File and other relevant data, we analyze the effects of ECPs on deaths among nursing home residents. We exploit variation in the existence of ECPs across states and over time, finding that these policies effectively reduce both non-COVID-19 and COVID-19 deaths, resulting in a decrease in total deaths. These effects are larger for states that implemented policies mandatorily or without restrictions, indicating a dose-response relationship. These policies reduce non-COVID-19 deaths in facilities with higher quality or staffing levels, while reducing COVID-19 deaths in facilities with lower quality or staffing levels. Our findings support the use and expansion of ECPs to balance resident safety and the need for social interaction and informal care during future pandemics.

Research on the Management of Residents in Resettlement Communities in the Yellow River Beach Area: A Case Study of H Community in Jinan City

In recent years, China has attached great importance to the development of the Yellow River coastal areas and formulated many promotion policies for this purpose. The relocation and resettlement of residents in the Yellow River beach area is one of them. The implementation of this measure has enabled the people in the beach area to move out of the Yellow River coast in an orderly manner, ensuring the safety of residents and making their lives more convenient. At the same time, it also promotes the construction of new rural communities. H community is a typical community for relocation in Huanghe Beach District, Jinan City. This study conducted semi-structured interviews with residents of different identities and occupations in the H community, and conducted field research for a period of time to point out the governance status and difficulties of the H community. Based on the research, suggestions were proposed for similar communities and problems.

Influence of Digital Health Technologies among Healthcare Providers on Long-term Care Services Implementation for Elderly with Disabilities

Purpose: The traditional paradigm of home care in China is not up to the challenges posed by the country's increasing population's advancing age and lack of available resources. Digital monitoring technology solutions are thought to be advantageous for managing caregivers' workloads while also improving resident safety and care quality in assisted living facilities. To effectively provide the best possible services, teamwork is crucial in New Hampshire. Due to several issues such staffing shortages, rising resident expectations, and increased demands, NH are consequently embracing the use of Knowledge Management (KM) activities more and more to facilitate knowledge development, storage, transfer, and application. Method: In China, the lack of an established system for caring for the elderly is having a significant impact on society as a whole, making it imperative to establish one. The researcher examined the real impacts of their quality management system and KM efforts on service quality, as well as the KM adjustments made by two China state NH that adopted the E-Qalin management of quality model, in the qualitative research. Additionally, the researchers looked at the approaches used by two China private NH that address service quality from a knowledge management standpoint but have not implemented a certified management of quality program. There were eighty nursing professionals in the sample. Teamwork is crucial in every NH included in this study, and each participant in the researcher’s poll is a member of a team. In this work, the researchers used linear regression to analyse connections between individual variables. Results: Between the development, transmission, and application of knowledge in the state of New Hampshire regardless of the E-Qalin certificate, the researchers discovered a strong and favourable correlation. The researchers found a statistically significant and favourable association between knowledge storage and service quality, but only in New Hampshire with an E-Qalin certificate. Thus, the researcher’s study's findings highlight the necessity of learning more about quality control and knowledge management in the context of New Hampshire. Conclusion: The government, tech companies, and nursing homes need to further restructure the current system before more elderly people adopt sophisticated technology in home care settings. The study's findings, which centre on the transmission of tacit knowledge, substantially advance the field of knowledge management research in New Hampshire.

Application of Indoor Positioning Systems in Nursing Homes: Enhancing Resident Safety and Staff Efficiency.

Providing a safe and secure living environment for residents that is supported by a dedicated healthcare team is one of the core values of nursing homes. Nursing homes must protect residents from the risk of going missing, track quarantined residents and visitors to control the spread of infection, and maintain proactive nursing rounds. However, recruiting and retaining qualified caregivers and medical staff has long been a challenge. Therefore, using advanced technology to ensure the safety and security of residents is highly desirable. In this work, we first demonstrate the applicability of indoor tracking applications in a nursing home, such as resident and asset tracking, nursing assistant management, visitor tracking, infection control, and vital-sign monitoring. To monitor the locations of residents and staff, Bluetooth tags were used, providing real-time data for location tracking. We then conduct a series of quantitative analyses to illustrate how indoor tracking data can support the management of nursing homes, including characterizing residents' activities in daily living and assessing the performance and workload of nursing assistants. Finally, we use qualitative research to evaluate the acceptability of an indoor positioning system in the nursing home. The results show that the implemented indoor positioning applications can improve the quality of healthcare and working efficiency, thereby providing a safer and more secure living environment for residents.

Extrapolating low-occurrence strong wind speeds at pedestrian levels using artificial neural networks trained by a single turbulent dataset

Predicting low-occurrence strong wind speeds in urban areas is crucial for enhancing the comfort and ensuring the safety of residents. Artificial neural network (ANN) models outperform traditional statistical methods in accurately estimating low-occurrence wind distributions. However, generalizing trained models to be applicable to various datasets remains a significant challenge. Therefore, three ANN models with different input variables, trained by a single turbulent dataset of a building array case, were applied to four types of datasets to evaluate the models’ generalization ability for predicting low-occurrence strong wind speeds in urban areas. It found that the ANN model, which uses the mean, standard deviation, and skewness of the turbulent flow, shows the best generalization ability among the three ANN models for all types of datasets. In addition, for the four datasets, the results calculated by this model show that the majority of points have an error of less than 20% compared to the true values. It is advisable to construct models using more extensive training data that encompasses a broad range of mean and skewness of wind speeds. This study contributes to the development of more effective and adaptable ANN models for urban wind safety in building engineering.

Analisis Dampak Volume Produksi Home Industri Batik Gambir Tigo Sarumpun dalam Meningkatkan Kesejahteraan Tenaga Kerja Menurut Perspektif Islam

This research is based on the results of the author's observations which target that there are in IKM Batik 3 Serumpun which are trying to increase the safety of its special residents for business actors and Activity power. These rewards and bonuses are usually used by employees to meet their basic needs or their families. The level of fulfillment of these rewards and bonuses will ultimately determine the level of safety of each employee, whether the rewards can meet their needs, or vice versa. This research aims to identify the Impact of Home Batik Gambir Tigo Sarumpun Factory in increasing Safety that is in accordance with its Islamic perspective. This research is a descriptive research with a Qualitative approach. The method of collecting information used through monitoring, question and answer and selection. The method of analyzing information uses data collection, data reduction, data presentation and drawing conclusions. And the data in this research are residents who work at Home Pabrik batik Gambir togo sarumpun. The results of this research are the income of employees at Home Pabrik Batik Gambir 3 Serumpun Suka is very useful for employee safety. Sufficient income has an impact on the venting of the desires of informants (residents) or their families to continue to be fulfilled, both basic and inferior desires, as a result the safety of residents or families is assumed to continue to be good. Based on the perspective of Islamic economics, the attempted reward system is in accordance with Islamic law which suggests that the rewards received by workers are in accordance with the power that has been given.

Urban traffic tiny object detection via attention and multi-scale feature driven in UAV-vision

The unmanned aerial vehicle (UAV) city patrol is of great significance in ensuring the safety of residents’ lives and properties, as well as maintaining the normal operation of the city. However, the detection of UAV images faces challenges such as numerous small-scale objects, complex backgrounds, and high requirements for detection speed. In response to these issues, we introduce a Real-time Small Object Detection network in UAV-vision (RTS-Net), tailored for UAV patrols. Initially, we introduce a multiscale feature fusion module (MFFM) designed to augment the expressiveness of features across scales, thereby enhancing the detection of smaller objects. Subsequently, leveraging attention mechanisms, we present the coordinated attention detection module (CADM), which bolsters the detection model’s ability to accurately segregate objects from the background in expansive, complex scenarios. Lastly, a lightweight real-time feature extraction module (RFEM) is crafted to diminish model computational complexity and boost inference speed. On the UAV road patrol image dataset we constructed, our proposed method attains a detection accuracy of 89.9%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} mAP, breaking previous records. It surpasses all prevailing detection methods, particularly for small-scale objects. Simultaneously, it achieves an inference speed of 163.9 FPS. The experimental results show that RTS-Net can satisfy the accurate and efficient detection of ground objects by various different UAV platforms in different complex scenarios.

Integrating Internet of Things and Big Data as an Approach for Enhancing the Quality of Life in Egyptian Cities

Abstract In modern times, the concept of quality of life (QoL) has been a focal point in numerous studies, offering solutions to challenges faced by residents in new cities worldwide, including Egypt. To ensure citizens enjoy a high quality of life, cities are increasingly leveraging innovative technologies to address various aspects such as the environment, physical health, mobility, social interaction, psychological well-being, and the economy. Among these technologies, the Internet of Things (IoT) plays a significant role in enhancing people’s lives. By utilizing IoT as an information technology tool, cities can tackle their unique challenges. Consequently, the IoT method and the information obtained through Big Data (BD) analysis will enhance cities sustainability, safety, and liveability for residents. This research aims to explore the integration of IoT and BD for enhancing the quality of life in Egyptian cities. A qualitative methodology is employed to achieve this goal. Initially, a comprehensive literature review is conducted to uncover the relationship between improving quality of life in Egyptian cities and the use of IoT and BD methods. Additionally, a case study of Busan city, which has successfully implemented several IoT technologies to enhance the well-being of its inhabitants, is presented and analyzed. The findings from both the literature review and the case study highlight the positive correlation between the adoption of IoT and BD technologies and the overall quality of life in cities, spanning dimensions such as transportation, economy, social aspects, and the environment.

Evaluation of geological hazard susceptibility in Xixia Xichuan area based on support vector machine

Abstract Geological hazards seriously affect the personal and property safety of regional residents, in the Xichuan area, a real-time monitoring and early warning system could have provided timely alerts before the landslide, potentially averting the tragic loss of life and destruction.So it is very important to carry out geological hazard susceptibility assessment and hazard prevention. The Xixia area of Xichuan is selected as the study area, and the support vector machine model is used to evaluate the susceptibility of geological hazards in the study area. Through Kendall factor correlation analysis, the susceptibility evaluation indexes of the study area are selected, the evaluation index system of the study area is established, and the thematic layers and attribute data of the evaluation indexes are extracted through GIS technology. The MATLAB platform was used to establish linear kernel function (LN-SVM), polynomial kernel function (PL-SVM), radial basis kernel function (RBF-SVM), sigmoid kernel function (Sigmoid-SVM) based on the four support vector machine models of the study area were used to evaluate and classify the susceptibility of geological hazards. ROC curves were plotted to check the accuracy of the evaluation results of the four kernel functions, and the most suitable kernel function model for the study area was selected. The results show that the RBF-SVM model has high evaluation accuracy and is most suitable for the evaluation of geological hazard susceptibility in the study area. Finally, GIS technology was applied to partition the evaluation results into susceptibility zones. The evaluation results show that 86.14% of the geological hazards in Xixia Xichuan area are of medium and high susceptibility, and the evaluation results have certain practical significance for the prevention and control of geological hazards in Xixia Xichuan area.

Identification and Deformation Characteristics of Active Landslides at Large Hydropower Stations at the Early Impoundment Stage: A Case Study of the Lianghekou Reservoir Area in Sichuan Province, Southwest China

Reservoir impoundment imposes a significant triggering effect on bank landslides. Studying the early identification of landslides and their stability concerning reservoir water levels and rainfall is vital for guaranteeing the safety of residents and infrastructure in reservoir regions. This study proposed a method for establishing a dynamic inventory of active landslides at large hydropower stations using integrated remote sensing techniques, demonstrated at Lianghekou Reservoir. We employed interferometric stacking synthetic aperture radar (stacking-InSAR) technology, small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology, and optical satellite images to identify and catalogue active landslides. Moreover, we conducted field investigations to examine the deformation characteristics of landslides. Finally, Pearson’s correlation analysis was employed to evaluate the response between deformation values, reservoir water levels, and rainfall. The results revealed 75 active landslides, including 12 long-term active landslides before impoundment and 63 new landslides after impoundment, which were primarily concentrated in the Waduo and Yazho–Zatou regions. The correlation coefficient between landslide deformation values and the reservoir level was high (0.93), while the correlation coefficient with rainfall was low (0.57). The results of this research offer a crucial foundation for preventing and mitigating landslides in reservoir areas.