Loss Of Life Research Articles

Preparedness and Response to Flooding Risks by Kenya Wildlife Service in Hells Gate National Park, Nakuru County, Kenya

Floods are short-lived events that occur abruptly with little to no warning and are among the most dangerous disasters relating to weather due to the hazardous conditions caused for people, the destruction of property and the loss of life as well. Thus, minimizing flooding impacts timely and passing accurate information is critical in flood preparedness and response. Previous studies have focused on the probability of floods occurring and their magnitude, therefore leaving a knowledge gap on preparedness and response to flooding risks in national parks by government agencies in Kenya. Hell’s Gate National Park experiences significant flash flooding; therefore, it provided a good venue to study the levels of preparation and response mechanisms available in Kenyan national parks. The objectives of the study were to understand the level of preparedness and mechanisms to respond to flooding events by Kenya Wildlife Service in Hells Gate National Park. The study was guided by a descriptive research design which involved collecting quantitative and qualitative data from 118 park users and other key informants. The study found that the Kenya Wildlife Service plays a crucial role in flood preparedness and response, but there is room for improvement in early warning systems, hazard map distribution, and user education because only 36.6% of the respondents were aware of escape routes to be used during floods. In terms of recommendations, the study found that it is crucial to enhance the existing flood preparedness and response mechanisms within the park including investing in advanced technology for early warning systems, conducting regular drills, and fostering community engagement and partnerships, continuous improvement of flood preparedness and response plans, along with a robust communication strategy.

Blast Exposure, Tinnitus, Hearing Loss, and Postdeployment Quality of Life in U.S. Veterans: A Longitudinal Analysis.

Examine the association between military blast exposure and functional status among veterans with a focus on functional disability as a proxy for quality of life and explore the potential modifying effect of hearing loss on this association. Prospective cohort. Multi-institutional tertiary referral centers. 540 veterans. Self-reported military blast exposure with and without tinnitus; high-frequency hearing loss (yes/no). WHO Disability Assessment Schedule 2.0 questionnaires at baseline and annually over 5 years. The odds of membership into three functional disability trajectory groups: low functional disability, moderate functional disability, and high functional disability. Of 540 veterans, 197 (36.5%) self-reported a blast exposure history, and 106 of 197 (53.8%) reported tinnitus as a direct result of the blast. Blast exposure without tinnitus increased the odds of moderate functional disability compared with low functional disability (odds ratio [OR] = 1.5; 95% confidence interval [CI], 0.92-2.51), which strengthened among those with blast with tinnitus (OR, 3.6; 95% CI, 2.1-6.1). Blast exposure without tinnitus also increased the odds of membership to high functional disability versus low functional disability (OR, 2.2; 95% CI, 1.1-4.8). Hearing loss further increased the odds of reporting functional disability. The probability of low functional disability was approximately 60% if there was no history of blast or hearing loss, dropping to 20% if there was blast, tinnitus, and hearing loss history. Blast exposure negatively affects the quality of life of veterans, especially when compounded with tinnitus and hearing loss.

The criminal activities and operational roles of Australian neo-jihadists: a network perspective

ABSTRACT Despite the significant global repercussions of terrorism, including loss of lives and economic resources, research on its relationship with crime has been limited to group-level analyses. This study examines the individual criminal activities of Australian Neo-Jihadists, via social network analysis from an individual perspective. Judicial documents and newspaper articles are analysed to determine the criminal activities and operational roles of network actors. Findings suggest a prominence of criminal activities, and a significant relationship between market criminal activities and facilitation of terrorist operations. The study concludes with suggestions for future research, emphasising the potential for disrupting terrorist networks by focusing on the new crime-terror nexus.

The 2004 Tsunami Impact on Andaman & Nicobar Islands: Consequences and Future Way Forward

The 2004 Indian Ocean tsunami, caused by a powerful undersea earthquake off the coast of Sumatra, resulted in unprecedented destruction across several coastal regions. The Andaman & Nicobar Islands, located near the epicenter, suffered significant loss of life, displacement, and environmental degradation. This paper provides a comprehensive analysis of the tsunami’s impact on the Andaman & Nicobar Islands, detailing the immediate effects, long-term consequences, and rehabilitation efforts. The paper also explores the economic, social, and psychological ramifications of the disaster, particularly on indigenous communities. Looking ahead, it emphasizes the importance of sustainable development, community-based disaster preparedness, and ecosystem restoration to build resilience in the region. The proposed way forward focuses on developing infrastructure that is disaster-resilient, improving disaster management systems, and empowering indigenous communities in rehabilitation efforts to ensure long-term recovery and resilience.

Prevention of Lipid Oxidation in Kilka Fillet (Clupeonella Cultriventris) using Biodegradable Packaging in the Refrigerator (3°C)

Kilka Fish is classified in the fatty fish group and is more perishable than other aquatics. It is very sensitive to oxidation during cold storage. Fat oxidation reduces the quality of Kilka, a decrease in shelf life and economic losses. Because it leads to color change (yellow) of Kilka. Therefore, the present study aimed to evaluate the effects of sodium alginate and whey protein coating on the prevention of fat oxidation of Kilka fillets during 12 days of refrigerated storage. Whey protein 15% and sodium alginate 1% were used at time= 5 min. Uncovered samples were considered as controls. Humidity, peroxide, FFA, TBA, pH, and fat were not significant in the samples covered with whey protein (73.62%, 0.08 meq kg oil-1, 1.01 g 100-1, 0.06 mg kg-1, 6.37 and 4.45%) compared with those covered by sodium alginate (73.91%, 0.05 meq kgoil-1, 1.09 g 100-1, 0.01 mg kg-1, 6.49, and 4.00%) (P>0.05). However, the index chemicals were lower in these samples compared with the control (59.16%, 3.07 meq kgoil-1, 5.73 g 100-1, 2.14 mg kg-1, 6.81 and 3.99%. Unlike fat, the other chemical tests were significant between control and covered samples (p<0.05). Covered samples had good quality at the end of the storage period. However, the control samples lost their quality after 6 days. Fish soaked with sodium alginate had better quality compared with the other samples. The use of sodium alginate is recommended by the seafood processing industry to maintain the quality of kilka during refrigeration.

Effects of Combined Visual-Motor Response Training on Cognitive Function and Brain Plasticity Mechanisms in Various Populations: Protocol for a Single-Center, Open-Label, Controlled Clinical Trial.

Cognitive impairment is one of the major diseases facing the aging population. The progressive decline of cognitive function can lead to declining health or even the loss of life, work, and social ability. Exercise and behavioral stimulation can increase neurotransmitters in the brain and improve overall health and cognitive function. Reactivity training can mobilize neuromuscular function and induce changes in brain plasticity, which may effectively improve cognitive dysfunction and delay the occurrence and development of Alzheimer disease; however, the evidence supporting its effectiveness is still limited. This study aims to explore the effectiveness and reliability of visual-motor reaction training in improving cognitive function, thereby promoting the application of novel nonpharmacological therapies. This study is a single-center, open-label, controlled clinical trial. A total of 78 participants will be recruited for the study, including an equal number of athletes, ordinary healthy college students, and ordinary older adults in the community. Participants will receive 2 weeks of visual-motor response training. The primary outcome of this study is to assess differences in functional magnetic resonance imaging (fMRI) at 2 weeks. The secondary outcomes were the following: acousto-optic response time, Hamilton Depression Rating Scale (HAM-D), Hamilton Anxiety Rating Scale (HAM-A), Mini Mental State Examination (MMSE), Activity of Daily Living (ADL) Scale, Subjective Cognitive Decline Questionnaire-9 (SCD-Q9), a 10-word memory test, and safety. The study was approved by the Shanghai Clinical Research Ethics Committee on January 2, 2024 (SECCR/2023-162-01). As of September 11, 2024, we have completed the recruitment of all 3 groups of volunteers. We expect to complete data collection and analysis by February 2025. The purpose of this study is to compare improvements in brain perceptual motor functions and cognitive levels across different populations through response ability training and to explore the efficacy and safety of exercise-based nonpharmacological therapies in improving cognitive function. Other potential benefits include understanding the functional differences and perceptual characteristics of the brain's perceptual-motor system between athletes and the general population and exploring the adaptability of the brain in acquiring skills during competitive sports training. This could provide an evidence base for early sports talent development and broader youth development. Chinese Clinical Trial Registry ChiCTR2400079602; https://tinyurl.com/23fbbndw. DERR1-10.2196/56424.

Fine spatio-temporal prediction of fishing time using big data

Overfishing, bycatch, and other anthropogenic threats may lead to the destruction of fragile habitats and substantial losses of marine life. Marine fishery resources can be protected by adjusting fishing intensity and establishing marine reserves. Currently, China adopts the closed fishing season management approach to protect traditional fishing grounds, where fine spatio-temporal prediction is essential to efficiently supervise the wide scope. Fishing vessel behaviors reflect fishers’ experience as well as the information provided by fish detection radar, while the fishery resource distribution is relevant to the marine environment. In this study, we identified fishing vessel behaviors (gillnets, trawls, purse seines, and abnormal behaviors) and qualitatively assessed and predicted fishing time of different fishing vessel behaviors to search for high intensity fishing operation areas by constructing a time-space prediction model. The model was based on big data of fishing vessel automatic identification systems and3 the marine environment, and was verified in the East China Sea. The prediction results generally corresponded with the distribution of traditional fishery resources in the East China Sea and the fishing efforts provided by the Global Fishing Watch. This model can provide an accurate and effective refined fishing vessel operation time prediction, and benefits fishing management and fishery resources protection.

Critical Analysis on Land Grabbing as an Organized Crime

Abstract: Land grabbing has emerged as a critical issue in contemporary global discourse, significantly impacting socio-economic and environmental landscapes. This research examines the phenomenon of land grabbing, defined as the large-scale acquisition of land, often by foreign entities or corporations, which displaces local communities and disrupts traditional land use practices. Utilizing a mixed-methods approach, this study analyses case studies from various regions, including Africa, Asia, and Latin America, to uncover the underlying motivations and implications of land grabbing. The findings reveal that factors such as food security, resource scarcity, and economic development drive land acquisitions, frequently resulting in adverse effects on local populations, including loss of livelihoods and environmental degradation. Additionally, the research highlights the role of government policies and international trade agreements in facilitating land grabbing practices. The study underscores the need for robust legal frameworks and the active involvement of local communities in land governance to mitigate the negative impacts of land grabbing and promote sustainable development. Ultimately, this research contributes to the understanding of land grabbing as a complex interplay of economic interests, social justice, and environmental sustainability.

A Methodological Approach to Improving Extreme Precipitation Reanalysis Data Using the Clausius-Clapeyron Relationship: A Case Study in a Mediterranean City

Climate change is a crucial issue of the 21st century, leading to more frequent and severe extreme precipitation events globally. These events result in significant social and economic disruptions, including flooding, loss of life, and damage to infrastructure. Projections suggest that extreme rainfall will intensify in the latter half of the century, underscoring the need for accurate and timely forecasting. Despite advancements in meteorological and climate models that offer high accuracy for various weather parameters, these models still struggle to detect extreme values, particularly for precipitation. This research examines the sensitivity of extreme precipitation events to temperature, based on the Clausius-Clapeyron relationship, focusing on Thessaloniki, Greece. It also evaluates the effectiveness of reanalysis data in identifying extreme precipitation and explores how rainfall-temperature relationships can enhance prediction accuracy. The findings are vital for improving the estimation of extreme rainfall events and informing the design of flood-resilient infrastructure.

Forecasting strong subsequent events in the Italian territory: a national and regional application for NESTOREv1.0

AbstractThe Italian territory is one of the most seismically active areas in Europe, where Strong Subsequent Events (SSEs), in combination with the strong mainshock effects, can lead to the collapse of already weakened buildings and to further loss of lives. In the last few years, the machine learning-based algorithm NESTORE (Next STrOng Related Earthquake) was proposed and used to forecast clusters in which the mainshock is followed by a SSE of similar magnitude. Recently, a first new version of a MATLAB package based on this algorithm (NESTOREv1.0) has been developed and the code has been further improved. In our analysis, we considered a nationwide and a regional catalogue for Italy to study the seismicity recorded over the last 40 years in two areas covering most of the Italian territory and northeastern Italy, respectively. For both applications, we obtained statistical information about the clusters in terms of duration, productivity and release of seismic moment. We trained NESTOREv1.0 on the clusters occurring approximately in the first 30 years of catalogues and we evaluated its performance on the last 10 years. The results showed that 1 day after the mainshock occurrence the rate of correct SSE forecasting is larger than 85% in both areas, supporting the application of NESTOREv1.0 in the Italian territory. Furthermore, by training the software on the entire period available for the two catalogues, we obtained good results in terms of near-real-time class forecasting for clusters recorded from 2021 onward.

Mild and rapid construction of Ti electrodes for efficient and corrosion-resistant oxidative catalysis at industrial-grade intensity

The development of cost-effective and corrosion-resistant catalytic electrodes for chlorine/oxygen evolution reaction (CER/OER) in large-scale industrial applications is a significant challenge. Herein, the sol–gel method is employed to achieve a uniform coating of ruthenium (Ru) doping copper (Cu) on titanium sheet (Ru + 20 %Cu@Ti), and the highly efficient industrial grade stable Ti dimensional stable anode can be quickly constructed at 723.15 K for 2 h. Cu doping reduces the vacancy formation energy of surface oxygen, promotes additional lattice oxygen vacancy assisted hydrolysis dissociation pathway, improves the selectivity and specific activity of CER at high concentration doping, and reduces the binding energy of OER intermediates (e.g., *OH, *O, and *OOH) at adjacent Ru active sites. The overpotentials require to reach the current density of 10 mA cm−2 for CER and OER were only 365 mV and 232 mV at the conditions of 5.0 M NaCl (pH = 7.0) and 1.0 M KOH + 0.5 M NaCl. More importantly, Ru + 20 %Cu@Ti demonstrates excellent stability, operates continuously for over 340h at industrial current density in neutral and alkaline electrolytes, and its strengthening life reaches 64 h, with ultra-low performance attenuation. Impressively, the designed applied electrode (8.0 cm ✕ 15.0 cm) achieves long-term CER at 0.2–0.3 A cm−2. Further industrial grade evaluation of CER shows that its chlorine extraction polarizability, enhances life and weight loss meet the requirements of industrial applications.

Intrusion detection using metaheuristic optimization within IoT/IIoT systems and software of autonomous vehicles

The integration of IoT systems into automotive vehicles has raised concerns associated with intrusion detection within these systems. Vehicles equipped with a controller area network (CAN) control several systems within a vehicle where disruptions in function can lead to significant malfunctions, injuries, and even loss of life. Detecting disruption is a primary concern as vehicles move to higher degrees of autonomy and the possibility of self-driving is explored. Tackling cyber-security challenges within CAN is essential to improve vehicle and road safety. Standard differences between different manufacturers make the implementation of a discreet system difficult; therefore, data-driven techniques are needed to tackle the ever-evolving landscape of cyber security within the automotive field. This paper examines the possibility of using machine learning classifiers to identify cyber assaults in CAN systems. To achieve applicability, we cover two classifiers: extreme gradient boost and K-nearest neighbor algorithms. However, as their performance hinges on proper parameter selection, a modified metaheuristic optimizer is introduced as well to tackle parameter optimization. The proposed approach is tested on a publicly available dataset with the best-performing models exceeding 89% accuracy. Optimizer outcomes have undergone rigorous statistical analysis, and the best-performing models were subjected to analysis using explainable artificial intelligence techniques to determine feature impacts on the best-performing model.

Security with Wireless Sensor Networks in Smart Grids: A Review

Smart Grids are an area where next-generation technologies, applications, architectures, and approaches are utilized. These grids involve equipping and managing electrical systems with information and communication technologies. Equipping and managing electrical systems with information and communication technologies, developing data-driven solutions, and integrating them with Internet of Things (IoT) applications are among the significant applications of Smart Grids. As dynamic systems, Smart Grids embody symmetrical principles in their utilization of next-generation technologies and approaches. The symmetrical integration of Wireless Sensor Networks (WSNs) and energy harvesting techniques not only enhances the resilience and reliability of Smart Grids but also ensures a balanced and harmonized energy management system. WSNs carry the potential to enhance various aspects of Smart Grids by offering energy efficiency, reliability, and cost-effective solutions. These networks find applications in various domains including power generation, distribution, monitoring, control management, measurement, demand response, pricing, fault detection, and power automation. Smart Grids hold a position among critical infrastructures, and without ensuring their cybersecurity, they can result in national security vulnerabilities, disruption of public order, loss of life, or significant economic damage. Therefore, developing security approaches against cyberattacks in Smart Grids is of paramount importance. This study examines the literature on “Cybersecurity with WSN in Smart Grids,” presenting a systematic review of applications, challenges, and standards. Our goal is to demonstrate how we can enhance cybersecurity in Smart Grids with research collected from various sources. In line with this goal, recommendations for future research in this field are provided, taking into account symmetrical principles.

B-152 Analysis of carbonyl compounds in exhaled breath for detection of COVID-19

Abstract Background COVID-19 pandemic has caused enormous loss of lives and economic disruption. The global crisis has resulted in more than 700 million infections and 7 million deaths worldwide. Rapid screening and detection of highly infectious respiratory diseases is a key for preventing and curbing future epidemic and pandemic. The objective of this work is to develop a novel breath analysis technique for detection of COVID-19 using chemoselective capture of carbonyl compounds in exhaled breath for analysis by gas chromatography-mass spectrometry (GC-MS). Respiratory viral infection causes oxidative stress and inflammation in its host body which leads to production of higher levels of some carbonyl compounds through oxidation of lipids and could be detected in exhaled breath. This breath analysis technique can be rapidly adapted for screening infectious respiratory diseases for future epidemic and potential pandemic. Methods A total of 237 subjects were enrolled in this study. Of these, 142 were COVID-19 positive and 95 were negative. One liter exhaled breath in Tedlar bags from both COVID-19 positive and negative subjects confirmed by reverse transcriptase polymerase chain reaction between April 2022 and September 2023. The collected one liter breath samples were completely evacuated through a cartridge packed with silica particles in 2-3 minutes. Silica particles were coated with a reactive aminooxy agent for capture of carbonyl compounds in the breath samples through oximation reactions. The captured carbonyl compounds were eluted by methanol and the eluted solutions were analyzed by GC-MS for calculation of each compound concentration in the exhaled breath samples. Biostatistics was used to identify significant carbonyl compounds (defined as p value < 0.05) as markers of CPOVID-19 infection. A machine learning algorithm was implemented with the captured carbonyl compounds as input features for differentiation of COVID-19 positive from negative subjects. Results A total of more than 30 carbonyl compounds were detected in exhaled breath samples of all subjects. 10 features including 3 individual carbonyl compounds and 7 compound ratios show significant differences (p value <0.05) between COVID-19 positive and negative groups. The developed machine learning and artificial intelligence (AI) algorithms successfully separated COVOD-19 positive from negative with an accuracy of 95.4%, sensitivity of 96.5%, specificity of 93.7%, and AUROC=0.974. Conclusions The analysis of carbonyl compounds in exhaled breath by GC-MS has great potential for non-invasive detection of COVID-19. The method could be rapidly adapted for detection of other respiratory infectious diseases with a small sample size and data set for training of the machine learning and AI algorithms.

Study of Natural Disaster in Manali Valley (Himachal Pradesh), India on 09 July 2023

Continuous heavy rainfall in July 2023, particularly from the 7th to the 10th, led to devastating flash floods, cloudbursts, and landslides in Himachal Pradesh, causing extensive damage to infrastructure, properties, and loss of lives. The most severely affected districts were Kullu, Mandi, Sirmaur, Shimla, Solan, and nearby areas. Manali sub-division, located in Himachal Pradesh, spans an altitude range of 1074 to 4017 meters above sea level. The region's climate is shaped by its mountainous terrain, with the Beas Valley's weather influenced by factors like relief, aspect, and altitude. The Pir Panjal Range's windward slopes create a barrier to monsoon winds, resulting in heavy rainfall and cloudbursts in the area. The report from the India Meteorological Department (IMD) in Shimla, Himachal Pradesh, on July 12, 2023, highlighted the active monsoon conditions in the state from July 7th to 10th, exacerbated by a Western Disturbance (WD). This combination led to intense and unprecedented rainfall, causing extensive damage to public and private properties, particularly in hilly regions. The report mentioned that similar disasters have occurred in the past due to heavy rainfall, cloudbursts, and landslides, possibly exacerbated by factors like unscientific construction, climate change, and increased human activities, including tourism. The report emphasized the need for accurate predictions and proactive planning to mitigate such disasters in the future.

Gaining Insight: Data Needs for Firearm Injury Prevention Researchers

ABSTRACTBetween 1996 and 2018, there was little federally funded research on gun violence, even though it remains a perennial issue, resulting in considerable loss of life. Advocacy and academic groups piece together data to conduct their research, but the U.S. firearms data infrastructure is limited. A current National Institute of Health (NIH)‐funded coordinating center led by researchers at the University of Michigan aims to help confront that problem. This Quality Improvement (QI) study seeks to uncover the information needs of researchers in firearm injury prevention and community‐based interventions through interviews with public health, medical, and criminal justice researchers. Interviews reveal problems associated with existing databases and infrastructures, as well as what researchers need to support their research. We find fragmented data resources and a lag in public health data availability create obstacles to obtaining many datasets. The researchers also describe problems and opportunities for data harmonization across intervention‐focused studies.

Exploiting the synergy of SARIMA and XGBoost for spatiotemporal earthquake time series forecasting

AbstractEarthquakes are vibrations that occur on the surface of earth, generating fires, ground shaking, tsunamis, landslides and cracks. These incidents can cause severe damage and loss of life. Accurate earthquake forecasts are critical for anticipating and mitigating these hazards, which can avoid damage to buildings and infrastructure and save lives. To address the challenges given by earthquakes probabilistic nature, this paper presents a hybrid SARIMA–XGBoost approach to earthquake magnitude prediction. The suggested technique consists of a two‐step process: an exploration phase that uses exploratory data analysis, which includes descriptive statistics and data visualisation, and a prediction phase that focusses on forecasting future earthquakes. Using a large significant earthquake dataset spanning 1965–2023, the study intends to gain insights and lessons for more effective earthquake prediction methods. Further, in a comparison analysis, the results of SARIMA‐XGBoost model are compared to those of traditional ARIMA and SARIMA models. The results highlight the superior performance of the hybrid SARIMA–XGBoost model, showcasing a mean absolute error (MAE) of 0.038, a mean squared error (MSE) of 0.0040, and a root mean squared error (RMSE) of 0.068. These metrics collectively underscore the model's enhanced accuracy in forecasting earthquake magnitudes. The notably low values of MAE, MSE and RMSE indicate that our hybrid approach significantly improves prediction accuracy compared to alternative models. By integrating SARIMA's time series (TS) analysis with XGBoost's machine learning (ML) capabilities, the hybrid model reduces forecasting errors more effectively, demonstrating its clear advantage in precision.

Automated construction safety reporting system integrating deep learning-based real-time advanced detection and visual question answering

The construction sector is globally acknowledged as one of the most hazardous industries, owing to the vulnerability of its workers to accidents, injuries, and even loss of life. Effective precautionary measures are necessary and ensuring the use of personal protective equipment (PPE) by workers is crucial for protecting them from accidents. Existing deep learning-based PPE detection systems mainly use simple vision-based target detection methods for tasks such as the identification of helmets or vests, and they tend to be task-specific. However, the identification of specific PPE based on respective job types and maintaining detailed safety records, requires further innovative approaches. In this paper, we propose an innovative intelligent system that not only accurately recognizes specific PPE according to the needs of different work types but also automatically generates safety inspection reports and establishes complete safety records, thus providing critical data to support accident investigations. The proposed system integrates a target detection model, visual question answering model, and text-based analysis of the relevant regulations to realize real-time detection of PPE and automatic generation of safety inspection reports. The experimental results show that the proposed YOLOv8n-DCA network strikes a good balance between performance and computational cost—, with a mAP value of 86%. Compared to the original YOLOv8n network, the mAP value is improved by 5.1%, while the model parameters and size are significantly reduced. Further, the visual question answering model exhibited a precision is 95.9. Finally, the automatic generation of safety inspection reports was successfully realized, verifying the feasibility of the developed system. This innovative system promises a comprehensive and efficient PPE management solution for the construction industry to ensure worker safety and provide strong data support.

The impact of a ketogenic diet on weight loss, metabolism, body composition and quality of life

The impact of a ketogenic diet on weight loss, metabolism, body composition and quality of life

Landslides: A Review from the Southern Western Ghats of India

ABSTRACT Landslides are the most unpredictable catastrophic events in mountainous and hilly regions. South Western Ghats one among of the regions in India that have experienced recurring landslides due to increase in pre-and post-monsoon rains. Landslides often cause damage to buildings, roads, natural vegetation, even result in the loss of human lives in South Western Ghats with indirect impact on the socioeconomic status of the country. It is crucial to depict the current trend technologies and their performance in landslide studies, offering valuable insights into mitigation strategies. This article highlights on prevalent techniques for mapping landslide zones, along with major conditional factors and assess the performance of these techniques in landslide susceptibility mapping of the South Western Ghats. Landslide studies of the South Western Ghats reveal 20% of the research work with the Landsat 8 OLI for mapping landslides, 13.3% of the studies using the higher spatial resolutions 2.5m and 10m of Cartosat DEM, the rest of the studies were based on the spatial resolution of 12.5m and 30m from ALOS PALSAR, ASTER and SRTM DEM. About 26% landslide studies have employed quantitative approaches primarily the frequency ratio of bivariate. Subsequently machine learning techniques such as random forest and support vector machine have gained prominence, accounting for 33% landslide studies. The literature depicts precipitation as a significant triggering factor and slope as the major conditional factor in the occurrence of landslides within the South Western Ghats. Prioritized densely populated areas in landslide studies have increased attention to smaller geographical areas and forest regions in the South Western Ghats to protect both inhabitants and ecosystems.