Article
Engineering, Geological
Ascanio Rosi, Samuele Segoni, Vanessa Canavesi, Antonio Monni, Angela Gallucci, Nicola Casagli
Summary: This study examines the definition and effectiveness of 3D rainfall thresholds in a region in Northern Italy, presenting a new approach to warning systems that can significantly reduce false alarms by considering factors such as average rainfall amount in each alert zone. The results suggest promising perspectives for the development of regional warning systems.
Article
Chemistry, Analytical
Autanan Wannachai, Somrawee Aramkul, Benya Suntaranont, Yuthapong Somchit, Paskorn Champrasert
Summary: Flash floods are common natural disasters caused by excessive rainfall. The research proposes the use of HERO stations in early warning systems to improve data accuracy and adaptability to environmental changes. The network of HERO stations in Thailand has shown improved availability and increased flood preparation time for villagers.
Article
Multidisciplinary Sciences
Rongjin Yang, Xuejie Hao, Long Zhao, Lizeyan Yin, Lu Liu, Xiuhong Li, Qiang Liu
Summary: This paper develops an IPV6-based high-spatial-temporal precision air pollutant monitoring and early warning platform, and verifies the feasibility of the system through data analysis and comparison, providing technical support and decision guidance.
SCIENTIFIC REPORTS
(2022)
Article
Engineering, Geological
Won Young Lee, Seon Ki Park, Hyo Hyun Sung
Summary: This study established criteria for a landslide early warning system (LEWS) using a Bayesian model and optimal thresholds for cumulative event rainfall-duration (ED), improving landslide monitoring and warning efficiency.
Article
Engineering, Geological
Samuele Segoni, Yusuf Serengil, Fatih Aydin
Summary: This article investigates the adequacy of knowledge and technical level available before 2021 to calibrate an effective landslide early warning system (LEWS) in Rize province, Turkey. It proposes a prototypal version of a LEWS based on landslide and rainfall data from 1990 to 2020, and tests its effectiveness using events in 2021. The prototype proves to be an effective tool for managing landslide risk and is expected to be helpful in the future.
Article
Engineering, Environmental
Gladys Rincon, Giobertti Morantes Quintana, Ahilymar Gonzalez, Yudeisy Buitrago, Jean Carlos Gonzalez, Constanza Molina, Benjamin Jones
Summary: This study conducted a sampling campaign in the Sartenejas Valley, Venezuela to collect PM2.5 and monitor meteorological parameters and anthropogenic events. The source appointment of PM was done using elemental composition and morphology, and an early warning system for PM pollution episodes was proposed.
ENVIRONMENTAL GEOCHEMISTRY AND HEALTH
(2022)
Article
Environmental Sciences
Yufei Song, Wen Fan, Ningyu Yu, Yanbo Cao, Chengcheng Jiang, Xiaoqing Chai, Yalin Nan
Summary: This study proposes a new method for calculating the spatiotemporal probability of rainfall-induced landslides based on a Bayesian approach and develops a probabilistic-based early warning model at the regional scale. The results show that the proposed model has higher warning accuracy and economic benefits compared to the conventional model.
Article
Chemistry, Multidisciplinary
Dongxin Bai, Guangyin Lu, Ziqiang Zhu, Xudong Zhu, Chuanyi Tao, Ji Fang
Summary: The data collection in automated landslide monitoring is characterized by large amounts of data, periodic fluctuations, outliers, and different collection intervals. This paper proposes a hybrid early warning method for landslide acceleration based on automated monitoring data, which combines traditional warning methods and critical sliding warning methods based on normalized tangent angle. Experiments show that the proposed method accurately identifies accelerating deformation of landslides with minimal false warnings.
APPLIED SCIENCES-BASEL
(2022)
Article
Engineering, Multidisciplinary
Bilal Saoud, Ibraheem Shayea, Marwan Hadri Azmi, Ayman A. El-Saleh
Summary: In this research, a new routing protocol scheme is proposed to improve the lifetime of wireless sensor networks (WSN). By utilizing the Firefly Algorithm to select cluster heads (CH), the proposed scheme achieves better energy consumption and transmission performance. Experimental results demonstrate that the scheme can effectively enhance the lifetime of WSN.
ALEXANDRIA ENGINEERING JOURNAL
(2023)
Article
Biotechnology & Applied Microbiology
Chao Tang, Fenfang Lei, Jirong Liu, Fengxiang Gong
Summary: The infection rate in the Neonatal Intensive Care Unit (NICU) is high and poses a significant risk to critically ill neonates and premature infants. The current monitoring system is incomplete and lacks early warning capabilities. This study developed an intelligent monitoring system using physiological sensors and wireless network technology to provide real-time monitoring and early warning signals, aiming to reduce the infection rate and improve preventive measures in the NICU.
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
(2023)
Article
Telecommunications
R. Dhanagopal, B. Muthukumar
Summary: Landslides are threatening disasters, especially in hilly regions, involving surface movements like debris flows and rock falls. Predicting and monitoring landslides is crucial for disaster prevention, and an IoT-based approach can improve detection and warning systems effectively.
WIRELESS PERSONAL COMMUNICATIONS
(2021)
Article
Environmental Sciences
Rohan T. Bhowmik, Youn Soo Jung, Juan A. Aguilera, Mary Prunicki, Kari Nadeau
Summary: Wildfires have a significant impact on the environment and human health, and California has experienced a surge in wildfires in recent years. This project developed a multi-modal wildfire prediction and early warning system using a novel spatio-temporal machine learning architecture. By integrating various data sources, including historical wildfires, environmental sensor data, and geological data, the system achieved a high accuracy rate in predicting and classifying wildfires. The system's implementation could save lives and protect the environment by providing early warnings and enabling better preparation.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2023)
Article
Green & Sustainable Science & Technology
Bushra Qayyum, Atiq Ahmed, Ihsan Ullah, Syed Attique Shah
Summary: This research focuses on designing a low-cost early warning system for underdeveloped countries prone to tsunami damage, providing cost-effective solutions. The proposed wireless sensor networking model is optimized in terms of cost, delay, and energy consumption, incorporating the intelligence of marine animals for early tsunami detection.
Article
Computer Science, Information Systems
Haythem Ahmad Bany Salameh, Mohammad Fozi Dhainat, Elhadj Benkhelifa
Summary: This article introduces an integrated end-to-end wireless sensor network system for LPG detection and monitoring, which can operate flexibly in different environments. Through an efficient communication protocol, data exchange is organized, and experimental test data are used to demonstrate the reliability and accuracy of the system.
IEEE SYSTEMS JOURNAL
(2021)
Article
Computer Science, Artificial Intelligence
Samayveer Singh, Manju, Aruna Malik, Pradeep Kumar Singh
Summary: This article introduces an energy-efficient cluster head selection technique that can prolong the lifespan of a heterogeneous wireless sensor network. By considering parameters such as network energy type and node density, this method can effectively elect cluster heads, reduce energy consumption, and avoid overloading the cluster heads. Through simulation and analysis in MATLAB, the superiority of this method's performance is demonstrated.
Article
Geosciences, Multidisciplinary
Qiang Zheng, Yongbo Zhang, Yanrong Li, Zhixiang Zhang, Aijing Wu, Hong Shi
ARABIAN JOURNAL OF GEOSCIENCES
(2019)
Review
Geosciences, Multidisciplinary
Yanrong Li, Wenhui Shi, Adnan Aydin, Mary Antonette Beroya-Eitner, Guohong Gao
EARTH-SCIENCE REVIEWS
(2020)
Review
Geography, Physical
Yanrong Li, Ping Mo
Article
Engineering, Geological
Wenhui Shi, Yanrong Li, Weiwei Zhang, Jin Liu, Shengdi He, Ping Mo, Fanfan Guan
Article
Geosciences, Multidisciplinary
Yanrong Li, Ping Mo, Yongfeng Wang, Tao Zhang, Huawei Zhang
Article
Geography, Physical
Jian Huang, Tristram C. Hales, Runqiu Huang, Nengpan Ju, Qiao Li, Yin Huang
Article
Engineering, Geological
Yanrong Li, Fanfan Guan, He Su, Adnan Aydin, Mary Antonette Beroya-Eitner, Hauke Zachert
GEOTECHNICAL TESTING JOURNAL
(2020)
Article
Geosciences, Multidisciplinary
Yanrong Li, Shengdi He, Jianbing Peng, Qiang Xu, Adnan Aydin, Yongxin Xu
JOURNAL OF ASIAN EARTH SCIENCES
(2020)
Article
Engineering, Geological
Nengpan Ju, Jian Huang, Chaoyang He, T. W. J. Van Asch, Runqiu Huang, Xuanmei Fan, Qiang Xu, Yang Xiao, Jue Wang
ENGINEERING GEOLOGY
(2020)
Article
Geosciences, Multidisciplinary
Huan Huang, Jian Huang, Dandan Liu, Zicheng He
Summary: The study revealed that the public has a good understanding of landslide risks and high level of trust in the capability of landslide countermeasures. People are more likely to trust an EWS than EM. However, there are diverse opinions on the effects of these measures among the public. These findings can guide local policymakers in designing and improving strategies for landslide risk management in specific regions.
INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION
(2021)
Article
Geography, Physical
Weiwei Zhang, Nannan Fan, Yanrong Li, Shengdi He, Dingyi Guo
Summary: The Malan loess is a greyish yellow unstratified sediment that is sensitive to water due to its porous and metastable structure. Results from laboratory tests show that the Malan loess experiences primary and secondary disintegration stages, with the primary stage showing high correlation with dry density, clay mineral content, and CaCO3 content. Additionally, the pore structure of the soil influences the disintegration behavior, with evenly distributed small pores leading to gradual disintegration and large pipes leading to abrupt failure.
EARTH SURFACE PROCESSES AND LANDFORMS
(2022)
Article
Engineering, Geological
Shengdi He, Yanrong Li, Shuai Zhang
Summary: Loess landslides result in heavy casualties and economic losses. This study used acoustic emission monitoring and in situ computed tomography scans to investigate the mechanical behavior of Malan loess. The results showed that Malan loess failed under compression-induced tension in unconfined compression condition.
CANADIAN GEOTECHNICAL JOURNAL
(2023)
Article
Engineering, Geological
Jianfeng Wu, Yanrong Li, Shuai Zhang, Joachim Chris Junior Oualembo Mountou
Summary: This study aims to develop a high-performance early identification model for loess landslides based on convolutional neural networks (CNNs). By comparing remote sensing images and analyzing data, it is found that the CNN structure with slope crest data is the most suitable for early identification of potential loess landslides.
GEORISK-ASSESSMENT AND MANAGEMENT OF RISK FOR ENGINEERED SYSTEMS AND GEOHAZARDS
(2022)
Article
Materials Science, Multidisciplinary
Yuan-Yuan Liu, Yaping Zhang, Yanrong Li, Yi-Chen Guo
Summary: The discrete element method is commonly used to study the meso-mechanics of granular materials. The anisotropy of particle shape and fabric of particle packings have a coupled influence on the shear behavior of granular materials. The anisotropy from particle shape has the most significant influence when the initial fabric anisotropy is perpendicular to the shear direction.
ADVANCES IN MATERIALS SCIENCE AND ENGINEERING
(2022)
Article
Geosciences, Multidisciplinary
Jian Huang, Theodoor Wouterus Johannes van Asch, Changming Wang, Qiao Li
NATURAL HAZARDS AND EARTH SYSTEM SCIENCES
(2019)
