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Journal articles on the topic 'Early Warning Systems (EEWS) Machine Learning (ML)'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Abdalzaher, Mohamed S., Moez Krichen, Derya Yiltas-Kaplan, Imed Ben Dhaou, and Wilfried Yves Hamilton Adoni. "Early Detection of Earthquakes Using IoT and Cloud Infrastructure: A Survey." Sustainability 15, no. 15 (2023): 11713. http://dx.doi.org/10.3390/su151511713.

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Earthquake early warning systems (EEWS) are crucial for saving lives in earthquake-prone areas. In this study, we explore the potential of IoT and cloud infrastructure in realizing a sustainable EEWS that is capable of providing early warning to people and coordinating disaster response efforts. To achieve this goal, we provide an overview of the fundamental concepts of seismic waves and associated signal processing. We then present a detailed discussion of the IoT-enabled EEWS, including the use of IoT networks to track the actions taken by various EEWS organizations and the cloud infrastruct

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Abdalzaher, Mohamed S., M. Sami Soliman, Moez Krichen, Meznah A. Alamro, and Mostafa M. Fouda. "Employing Machine Learning for Seismic Intensity Estimation Using a Single Station for Earthquake Early Warning." Remote Sensing 16, no. 12 (2024): 2159. http://dx.doi.org/10.3390/rs16122159.

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An earthquake early-warning system (EEWS) is an indispensable tool for mitigating loss of life caused by earthquakes. The ability to rapidly assess the severity of an earthquake is crucial for effectively managing earthquake disasters and implementing successful risk-reduction strategies. In this regard, the utilization of an Internet of Things (IoT) network enables the real-time transmission of on-site intensity measurements. This paper introduces a novel approach based on machine-learning (ML) techniques to accurately and promptly determine earthquake intensity by analyzing the seismic activ

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Yashaswini, A., and KV Skandana. "Integrating Artificial Intelligence and IoT in Earthquake Disaster Management: A Comparative Literature Review." Journal of Advances Research in IOT Security 1, no. 1 (2025): 1–10. https://doi.org/10.5281/zenodo.14869006.

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<em>The integration of AI and IoT has revolutionized the management of earthquake disasters and redefined methods of prediction, preparation, response, and recovery. This paper presents a comprehensive literature survey and comparison of 11 research studies exploring the integration of the latest IoT and AI in the management of seismic disasters. The analysis examines technological methods, applications, efficiency, and research gaps. The key results present the advancements in the machine learning (ML) models including CNN, LSTM, hybrid frameworks, graph neural networks (GNNs), and the real-t

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Abdalzaher, Mohamed S., Hussein A. Elsayed, Mostafa M. Fouda, and Mahmoud M. Salim. "Employing Machine Learning and IoT for Earthquake Early Warning System in Smart Cities." Energies 16, no. 1 (2023): 495. http://dx.doi.org/10.3390/en16010495.

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An earthquake early warning system (EEWS) should be included in smart cities to preserve human lives by providing a reliable and efficient disaster management system. This system can alter how different entities communicate with one another using an Internet of Things (IoT) network where observed data are handled based on machine learning (ML) technology. On one hand, IoT is employed in observing the different measures of EEWS entities. On the other hand, ML can be exploited to analyze these measures to reach the best action to be taken for disaster management and risk mitigation in smart citi

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Soland, James, Benjamin Domingue, and David Lang. "Using Machine Learning to Advance Early Warning Systems: Promise and Pitfalls." Teachers College Record: The Voice of Scholarship in Education 122, no. 14 (2020): 1–30. http://dx.doi.org/10.1177/016146812012201403.

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Background/Context Early warning indicators (EWI) are often used by states and districts to identify students who are not on track to finish high school, and provide supports/interventions to increase the odds the student will graduate. While EWI are diverse in terms of the academic behaviors they capture, research suggests that indicators like course failures, chronic absenteeism, and suspensions can help identify students in need of additional supports. In parallel with the expansion of administrative data that have made early versions of EWI possible, new machine learning methods have been

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Pahuriray, Archolito V., and Patrick D. Cerna. "IoT-Enabled Flood Monitoring and Early Warning Systems: A Systematic Review." International Journal of Computer Science and Mobile Computing 14, no. 4 (2025): 50–67. https://doi.org/10.47760/ijcsmc.2025.v14i04.005.

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Technology powered by the Internet of Things (IoT) and Machine Learning (ML) is transforming flood monitoring and disaster management. This study systematically examines key components including sensors, machine learning models, and communication networks within IoT-based flood detection and prediction systems. Using the PRISMA framework, the researcher evaluated 44 high-quality papers from IEEE Xplore, ScienceDirect, and other relevant databases. The review highlights ultrasonic sensors, rain gauges, water level, and flow sensors as the most effective hardware components, often paired with en

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Shankar, Anand, Ashish Kumar, and Vivek Sinha. "Machine Learning approach in the prediction of Fog: An Early Warning System." MAUSAM 75, no. 4 (2024): 1039–50. http://dx.doi.org/10.54302/mausam.v75i4.5919.

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The aviation sector is extremely vulnerable to fog. Thus, accurate fog predictions are essential foraviation sector efficiency, particularly airport management and flight scheduling. Even with numerical weather predictionmodels and guiding systems, fog prediction is challenging. The difficulty of fog prediction is due to the inability to graspthe micro-scale factors that cause fog to form, intensify, augment and dissipate in the boundary layer. This study looks athow well machine learning (ML) tools can predict fog (Visibility <1000 m) and dense fog (Visibility <200 m) at India'sJay Prak

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Navarro-Rodríguez, Andrés, Oscar Alberto Castro-Artola, Enrique Efrén García-Guerrero, et al. "Recent Advances in Early Earthquake Magnitude Estimation by Using Machine Learning Algorithms: A Systematic Review." Applied Sciences 15, no. 7 (2025): 3492. https://doi.org/10.3390/app15073492.

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Earthquakes are among the most destructive natural phenomena, leading to significant loss of human life and substantial economic damage that severely impacts affected communities. Rapid detection and characterization of seismic parameters, including location and magnitude, are crucial for real-time seismological applications, including Earthquake Early Warning (EEW) systems. Machine learning (ML) has emerged as a powerful tool to enhance the accuracy of these applications, enabling more efficient responses to seismic events of different magnitudes. This systematic review aims to provide resear

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Rinta Kridalukmana, Dania Eridani, Risma Septiana, and Ike Pertiwi Windasari. "Enhancing River Flood Prediction in Early Warning Systems Using Fuzzy Logic-Based Learning." International Journal of Engineering and Technology Innovation 14, no. 4 (2024): 434–50. http://dx.doi.org/10.46604/ijeti.2024.13426.

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Previous studies show that the fuzzy-based approach predicts incoming floods better than machine learning (ML). However, with numerous observation points, difficulties in manually determining fuzzy rules and membership values increase. This research proposes a novel fuzzy logic-based learning (FLBL) that embeds missing data imputations and a fuzzy rule optimization strategy to enhance ML performance while still benefiting from fuzzy theory. The simple moving average handles sensors’ missing data. The logical mapping is used for fuzzification automation and fuzzy rule generation. The join funct

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Yadav, Himanshu. "Early Natural Disaster Prediction Using Machine Learning: A Comprehensive Review." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44416.

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Abstract - In many parts of the world, climate change has caused floods, earthquakes, cyclones, wildfires, and landslides that are more frequent and violent than ever before, and human activity is making them worse through deforestation and urbanization, threatening lives, economies, and ecosystems at previously unseen levels. The fallout from such events costs billions each year and forces millions from their homes — highlighting the need for better predictive tools to improve early warning systems that can trigger timely interventions to avoid human suffering and economic destruction. Into t

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Dr, Ketan Kargirwar, Anjali Dange Dr, and Rahul Pandit Dr. "The Role of Artificial Intelligence and Machine Learning in Decision-Making in the ICU." International Journal of Medical Science and Clinical Research Studies 04, no. 12 (2024): 2289–95. https://doi.org/10.5281/zenodo.14472265.

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Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing critical care. In the Intensive Care Unit (ICU), timely and accurate decisions are crucial. AI and ML can enhance decision-making by predicting adverse events, personalizing treatment plans, and improving diagnostic accuracy. Early warning systems, powered by AI, can detect conditions like sepsis and acute respiratory distress syndrome early on. AI-driven decision support systems provide real-time recommendations, optimizing resource allocation and ensuring adherence to best practices. While AI offers significant benefi

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El-Sayed, El, Marwa M. Eid, and Laith Abualigah. "Machine Learning in Public Health Forecasting and Monitoring the Zika Virus." Metaheuristic Optimization Review 1, no. 2 (2024): 01–11. https://doi.org/10.54216/mor.010201.

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The Zika virus is a severe public health threat all across the world, owing to its spreading mechanism through Aedes mosquitoes and its ability to result in extreme neurological diseases, which include the congenital Zika syndrome and the Guillain-Barré syndrome, amongst others. Conventional monitoring techniques often fail because many asymptomatic cases render early diagnosis challenging. Machine learning (ML) techniques can be seen as a constructive development in addressing this challenge, which entails predicting and tracking the spread of diseases such as Zika through extensive and compl

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Pang, Allan. "1 No NEWS is good NEWS – a machine learning approach to improve physiological early warning scoring." BMJ Military Health 171, no. 1 (2025): e1.2. https://doi.org/10.1136/bmjmilitary-2024-rsmabstracts.1.

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BackgroundCurrent Early Warning Scoring (EWS) systems in clinical practice are threshold rules-based systems using physiological data to highlight patients at risk of impending in-hospital death.Examples include the National Early Warning Score (NEWS2) and the Electronic Cardiac Arrest Triage (eCART) score, the standards of care within the UK and the USA, respectively.The current EWS modelling framework has two limitations. Firstly, they consider risk at a single time point and, therefore, cannot consider trajectories. Secondly, they negate relational information between covariates by decompos

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AL Rafy, Md Mashfiquer Rahman, Sharmin Nahar, Md. Najmul Gony, and MD IMRANUL HOQUE Bhuiyan. "The role of machine learning in predicting zero-day vulnerabilities." International Journal of Science and Research Archive 10, no. 1 (2023): 1197–208. https://doi.org/10.30574/ijsra.2023.10.1.0838.

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Zero-day vulnerabilities keep growing as an important threat in cybersecurity because attackers discover them before security teams can detect them. Signature-based detection methods fail to discover unknown vulnerabilities since they need prior knowledge of known attack techniques. ML technology emerges as the promising tool that predicts zero-day threats before attackers exploit them. This research aims to study the training approach of ML models that detect vulnerabilities by analyzing code structures, behavioral irregularities, and network traffic characteristics. The research examines zer

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AbdulRaheem, Muyideen, Joseph Bamidele Awotunde, Abidemi Emmanuel Adeniyi, Idowu Dauda Oladipo, and Sekinat Olaide Adekola. "Weather prediction performance evaluation on selected machine learning algorithms." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 4 (2022): 1535. http://dx.doi.org/10.11591/ijai.v11.i4.pp1535-1544.

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Prediction of weather has been proved useful in the early warning on the impacts of weather on several areas of human livelihood. For example, the provision of decisions for autonomous transportation to reduce traffic congestion and accidents during the rainy season. However, providing the most accurate and effective forecasting model for weather forecasts has been a challenge. Hence, machine learning (ML) techniques and factors influencing weather prediction need to be investigated. Data scientists are yet to discover the best models for weather prediction. Therefore, this study compares thre

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Adeoye, Adekunle, Chibuzo Okechukwu Onah, Enibokun Theresa Orobator, et al. "AI and Machine Learning for Early Detection of Infectious Diseases in the US: Opportunities and Challenges." Journal of Medical Science, Biology, and Chemistry 2, no. 1 (2025): 54–63. https://doi.org/10.69739/jmsbc.v2i1.465.

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Artificial intelligence and machine learning are reshaping U.S. infectious-disease surveillance by rapidly integrating clinical, environmental, and open-source data to flag anomalies sooner than conventional methods. This article aims to assess how artificial intelligence (AI) and machine learning (ML) can accelerate early detection of emerging infectious diseases in the United States In case studies, real-time ML tools cut hospital-acquired infections by 40 %, and systems like BlueDot predicted major outbreaks days before official alerts, underscoring strong gains in speed and accuracy. Howev

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Lin, Ge, Ai Shangle, Zhao Haoxiang, Yu Jingyue, and Yang Junyao. "Research on the Construction of Financial Supervision Information System Based on Machine Learning." Wireless Communications and Mobile Computing 2022 (June 15, 2022): 1–10. http://dx.doi.org/10.1155/2022/9986095.

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In order to fully implement systematic, continuous and effective supervision of financial institutions and promote the safe, steady, and efficient operation of China’s financial system, this research needs to develop a fully intelligent financial supervision information system, so as to take measures to effectively prevent and resolve financial risks. In this paper, based on ML (machine learning), an LSTM (long short-term memory) model with good comprehensive performance is built. This model is different from the existing scorecard model which relies on statistical learning. It not only furthe

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Sutradhar, Ananda, Mustahsin Al Rafi, Mohammad Jahangir Alam, and Saiful Islam. "An early warning system of heart failure mortality with combined machine learning methods." Indonesian Journal of Electrical Engineering and Computer Science 32, no. 2 (2023): 1115. http://dx.doi.org/10.11591/ijeecs.v32.i2.pp1115-1122.

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<span>Heart failure (HF) is currently the leading cause of morbidity and mortality worldwide. Identifying the risk of mortality at the early stages is crucial to reducing the mortality rate. However, the traditional methods for exploring the signs of mortality are difficult and time-consuming. Whereas, machine learning (ML) methods are superior in reducing HF’s mortality rate by providing early warnings. This study presents a novel ML classifier called imperial boost-stacked (IBS) that can serve as an effective early warning system for predicting HF mortality. Initially, we performed an

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Kemisola Kasali. "Machine learning applications in early warning systems for supply chain disruptions: strategies for adapting to risk, pandemics and enhancing business resilience and economic stability." International Journal of Science and Research Archive 15, no. 2 (2025): 1829–45. https://doi.org/10.30574/ijsra.2025.15.2.1612.

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Supply chains face unprecedented disruptions from cascading challenges such as pandemics, geopolitical tensions, and natural disasters, which pose significant risks to operational continuity and economic stability. This research examines the transformative role of machine learning-driven early warning systems in enhancing business resilience through predictive capabilities while supporting economic stability. Systematic analysis of evidence from literature and industry reports reveals machine learning (ML) models achieve up to 41% improvement in forecast accuracy and 15% reduction in supply ch

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Busari, Ibrahim, Debabrata Sahoo, R. Daren Harmel, and Brian E. Haggard. "Prediction Of Chlorophyll-a As an Index of Harmful Algal Blooms Using Machine Learning Models." Journal of Natural Resources and Agricultural Ecosystems 2, no. 2 (2024): 53–61. http://dx.doi.org/10.13031/jnrae.15812.

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Highlights The monitoring of HABs can be improved using ML models for chlorophyll-a prediction. ML model selection for HABs monitoring depends on target objectives. Random forest model predicts chlorophyll-a better when the temporal dimension is not considered. The LSTM model is essential for making time-dependent chlorophyll-a predictions for HABs monitoring. Abstract. The complex dynamics of freshwater harmful algal blooms (HABs) necessitate proactive monitoring approaches to mitigate their impacts. The rapid breakthrough in computing prowess and statistical advances is triggering the develo

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Soria-Lopez, Anton, Carlos Sobrido-Pouso, Juan C. Mejuto, and Gonzalo Astray. "Assessment of Different Machine Learning Methods for Reservoir Outflow Forecasting." Water 15, no. 19 (2023): 3380. http://dx.doi.org/10.3390/w15193380.

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Reservoirs play an important function in human society due to their ability to hold and regulate the flow. This will play a key role in the future decades due to climate change. Therefore, having reliable predictions of the outflow from a reservoir is necessary for early warning systems and adequate water management. In this sense, this study uses three approaches machine learning (ML)-based techniques—Random Forest (RF), Support Vector Machine (SVM) and artificial neural network (ANN)—to predict outflow one day ahead of eight different dams belonging to the Miño-Sil Hydrographic Confederation

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Becker, Marcus, Mikhail Beketov, and Manuel Wittke. "Machine Learning in Automated Asset Management Processes 4.1." Die Unternehmung 75, no. 3 (2021): 411–31. http://dx.doi.org/10.5771/0042-059x-2021-3-411.

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The traditional (human driven) process of Asset Management has become automatized by algorithmic decision trading with so called Robo Advisors (RAs). With an increasing amount of publicly available financial data, the foundation for applying machine learning (ML) algorithms has been paved. We examine the question in which process steps of automated investment advice ML algorithms could be applied and investigate which implementations have already been placed on the market. As the following study shows, (surprisingly) ML is globally still under its development phase in Robo Advisory. German and

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Hasan, Md Khalid, Md Mofizul Islam, and Maisha Fahmida. "Forecasting of Flood in the Non-Tidal River of Northern Regions of Bangladesh Using Machine Learning-Based Approach." Ceddi Journal of Information System and Technology (JST) 3, no. 1 (2024): 26–37. http://dx.doi.org/10.56134/jst.v3i1.69.

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Floods are among the most devastating natural disasters, causing extensive damage to property and posing a threat to human lives. However, significant progress has been made in mitigating their impact through the development of effective early warning systems. Over the past two decades, advances in machine learning (ML) technology have played a crucial role in enhancing the predictive capabilities of these systems. A recent study focused on predicting floods in non-tidal rivers by proposing various machine-learning models. The research findings indicate that the Random Forest algorithm emerges

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Chang, Li-Chiu, Fi-John Chang, Shun-Nien Yang, et al. "Building an Intelligent Hydroinformatics Integration Platform for Regional Flood Inundation Warning Systems." Water 11, no. 1 (2018): 9. http://dx.doi.org/10.3390/w11010009.

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Flood disasters have had a great impact on city development. Early flood warning systems (EFWS) are promising countermeasures against flood hazards and losses. Machine learning (ML) is the kernel for building a satisfactory EFWS. This paper first summarizes the ML methods proposed in this special issue for flood forecasts and their significant advantages. Then, it develops an intelligent hydroinformatics integration platform (IHIP) to derive a user-friendly web interface system through the state-of-the-art machine learning, visualization and system developing techniques for improving online fo

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Hadweh, Pierre, Alexandre Niset, Michele Salvagno, et al. "Machine Learning and Artificial Intelligence in Intensive Care Medicine: Critical Recalibrations from Rule-Based Systems to Frontier Models." Journal of Clinical Medicine 14, no. 12 (2025): 4026. https://doi.org/10.3390/jcm14124026.

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Artificial intelligence (AI) and machine learning (ML) are rapidly transforming clinical decision support systems (CDSSs) in intensive care units (ICUs), where vast amounts of real-time data present both an opportunity and a challenge for timely clinical decision-making. Here, we trace the evolution of machine intelligence in critical care. This technology has been applied across key ICU domains such as early warning systems, sepsis management, mechanical ventilation, and diagnostic support. We highlight a transition from rule-based systems to more sophisticated machine learning approaches, in

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Linardos, Vasileios, Maria Drakaki, Panagiotis Tzionas, and Yannis L. Karnavas. "Machine Learning in Disaster Management: Recent Developments in Methods and Applications." Machine Learning and Knowledge Extraction 4, no. 2 (2022): 446–73. http://dx.doi.org/10.3390/make4020020.

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Recent years include the world’s hottest year, while they have been marked mainly, besides the COVID-19 pandemic, by climate-related disasters, based on data collected by the Emergency Events Database (EM-DAT). Besides the human losses, disasters cause significant and often catastrophic socioeconomic impacts, including economic losses. Recent developments in artificial intelligence (AI) and especially in machine learning (ML) and deep learning (DL) have been used to better cope with the severe and often catastrophic impacts of disasters. This paper aims to provide an overview of the research s

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Karad R and Murnal P. "Seismic Excitation Processing Using Different Wavelets: A Review." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 02 (2025): 308–14. https://doi.org/10.47392/irjaem.2025.0048.

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Seismic excitation processing is essential for assessing and mitigating the effects of earthquakes and ground vibrations. Traditional methods like Fourier Transform (FT) and Short-Time Fourier Transform (STFT) are limited when analyzing non-stationary seismic signals, as they cannot simultaneously provide time and frequency localization. Wavelet Transform (WT) overcomes these limitations by decomposing signals across multiple scales, making it a powerful tool for seismic data analysis. This review delves into the mathematical framework of WT, emphasizing its capability to handle transient sign

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Al-Rawas, Ghazi, Mohammad Reza Nikoo, Malik Al-Wardy, and Talal Etri. "A Critical Review of Emerging Technologies for Flash Flood Prediction: Examining Artificial Intelligence, Machine Learning, Internet of Things, Cloud Computing, and Robotics Techniques." Water 16, no. 14 (2024): 2069. http://dx.doi.org/10.3390/w16142069.

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There has been growing interest in the application of smart technologies for hazard management. However, very limited studies have reviewed the trends of such technologies in the context of flash floods. This study reviews innovative technologies such as artificial intelligence (AI)/machine learning (ML), the Internet of Things (IoT), cloud computing, and robotics used for flash flood early warnings and susceptibility predictions. Articles published between 2010 and 2023 were manually collected from scientific databases such as Google Scholar, Scopus, and Web of Science. Based on the review, A

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Bonakdari, Hossein, Afshin Jamshidi, Jean-Pierre Pelletier, François Abram, Ginette Tardif, and Johanne Martel-Pelletier. "A warning machine learning algorithm for early knee osteoarthritis structural progressor patient screening." Therapeutic Advances in Musculoskeletal Disease 13 (January 2021): 1759720X2199325. http://dx.doi.org/10.1177/1759720x21993254.

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Aim: In osteoarthritis (OA) there is a need for automated screening systems for early detection of structural progressors. We built a comprehensive machine learning (ML) model that bridges major OA risk factors and serum levels of adipokines/related inflammatory factors at baseline for early prediction of at-risk knee OA patient structural progressors over time. Methods: The patient- and gender-based model development used baseline serum levels of six adipokines, three related inflammatory factors and their ratios (36), as well as major OA risk factors [age and bone mass index (BMI)]. Subjects

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Muñoz, Paul, Johanna Orellana-Alvear, Jörg Bendix, Jan Feyen, and Rolando Célleri. "Flood Early Warning Systems Using Machine Learning Techniques: The Case of the Tomebamba Catchment at the Southern Andes of Ecuador." Hydrology 8, no. 4 (2021): 183. http://dx.doi.org/10.3390/hydrology8040183.

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Worldwide, machine learning (ML) is increasingly being used for developing flood early warning systems (FEWSs). However, previous studies have not focused on establishing a methodology for determining the most efficient ML technique. We assessed FEWSs with three river states, No-alert, Pre-alert and Alert for flooding, for lead times between 1 to 12 h using the most common ML techniques, such as multi-layer perceptron (MLP), logistic regression (LR), K-nearest neighbors (KNN), naive Bayes (NB), and random forest (RF). The Tomebamba catchment in the tropical Andes of Ecuador was selected as a c

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Guerra, Pedro, and Mauro Castelli. "Machine Learning Applied to Banking Supervision a Literature Review." Risks 9, no. 7 (2021): 136. http://dx.doi.org/10.3390/risks9070136.

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Machine learning (ML) has revolutionised data analysis over the past decade. Like innumerous other industries heavily reliant on accurate information, banking supervision stands to benefit greatly from this technological advance. The objective of this review is to provide a comprehensive walk-through of how the most common ML techniques have been applied to risk assessment in banking, focusing on a supervisory perspective. We searched Google Scholar, Springer Link, and ScienceDirect databases for articles including the search terms “machine learning” and (“bank” or “banking” or “supervision”).

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Kong, Qingzhao, Jiaxuan Liu, Xiaohan Wu, and Cheng Yuan. "Seismic Fragility Estimation Based on Machine Learning and Particle Swarm Optimization." Buildings 14, no. 5 (2024): 1263. http://dx.doi.org/10.3390/buildings14051263.

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In seismic performance assessment, the development of building fragility curves is critical for performance-based engineering. Traditional methods for time history analysis, reliant on detailed ground motion (GM) inputs, often suffer from inefficiency and a lack of automation. This study proposes an accurate fragility assessment methodology, which is assisted by machine learning (ML) and particle swarm optimization (PSO), adept at handling scenarios with both scarce and sufficient fragility data. Under scenarios of scarce data, the integrated algorithms of PSO and ML are utilized, focusing on

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Subramaniam, Shankar, Naveenkumar Raju, Abbas Ganesan, et al. "Artificial Intelligence Technologies for Forecasting Air Pollution and Human Health: A Narrative Review." Sustainability 14, no. 16 (2022): 9951. http://dx.doi.org/10.3390/su14169951.

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Air pollution is a major issue all over the world because of its impacts on the environment and human beings. The present review discussed the sources and impacts of pollutants on environmental and human health and the current research status on environmental pollution forecasting techniques in detail; this study presents a detailed discussion of the Artificial Intelligence methodologies and Machine learning (ML) algorithms used in environmental pollution forecasting and early-warning systems; moreover, the present work emphasizes more on Artificial Intelligence techniques (particularly Hybrid

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Saurabh, Saurabh. "Comparative Study of Machine Learning Algorithms in Predicting Load-Induced Bridge Failures." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48074.

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Abstract: Bridges are critical components of transportation infrastructure, and their failure can lead to severe economic losses and safety risks. Traditional methods of monitoring and predicting structural failures often rely on manual inspections and periodic maintenance, which may miss early warning signs of degradation. This research explores the application of Artificial Intelligence (AI) techniques, including machine learning (ML) and deep learning (DL), in predicting structural failures of bridges. By analyzing data from sensors embedded in bridge structures, such as strain gauges, acce

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Vinodkumar Devarajan. "Integrated AI-ML framework for disaster lifecycle management: From prediction to recovery." World Journal of Advanced Research and Reviews 26, no. 2 (2025): 585–93. https://doi.org/10.30574/wjarr.2025.26.2.1630.

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This article examines the transformative role of artificial intelligence and machine learning (AI-ML) technologies across the disaster management lifecycle. It shows how these technologies enhance prediction accuracy, optimize resource allocation during emergency response, and improve post-disaster recovery operations. The article synthesizes findings from multiple studies and implementations worldwide, demonstrating how AI-ML systems outperform traditional approaches in early warning systems, emergency resource coordination, damage assessment, and infrastructure restoration. Through systemati

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Nwobodo, Jessica, Shugaba Wuta, Michael Ibitoye, Paul Omagbemi, and Martins Offie. "Recent Advances in Machine-Learning Driven Cholera Research: A Review." International Journal of Scientific Research in Modern Science and Technology 3, no. 10 (2024): 07–21. http://dx.doi.org/10.59828/ijsrmst.v3i10.255.

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Cholera is a potentially epidemic and life-threatening secretory diarrheal disease caused by Vibrio cholerae, it is transmitted through the consumption of contaminated water. Cholera is prevalent in developing countries, characterized by inadequate access to clean water, sanitation and proper hygiene. Various studies have been conducted to evaluate its impact, predict its outbreak, and determine the best response during an epidemic. In conducting those studies, traditional mathematical and statistical models have been utilized, but more recently, artificial intelligence machine learning (ML) m

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Venkata, Chaitanya Kumar Suram. "An Impact of Machine Learning Applications in Medicine and Healthcare." European Journal of Advances in Engineering and Technology 9, no. 9 (2022): 107–15. https://doi.org/10.5281/zenodo.14274651.

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The medical field is seeing the profound effects of machine learning (ML) applications. Machine learning (ML) is an area of artificial intelligence that aims to streamline medical procedures for the benefit of patients. Countries that are facing healthcare system overloads due to a shortage of trained medical professionals may find some solace in artificial intelligence. Using healthcare data, we may achieve several aims, such as finding the perfect trial sample, gathering more data points, assessing ongoing data from trial participants, and removing data-based errors. Machine learning techniq

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Adil, Hussain, Rao Gagandeep, K. P. Karthik, M. J. Samartha, and Thomas Likewin. "Machine Learning-Driven Cardiovascular and Stroke Screening Using IoT-Based Health Monitoring Systems." Journal of Advanced Research in Artificial Intelligence & It's Applications 2, no. 3 (2025): 18–29. https://doi.org/10.5281/zenodo.15128907.

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<em>The CVD and stroke global burden has become higher thereby emphasizing the need for improved surveillance and control measures. The utilization of wearable technologies and Heart Rate Variability (HRV) analysis accompanied by machine learning (ML) offer a novel preventive strategy. As a biomarker of ANS integrity, HRV allows evaluating the cardiovascular and stroke risks with no invasiveness. These devices use real-time data capture, data preprocessing, and ML algorithms to offer twofold, early warning alarms for looming risks and timely alarms for response at critical health episodes. Thi

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Sakovich, M. "Macroprudential Policies in the Light of the Development of Information Technologies: A Synthesis on the Effective Early Warning Signals." AlterEconomics 21, no. 3 (2024): 512–26. http://dx.doi.org/10.31063/altereconomics/2024.21-3.5.

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In response to recent recurrent crises, innovative macroprudential policies (MaPs) have been framed and implemented to address the weaknesses of market-led microprudential mechanisms and enhance the stability of financial systems. However, the effectiveness of the tools used to implement MaPs remains a critical research question. Early warning signals (EWS) serve as indicators of potential future crises. This paper explores approaches for identifying EWS to optimize the impact of MaPs, particularly in light of advances in information technology. It provides a comprehensive review of academic

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Singh, Niharika. "Advances in Smart Grids: Optimizing Energy Distribution Using IoT and Machine Learning." International Journal of Research in Modern Engineering & Emerging Technology 10, no. 3 (2022): 33–41. https://doi.org/10.63345/ijrmeet.org.v10.i3.5.

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Advances in smart grid technologies have transformed traditional electrical networks into dynamic, efficient, and resilient systems. This manuscript investigates the integration of Internet of Things (IoT) devices and machine learning (ML) algorithms to optimize energy distribution within smart grids, using only technologies available up to 2022. We present a comprehensive methodology combining real‐time sensor data acquisition, predictive analytics, and adaptive control strategies. A simulation study evaluates four optimization scenarios—baseline, IoT‐enhanced monitoring, ML‐driven forecastin

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.., El Mehdi, and Amine Saddik. "Machine Learning Data Fusion for Plant Disease Detection and Classification." Fusion: Practice and Applications 8, no. 1 (2022): 39–49. http://dx.doi.org/10.54216/fpa.080104.

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It is crucial to quickly identify plant diseases since they impede the development of affected plants. Despite the widespread use of Machine Learning (ML) models for this purpose, the recent advances in a subset of ML known as Deep Learning (DL) suggest that this field of study has much room for improvement in terms of detection and classification accuracy. To identify and categorize plant diseases, a wide variety of established and customized DL architectures are deployed with several visual analysis methods. In this study, we use deep learning techniques to create a model for a convolutional

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Byaruhanga, Nicholas, Daniel Kibirige, Shaeden Gokool, and Glen Mkhonta. "Evolution of Flood Prediction and Forecasting Models for Flood Early Warning Systems: A Scoping Review." Water 16, no. 13 (2024): 1763. http://dx.doi.org/10.3390/w16131763.

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Floods are recognised as one of the most destructive and costliest natural disasters in the world, which impact the lives and livelihoods of millions of people. To tackle the risks associated with flood disasters, there is a need to think beyond structural interventions for flood protection and move to more non-structural ones, such as flood early warning systems (FEWSs). Firstly, this study aimed to uncover how flood forecasting models in the FEWSs have evolved over the past three decades, 1993 to 2023, and to identify challenges and unearth opportunities to assist in model selection for floo

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Oluwabukola Emi-Johnson, Oluwafunmibi Fasanya, and Ayodele Adeniyi. "Predictive crop protection using machine learning: A scalable framework for U.S. Agriculture." International Journal of Science and Research Archive 15, no. 1 (2025): 670–88. https://doi.org/10.30574/ijsra.2025.15.1.1536.

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The increasing unpredictability of biotic stressors—such as pests, pathogens, and invasive species—poses a major threat to crop productivity, profitability, and food security across U.S. agricultural systems. Traditional crop protection approaches, often reactive and resource-intensive, struggle to cope with the dynamic interactions between environmental conditions, crop genotypes, and pathogen evolution. As the agricultural sector transitions toward climate-resilient and precision-based farming systems, there is a growing imperative for scalable, data-driven solutions that can anticipate dise

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Oluwabukola Emi-Johnson, Oluwafunmibi Fasanya, and Ayodele Adeniyi. "Predictive crop protection using machine learning: A scalable framework for U.S. Agriculture." International Journal of Science and Research Archive 12, no. 2 (2024): 3065–83. https://doi.org/10.30574/ijsra.2024.12.2.1536.

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The increasing unpredictability of biotic stressors—such as pests, pathogens, and invasive species—poses a major threat to crop productivity, profitability, and food security across U.S. agricultural systems. Traditional crop protection approaches, often reactive and resource-intensive, struggle to cope with the dynamic interactions between environmental conditions, crop genotypes, and pathogen evolution. As the agricultural sector transitions toward climate-resilient and precision-based farming systems, there is a growing imperative for scalable, data-driven solutions that can anticipate dise

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Aswad, Firas Mohammed, Ali Noori Kareem, Ahmed Mahmood Khudhur, Bashar Ahmed Khalaf, and Salama A. Mostafa. "Tree-based machine learning algorithms in the Internet of Things environment for multivariate flood status prediction." Journal of Intelligent Systems 31, no. 1 (2021): 1–14. http://dx.doi.org/10.1515/jisys-2021-0179.

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Abstract Floods are one of the most common natural disasters in the world that affect all aspects of life, including human beings, agriculture, industry, and education. Research for developing models of flood predictions has been ongoing for the past few years. These models are proposed and built-in proportion for risk reduction, policy proposition, loss of human lives, and property damages associated with floods. However, flood status prediction is a complex process and demands extensive analyses on the factors leading to the occurrence of flooding. Consequently, this research proposes an Int

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Cheng, Yunyun, Rong Cheng, Ting Xu, Xiuhui Tan, and Yanping Bai. "Machine Learning Techniques Applied to COVID-19 Prediction: A Systematic Literature Review." Bioengineering 12, no. 5 (2025): 514. https://doi.org/10.3390/bioengineering12050514.

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COVID-19 was one of the most serious global public health emergencies in recent years, and its extremely fast spreading speed had a profound negative impact on society. A comprehensive analysis and prediction of COVID-19 could lay a theoretical foundation for monitoring and early warning systems. Since the outbreak of COVID-19, there has been an influx of research on predictive modelling, with artificial intelligence (AI) techniques, particularly machine learning (ML) methods, becoming the dominant research direction due to their superior capability in processing multidimensional datasets and

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Nizar, HAMADEH. "Advancements in Wildfire Detection and Prediction: An In-Depth Review." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 13, no. 2 (2024): 6–15. https://doi.org/10.35940/ijitee.B9774.13020124.

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<strong>Abstract:</strong> Wildfires pose a significant hazard, endangering lives, causing extensive damage to both rural and urban areas, causing severe harm for forest ecosystems, and further worsening the atmospheric conditions and the global warming crisis. Electronic bibliographic databased were searched in accordance with PRISMA guidelines. Detected items were screened on abstract and title level, then on full-text level against inclusion criteria. Data and information were then abstracted into a matrix and analyzed and synthesized narratively. Information was classified into 2 main cate

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Supriya, Y., and Thippa Reddy Gadekallu. "Particle Swarm-Based Federated Learning Approach for Early Detection of Forest Fires." Sustainability 15, no. 2 (2023): 964. http://dx.doi.org/10.3390/su15020964.

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Forests are a vital part of the ecological system. Forest fires are a serious issue that may cause significant loss of life and infrastructure. Forest fires may occur due to human or man-made climate effects. Numerous artificial intelligence-based strategies such as machine learning (ML) and deep learning (DL) have helped researchers to predict forest fires. However, ML and DL strategies pose some challenges such as large multidimensional data, communication lags, transmission latency, lack of processing power, and privacy concerns. Federated Learning (FL) is a recent development in ML that en

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Aljohani, Fares Hamad, Adnan Ahmed Abi Sen, Muhammad Sher Ramazan, Bander Alzahrani, and Nour Mahmoud Bahbouh. "A Smart Framework for Managing Natural Disasters Based on the IoT and ML." Applied Sciences 13, no. 6 (2023): 3888. http://dx.doi.org/10.3390/app13063888.

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Natural disasters greatly threaten our lives in addition to adversely affecting all activities. Unfortunately, most solutions currently used in flood management are suffering from many drawbacks related to latency and accuracy. Moreover, the previous solutions consider that the whole city has the same level of vulnerability to damage, while each area in the city may have different topologies and conditions. This study presents a new framework that collects data in real-time about bad weather, which may cause floods, where the framework has a proposed classification algorithm to process sensed

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Wu, Kuan-Han, Fu-Jen Cheng, Hsiang-Ling Tai, et al. "Predicting in-hospital mortality in adult non-traumatic emergency department patients: a retrospective comparison of the Modified Early Warning Score (MEWS) and machine learning approach." PeerJ 9 (August 24, 2021): e11988. http://dx.doi.org/10.7717/peerj.11988.

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Background A feasible and accurate risk prediction systems for emergency department (ED) patients is urgently required. The Modified Early Warning Score (MEWS) is a wide-used tool to predict clinical outcomes in ED. Literatures showed that machine learning (ML) had better predictability in specific patient population than traditional scoring system. By analyzing a large multicenter dataset, we aim to develop a ML model to predict in-hospital morality of the adult non traumatic ED patients for different time stages, and comparing performance with other ML models and MEWS. Methods A retrospectiv

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