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machine learning for Urban Heat Island (UHI) Analysis: Predicting Land Surface Temperature (LST) in Urban Environments

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URBAN CLIMATE 2024年 55卷

作者： Tanoori, Ghazaleh Soltani, Ali Modiri, Atoosa Islamic Azad Univ Dept Elect Engn Cent Tehran Branch Tehran Iran Flinders Univ S Australia Flinders Hlth & Med Res Inst FHMRI Bedford Pk 5042 Australia

This study investigates how urban configuration influences the distribution of heat, known as the Urban Heat Island (UHI) effect, in Shiraz, Iran. Several machine learning algorithms are employed to analyze Land Surface Temperature (LST) data across various land cover types, including built-up, soil, and vegetation. The analysis reveals that Deep Neural Networks (DNNs) and Extreme Gradient Boosting (XGB) models excel at predicting LST, outperforming other methods. These results highlight the significant impact of land use on LST patterns within the metropolitan regions. Furthermore, the study assesses the influence of specific configuration metrics within each land cover category. This allows researchers to pinpoint which urban morphology features most significantly affect LST. These insights can inform targeted interventions and management strategies implemented to mitigate heat and improve thermal comfort in specific areas of Shiraz.

关键词： Urban Heat Island Land surface temperature Configuration metrics machine learning algorithms Prediction Deep Neural Network

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machine learning-based ensemble approach in prediction of lung cancer predisposition using XRCC1 gene polymorphism

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JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS 2024年第15期42卷 7828-7837页

作者： Choudhary, Abhishek Anand, Adarsh Singh, Amrita Roy, Pratima Singh, Navneet Kumar, Vinay Sharma, Siddharth Baranwal, Manoj Thapar Inst Engn & Technol Dept Comp Sci Patiala India Thapar Inst Engn & Technol Dept Elect & Commun Engn Patiala India Thapar Inst Engn & Technol Dept Biotechnol Patiala 147004 Punjab India Post Grad Inst Educ & Med Res PGIMER Dept Pulm Med Chandigarh India

The employment of machine learning approaches has shown promising results in predicting cancer. In the current study, polymorphisms data of five single nucleotide polymorphisms (SNPs) of DNA repair gene XRCC1 (XRCC1 399, XRCC1 194, XRCC1 206, XRCC1 632, XRCC1 280) of the north Indian population along with four smoking status data is considered as an input to the proposed ensemble model to predict the risk of individual susceptibility to the lung cancer. The prediction accuracy of the proposed ensemble model for cancer predisposition was found to be 85%. The model performance is also evaluated using sensitivity, specificity, precision and the Gini index, which is found in the range of 0.83-0.87. The proposed model also outperformed in all evaluation parameters when compared with the individual Model (LM, SVM, RF, KNN and baseline neural net). Collectively, current results suggest the potential of the proposed ensemble model in predicting the risk of cancer based on XRCC1 SNPs *** by Ramaswamy H. Sarma

关键词： Lung cancer single nucleotide polymorphism XRCC1 machine learning algorithms

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machine learning-aided hybrid technique for dynamics of rail transit stations classification: a case study

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SCIENTIFIC REPORTS 2024年第1期14卷 1-30页

作者： Pishro, Ahad Amini Zhang, Shiquan L'Hostis, Alain Liu, Yuetong Hu, Qixiao Hejazi, Farzad Shahpasand, Maryam Rahman, Ali Oueslati, Abdelbacet Zhang, Zhengrui Sichuan Univ Sci & Engn Sch Civil Engn Zigong 643000 Peoples R China Sichuan Univ Sch Math Chengdu 610065 Peoples R China Univ Gustave Eiffel Ecole Ponts LVMT F-77454 Marne La Vallee France Univ West England Sch Environm & Technol Bristol BS16 1QY England Staffordshire Univ London Here East Queen Elizabeth Olymp Pk Here East London E20 3BS England Univ Leeds Fac Engn & Phys Sci Sch Civil Engn Leeds England Univ Lille Cent Lille CNRS UMR 9013LaMcube Lab Mecan Multiphys Multiechelle F-59000 Lille France

Accurate classification of rail transit stations is crucial for successful Transit-Oriented Development (TOD) and sustainable urban growth. This paper introduces a novel classification model integrating traditional methodologies with advanced machine learning algorithms. By employing mathematical models, clustering methods, and neural network techniques, the model enhances the precision of station classification, allowing for a refined evaluation of station attributes. A comprehensive case study on the Chengdu rail transit network validates the model's efficacy, highlighting its value in optimizing TOD strategies and guiding decision-making processes for urban planners and policymakers. The study employs several regression models trained on existing data to generate accurate ridership forecasts, and data clustering using mathematical algorithms reveals distinct categories of stations. Evaluation metrics confirm the rationality and accuracy of the results. Additionally, a neural network achieving high accuracy on labeled data enhances the model's predictive capabilities for unlabeled instances. The research demonstrates high accuracy, with the Mean Squared Error (MSE) for regression models (Multiple Linear Regression (MLR), Deep-learning Neural Network (DNN), and K-Nearest Neighbor (KNN)) remaining below 0.012, while the neural networks used for station classification achieve 100% accuracy across seven time intervals and 98.15% accuracy for the eighth, ensuring reliable ridership forecasts and classification outcomes. Accuracy in rail transit station classification is critical, as it not only strengthens the model's predictive capabilities but also ensures more reliable data-driven decisions for transit planning and development, allowing for more precise ridership forecasts and evidence-based strategies for optimizing TOD. This classification model provides stakeholders with valuable insights into the dynamics and features of rail transit stations, supporting sustain

关键词： Rail Transit Station Classification Transit Oriented Development machine learning algorithms Clustering methods Regression models

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A Comprehensive Review of machine learning Approaches for Anomaly Detection in Smart Homes: Experimental Analysis and Future Directions

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FUTURE INTERNET 2024年第4期16卷 139-139页

作者： Rahman, Md Motiur Gupta, Deepti Bhatt, Smriti Shokouhmand, Shiva Faezipour, Miad Purdue Univ Sch Engn Technol Elect & Comp Engn Technol W Lafayette IN 47907 USA Texas A&M Univ Cent Texas Subhani Dept Comp Informat Syst Killeen TX 76549 USA Purdue Univ Dept Comp & Informat Technol W Lafayette IN 47907 USA

Detecting anomalies in human activities is increasingly crucial today, particularly in nuclear family settings, where there may not be constant monitoring of individuals' health, especially the elderly, during critical periods. Early anomaly detection can prevent from attack scenarios and life-threatening situations. This task becomes notably more complex when multiple ambient sensors are deployed in homes with multiple residents, as opposed to single-resident environments. Additionally, the availability of datasets containing anomalies representing the full spectrum of abnormalities is limited. In our experimental study, we employed eight widely used machine learning and two deep learning classifiers to identify anomalies in human activities. We meticulously generated anomalies, considering all conceivable scenarios. Our findings reveal that the Gated Recurrent Unit (GRU) excels in accurately classifying normal and anomalous activities, while the na & iuml;ve Bayes classifier demonstrates relatively poor performance among the ten classifiers considered. We conducted various experiments to assess the impact of different training-test splitting ratios, along with a five-fold cross-validation technique, on the performance. Notably, the GRU model consistently outperformed all other classifiers under both conditions. Furthermore, we offer insights into the computational costs associated with these classifiers, encompassing training and prediction phases. Extensive ablation experiments conducted in this study underscore that all these classifiers can effectively be deployed for anomaly detection in two-resident homes.

关键词： anomaly detection smart home machine learning algorithms deep learning

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Stabilization of Na-Ion Cathode Surfaces: Combinatorial Experiments with Insights from machine learning Models

ADVANCED ENERGY AND SUSTAINABILITY RESEARCH

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ADVANCED ENERGY AND SUSTAINABILITY RESEARCH 2024年第9期5卷

作者： Jia, Shipeng Abdolhosseini, Marzieh Liu, Chenghao Jonderian, Antranik Li, Yixuan Kwak, Hunho Kumakura, Shinichi Sieffert, James Michael Eisnor, Maddison Mccalla, Eric McGill Univ Dept Chem Montreal PQ H3A 0B8 Canada Mila Quebec Artificial Intelligence Inst 6666 Rue St Urbain Montreal PQ H2S 3H1 Canada Umicore 31 Rue Marais BE-1000 Brussels Belgium Umicore Japan KK Minato Tokyo 1076026 Japan

Na-Fe-Mn-O cathodes hold promise for environmentally benign high-energy sodium-ion batteries, addressing material scarcity concerns in Li-ion batteries. To date, these materials show poor stability in the air and suffer significant Fe/Mn dissolution during use. These two detrimental surface effects have so far prevented the commercialization of these materials. Herein, high-throughput experiments to make hundreds of substitutions into a previously optimized Na-Fe-Mn-O material are utilized. Numerous single-phase materials are made with good electrochemical performance that shows moderate improvements over the unsubstituted. By contrast, dramatic improvements are made in suppressing decomposition in air and Fe/Mn dissolution. machine learning algorithms are utilized to further understand the changes in air stability and to decouple the effects of various structural parameters such as lattice parameters and crystallite size. The comprehensive dataset and methodology established here lay the groundwork for future exploration and optimization of cathode materials, driving the advancement of next-generation sodium-ion batteries. High-throughput methods improve both air stability and resistance to transition metal dissolution in next-generation sodium-ion cathodes. The layered oxides are tuned by adding dopants with a number of candidates being found that advance these materials toward commercial viability. machine learning is used to better understand how structural changes stabilize the particle *** (c) 2024 WILEY-VCH GmbH

关键词： air stability high-throughput experimentation machine learning algorithms Na-ion battery cathodes transition metal dissolution

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The role of morphometric characteristics in predicting 20-meter sprint performance through machine learning

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SCIENTIFIC REPORTS 2024年第1期14卷 1-15页

作者： Kurtoglu, Ahmet Eken, Ozgur Ciftci, Rukiye Car, Bekir Donmez, Emrah Kilicarslan, Serhat Jamjoom, Mona M. Samee, Nagwan Abdel Hassan, Dina S. M. Mahmoud, Noha F. Bandirma Onyedi Eylul Univ Fac Sport Sci Dept Coaching Educ TR-10200 Balikesir Turkiye Inonu Univ Fac Sports Sci Dept Phys Educ & Sport Teaching Malatya Turkiye Gaziantep Islamic Sci & Technol Univ Med Fac Dept Anat Gaziantep Turkiye Bandirma Onyedi Eylul Univ Fac Sport Sci Dept Phys Educ & Sport Teaching TR-10200 Balikesir Turkiye Bandirma Onyedi Eylul Univ Dept Software Engn Balikesir Turkiye Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Comp Sci Riyadh 11671 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol POB 84428 Riyadh 11671 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Hlth & Rehabil Sci Coll Rehabil Sci Dept POB 84428 Riyadh 11671 Saudi Arabia

The aim of this study was to test the morphometric features affecting 20-m sprint performance in children at the first level of primary education using machine learning (ML) algorithms. In this study, 130 male and 152 female volunteers aged between 6 and 11 years were included. After obtaining demographic information of the participants, skinfold thickness, diameter and circumference measurements, and 20-m sprint performance were determined. The study conducted three distinct experiments to determine the optimal ML technique for predicting outcomes. Initially, the entire feature space was utilized for training the ML models to establish a baseline performance. In the second experiment, only significant features identified through correlation analysis were used for training and testing the models, enhancing the focus on relevant predictors. Lastly, Principal Component Analysis (PCA) was employed to reduce the feature space, aiming to streamline model complexity while retaining data variance. These experiments collectively aimed to evaluate different feature selection and dimensionality reduction techniques, providing insights into the most effective strategies for optimizing predictive performance in the given context. The correlation-based selected features (Age, Height, waist circumference, hip circumference, leg length, thigh length, foot length) has produced a minimum Mean Squared Error (MSE) value of 0.012 for predicting the sprint performance in children. The effective utilization of correlation analysis in the selection of relevant features for our regression model suggests that the features selected exhibit robust linear associations with the target variable and can be relied upon as predictors.

关键词： Morphometric features 20-m sprint performance Children machine learning algorithms Correlation analysis

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machine learning-based design of double corrugated steel plate shear walls

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INTERNATIONAL JOURNAL OF STRUCTURAL INTEGRITY 2024年第6期15卷 1216-1248页

作者： Nezhadi, Elyas Baboli Labibzadeh, Mojtaba Hosseinlou, Farhad Khayat, Majid Shahid Chamran Univ Ahvaz Fac Civil Engn & Architecture Dept Civil Engn Ahvaz Iran

Purpose - In this study, machine learning (ML) algorithms were employed to predict the shear capacity and behavior of DCSWs. Design/methodology/approach - In this study, ML algorithms were employed to predict the shear capacity and behavior of DCSWs. Various ML techniques, including linear regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost) and artificial neural network (ANN), were utilized. The ML models were trained using a dataset of 462 numerical and experimental samples. Numerical models were generated and analyzed using the finite element (FE) software Abaqus. These models underwent push-over analysis, subjecting them to pure shear conditions by applying a target displacement solely to the top of the shear walls without interaction from a frame. The input data encompassed eight survey variables: geometric values and material types. The characterization of input FE data was randomly generated within a logical range for each variable. The training and testing phases employed 90 and 10% of the data, respectively. The trained models predicted two output targets: the shear capacity of DCSWs and the likelihood of buckling. Accurate predictions in these areas contribute to the efficient lateral enhancement of structures. An ensemble method was employed to enhance capacity prediction accuracy, incorporating select algorithms. Findings - The proposed model achieved a remarkable 98% R-score for estimating shear strength and a corresponding 98% accuracy in predicting buckling occurrences. Among all the algorithms tested, XGBoost demonstrated the best performance. Originality/value - In this study, for the first time, ML algorithms were employed to predict the shear capacity and behavior of DCSWs.

关键词： Double corrugated shear walls Shear capacity prediction Artificial intelligence machine learning algorithms Buckling analysis

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A Novel Federated & Ensembled learning-Based Battery State-of-Health Estimation for Connected Electric Vehicles

IEEE OPEN JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS

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IEEE OPEN JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS 2024年 5卷 445-453页

作者： Abbaraju, Praveen Kundu, Subrata Kumar Hitachi Astemo Amer Inc Adv Technol Dev Dept Farmington Hills MI 48335 USA

Electric vehicles (EV) are gaining wide traction and popularity despite the operational range and charging time limitations. Therefore, to ensure the reliability of EVs for realizing improved customer satisfaction, it is necessary to monitor and track its battery condition. This paper introduces a novel federated & ensembled learning (FEL) algorithm for precise estimation of battery State of Health (SoH). FEL algorithm leverages real-world data from diverse stakeholders and geographical factors like traffic and weather data. A Long-Short Term Memory (LSTM) model has been implemented as a base-model for SoH estimation, continuously updating for each trip as an edge scenario using data-centric federated learning strategy. A stacked ensemble learning algorithm is employed to combine data from heterogenous data sources for retraining the base-model. The effectiveness of the proposed FEL algorithm has been evaluated using NASA battery dataset, showing significant improvement in SoH estimations with a mean average error of 3.24% after 30 iterations. Comparative analysis, including LSTM model with and without ensembled stakeholder data, reveals up to 75% accuracy improvement. The proposed model-agnostic FEL algorithm shows its effectiveness in precise SoH estimation through efficient data sharing among stakeholders and could bring significant benefits for realizing data-centric intelligent solutions for connected EVs.

关键词： Batteries Estimation Data models Accuracy Stakeholders Integrated circuit modeling machine learning algorithms Data-centric AI federated learning state of health (SoH) connected vehicles

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Applications of chemical fingerprints and machine learning in plant ecology: Recent progress and future perspectives

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MICROCHEMICAL JOURNAL 2024年 206卷

作者： Zhong, Chen Li, Li Wang, Yuan-Zhong Med Plants Res Inst Yunnan Acad Agr Sci Kunming 650200 Peoples R China Jishou Univ Coll Biol Resources & Environm Sci Hunan Prov Jishou 416000 Peoples R China

With the rapid development of chromatography, spectroscopy and other detection techniques, chemical fingerprinting has become a powerful tool for ecology research. The data generated by these techniques contain a large amount of key information related to the molecular structure, and the optimization and study of these data using chemometrics and machine learning algorithms will have higher precision and accuracy. This paper reviews the analytical techniques used to generate chemical fingerprints in recent years and scrutinises the diversity of applications of chemical fingerprints in the field of plant ecology. Applications in combination with machine learning are emphasized. Prospects for chemical fingerprinting combined with machine learning in plant ecology include the development of fingerprinting databases for accurate species identification, and the integration of advanced techniques to incorporate fingerprinting techniques into ecological modelling to predict plant responses to environmental changes. These innovative avenues hold the promise of improving our understanding of plants and their complex interactions in various ecosystems and integrating them to advance ecological research.

关键词： Chemical fingerprints Chemometrics Plant ecology Analytical techniques machine learning algorithms

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Integration of machine learning and remote sensing for assessing the change detection of mangrove forests along the Mumbai coast

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JOURNAL OF EARTH SYSTEM SCIENCE 2024年第4期133卷 186页

作者： Sawant, Suraj Bonala, Praneetha Joshi, Amit Shindikar, Mahesh Patil, Abhilasha Vyas, Swapnil Deobagkar, Deepti Pune Technol Univ Coll Engn Dept Comp Sci & Engn Wellesley Rd Pune 411005 India Coll Engn Pune Technol Univ Dept Appl Sci & Humanity Wellesley Rd Pune 411005 India Savitribai Phule Pune Univ Dept Geog Geoinformat Pune 411007 India Savitribai Phule Pune Univ Dept Geog Geoinformat Pune 411007 India

Mangrove forests, being high-yielding ecosystems, often dominate the intertidal sites along equatorial and subtropical coasts. Despite the known significance of mangroves to the coastal ecology, especially fisheries, deforestation remains a severe danger due to coercion for forest products, ground transformation for aquaculture, and seaside urban growth. Remote sensing is integral in mapping and analysing changes in mangrove forests' areal extent and spatial patterns due to natural disasters and anthropogenic causes over the last three decades. This work depicts remote sensing analysis for change detection in mangrove forest land use land cover from 2014 to 2019. Indian Remote-Sensing Satellite Resourcesat-2 LISS-IV datasets have been used for analysis. A comparison with the Sentinel-2A dataset and two machine learning models: Random Forest and Classification and Regression Tree, has been performed with 2019 data. This work identifies CART as a suitable choice for supervised landform classification utilising remotely sensed geophysical data that is used to decipher spatial changes concurred over time. An overall growth in the mangrove cover was observed from 2014 to 2019, from 86.26 to 89.63 km2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\hbox {km}<^>{2}}$$\end{document}, along the Mumbai coastline. Spatial comparison over the years shows the growth and loss of land-use cover areas. The performance metrics such as overall accuracy, producer accuracy, Kappa coefficient, and Matthews correlation coefficient are computed. The experiments were conducted using the Google Earth Engine, a powerful cloud computing platform.

关键词： Mangrove forest machine learning algorithms land use land cover assessment change detection Google Earth Engine

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