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A Method for Predicting the Morphology of Multi-track Laser Cladding Coatings Based on WOA-RF

INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING-GREEN TECHNOLOGY 2025年 1-17页

作者： Du, Yanbin Lei, Xin Chen, Hongxi Liang, Qiang Ma, Wensheng Tu, Jian Chongqing Technol & Business Univ Chongqing Key Lab Green Design & Mfg Intelligent E Chongqing 400067 Peoples R China Chongqing Technol & Business Univ Coll Mech Engn Chongqing 400067 Peoples R China Chongqing Pump Ind Co Ltd Chongqing 400033 Peoples R China Chongqing Univ Technol Coll Mat Sci & Engn Chongqing 400054 Peoples R China

The morphology of multi-track laser cladding coatings is influenced by various process parameters, including laser power, scanning speed, and powder feeding rate. Accurate morphology prediction is crucial for optimizing process parameters and enhancing coating performance. A novel prediction framework that integrates the Whale Optimization algorithm (WOA) with the random forest (RF) algorithm (referred to as WOA-RF) is proposed to model the nonlinear relationship between the process parameters involved in laser cladding and the coating morphology. A central composite experiment design (CCD) with 4 factors and 5 levels was designed to carry out the laser cladding experiment, with the 17-4PH as the substrate and the 15-5PH as the cladding powder. The effects of the process parameters of laser cladding (laser power, powder feeding rate, scanning speed, and overlap rate) on the coating morphology characteristics (dilution rate, heat-affected zone area, and surface flatness) were analyzed. The relative importance of each process parameter was assessed by the random forest model. Based on the assessment results, a WOA-RF prediction model was established, and a multiple regression model and a random forest (RF) model were established for comparative analysis. The analysis reveals that the powder feeding rate exerts the most significant impact on the dilution rate, laser power is the key determinant of the area of the heat-affected zone, and the overlap rate predominantly affects surface flatness of the coating. The evaluation of parameter importance indicates that, under the conditions of this experiment, the overlap rate in the predictive model has a minimal contribution to the area of the heat-affected zone, and the laser power has a minimal contribution to the surface flatness of the coating. The WOA-RF model demonstrates superior performance in predicting morphological features across all evaluated metrics, outperforming both the RF model and the multiple regression mo

关键词： Laser cladding Morphology prediction random forest algorithm Whale Optimization algorithm

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Fault diagnosis and degree evaluation of steam generator heat transfer tube rupture based on hybrid method

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ANNALS OF NUCLEAR ENERGY 2025年 211卷

作者： Jiang, Yingying Xia, Hong Zhou, Zhuoran Yin, Wenzhe Jia, Zhujun Huang, Xueying Zhang, Jiyu Zhu, Yihu Harbin Engn Univ Fundamental Sci Nucl Safety & Simulat Technol Lab Harbin 150001 Peoples R China Xi An Jiao Tong Univ Natl Innovat Platform Ctr Ind Educ Integrat Energy Xian 710049 Peoples R China

As critical pressure boundary components in pressurized water reactor, the integrity of steam generator heat transfer tubes is continually challenged by adverse operational environments. Direct monitoring of their condition is unfeasible. A hybrid fault diagnosis method for detecting and evaluating the damage of steam generator heat transfer tubes was proposed by this paper. This method constructed pseudo bond graph model by acquiring in-depth knowledge of steam generators and the causal relationships among its various parameters. To overcome the expert system's limitation in evaluating the extent of damage, a random forest algorithm was employed for severity assessment. Furthermore, particle swarm optimization-based swarm intelligence method was utilized to optimize the hyperparameters of the random forest model. Performance tests indicated that the proposed method could diagnose damage in heat transfer tubes with high accuracy and promptness and effectively assesses the extent of damage in the heat transfer tubes of steam generators.

关键词： Natural circulation steam generator Heat transfer tube rupture Fault severity assessment Pseudo bond graph random forest algorithm

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The Response of Small Watershed Storm Floods to Climate Change

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WATER 2025年第1期17卷 33-33页

作者： Qian, Jing-Lin Wu, Yun-Xin Zhang, Qi-Ting Zhejiang Univ Water Resources & Elect Power Sch Hydraul Engn Hangzhou 310018 Peoples R China

This study utilizes historical monitoring data from the Xu Fan small watershed spanning 1962 to 2021 and employs the K-means clustering algorithm to classify extreme rainfall events into three distinct categories: short-duration high-intensity rainfall, sustained moderate-intensity rainfall, and long-duration heavy rainfall. Through the application of the random forest model, key factors influencing flood characteristics are identified, including total rainfall, maximum rainfall intensity, the timing of maximum intensity, and rainfall duration. The comparative analysis of data before and after 1990 highlights that climate change has led to increased maximum rainfall intensity, reduced rainfall duration, and shifts in the temporal distribution of rainfall, thereby exerting a significant influence on the flood generation process.

关键词： climate change small watershed random forest algorithm extreme rainfall flood

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Remote Sensing Detection of forest Changes in the South Ridge Corridor and an Attribution Analysis

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forestS 2025年第2期16卷 205-205页

作者： Wu, Nan Huang, Linghui Zhang, Meng Dou, Yaqing Mo, Kehan Liu, Junang Cent South Univ Forestry & Technol Key Lab Natl Forestry & Grassland Adm Control Arti Changsha 410004 Peoples R China Hunan Linkeda Agroforestry Tech Serv Co Ltd Changsha 410004 Peoples R China Zhejiang A&F Univ Coll Environm & Resource Sci Hangzhou 311300 Peoples R China

As the largest mountain range in Southern China, the natural vegetation of Nanling plays an irreplaceable role in maintaining the stability of the ecosystem and exerting its functions. The forested area of the Nanling Corridor encompasses 168,633 km2, with a forest coverage rate exceeding 60% of all cities together. Long-term analysis of the temporal and spatial evolution of this forest and the disturbance factors in this region is of great importance for realizing the "dual carbon" goals, sustainable forest management, and protecting biodiversity. In this study, remote sensing images from a Landsat time series with a resolution of 30 m were obtained from the GEE (Google Earth Engine) cloud processing platform, and forest disturbance data were obtained using the LandTrendr algorithm. Using a machine learning random forest algorithm, the forest disturbance status and disturbance factors were explored from 2001 to 2020. The results show that the estimated disturbed forest area from 2001 to 2020 was 11,904.3 km2, accounting for 7.06% of the total area of the 11 cities in the Nanling Corridor, and the average annual disturbed area was 595.22 km2. From 2001 to 2016, the overall disturbed area increased, reaching a peak value of 1553.36 km2 in 2008, with a low value of 37.71 km2 in 2002. After 2016, the disturbed area showed a downward trend. In this study, an attribution analysis of forest disturbance factors was carried out. The results showed that the overall accuracy of forest disturbance factor attribution was as high as 82.48%, and the Kappa coefficient was 0.70. Among the disturbance factors, deforestation factors accounted for 58.45% of the total area of forest disturbance, followed by fire factors (28.69%) and building or road factors (12.85%). The regional distribution of each factor also had significant characteristics, and the Cutdown factors were mostly distributed in the lower elevations of the mountain margin, with most of them distributed in sheets. The fi

关键词： forest disturbance Landsat time-series image LandTrendr algorithm attribution of disturbances random forest algorithm

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Enhanced data-driven shear strength prediction for RC deep beams: analyzing key influencing factors and model performance

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STRUCTURES 2024年 70卷

作者： Abbas, Yassir M. Albidah, Abdulrahman S. King Saud Univ Coll Engn Dept Civil Engn POB 800 Riyadh 11421 Saudi Arabia

Accurately predicting the shear strength of reinforced concrete (RC) deep beams remains a challenging task due to the complex interplay of influencing factors. This study advances the field by integrating sophisticated machine learning (ML) techniques. A comprehensive dataset of 386 beams was compiled, covering a diverse range of geometries, materials, loading conditions, and reinforcements. Utilizing a random forest (RF) algorithm, a robust ML model was developed that significantly outperforms existing models, including those by Feng et al., in both accuracy and consistency. This model achieved a nearly perfect mean prediction (1.03) and the lowest coefficient of variation (19.4 %) for predicted versus target values. It identifies key parameters (beam width, effective depth, shear span-depth ratio, load and support plate widths, and material properties of concrete and steel) as critical factors influencing shear strength. In addition, an innovative nonlinear model based on insights from the ML model and fundamental mechanical principles was proposed. This nonlinear model, refined using data from 1650 deep beams, surpasses traditional models (e.g., those specified by ACI-318 and Eurocode-2) demonstrating superior predictive accuracy. The study illustrates the successful integration of traditional engineering principles with advanced ML techniques, highlighting the substantial potential of interdisciplinary approaches. These findings offer valuable insights into the complex relationships among material properties, reinforcement types, and shear strength, advancing our understanding and predictive capabilities for RC deep beams.

关键词： Machine learning Nonlinear modeling random forest algorithm Reinforced concrete Deep beams Shear strength

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Research on the refinement of atmospheric weighted average temperature model in Xi'an based on machine learning

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ADVANCES IN SPACE RESEARCH 2025年第1期75卷 353-366页

作者： Shen, Yu Liu, Ning Zhang, Shuangcheng Zhu, Xuejian An, Ningkang Changan Univ Coll Geol Engn & Geomat Xian 710054 Peoples R China

One of the most important parameters for calculating the atmospheric precipitable water vapor (PWV) is the atmospheric weighted mean temperature (Tm). Meteorological parameters that are measured typically constrain the accuracy and regional applicability of commonly used empirical models. When precipitable water is inverted using the Global Navigation Satellite System (GNSS), it results in erroneous Tm being obtained. Thus, in this paper, a small-scale Tm model for Xi'an is proposed by least squares method based on data provided by the European Centre for Mesoscale Weather Forecasts (ECMWF) from 2019 to 2022, while improving the functional model's capacity to fit nonlinear residuals through the random forest method and enabling it to predict peaks more accurately. It is based on a random forest to confirm the weighted average temperature model's correctness (RF Tm). When comparing the RF Tm model's projected Tm accuracy to the GPT3, UNB3, and regional Bevis models, using the 2023 ERA5 data as the experimental data, the improvements are 23.6 %, 18.6 %, and 11.4 %, respectively. With the sounding station data as the reference value, the BIAS of RF Tm is only 1.51 K. In determining the atmospheric weighted average temperatures, the two models' accuracy was comparable to that of the Long Short-Term Memory neural network model (LSTM). However, the LSTM model requires short-term forward data for practical usage, the RF Tm model compensated for the shortfall. The RF Tm model is used to invert the atmospheric precipitable water vapor at the appropriate monitoring stations using data from GNSS monitoring stations in Xi'an. It was demonstrated that the RF Tm model's PWV inversion accuracy outperformed the PWV inverted by the GPT3 model by at least 30 %. Moreover, Xi'an rainfall data integration shows that the PWV inverted by the RF Tm model reacts to brief rainfall episodes efficiently. To sum up, this study's RF Tm model provides a dependable and effective approach to meteor

关键词： Global satellite navigation system Weighted mean atmospheric temperature Precipitable water vapor random forest algorithm Zenith wet delay

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Machine learning-driven simultaneous quantification of Cd(II) and Cu(II) on Co2P/CoP heterostructure: enhanced electrochemical signals via activated Co-P electron bridge

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JOURNAL OF HAZARDOUS MATERIALS 2025年 491卷 138030页

作者： Gao, Zhi-Wei Yu, Yan Chen, Shi-Hua Li, Yong-Yu Liu, Zi-Hao Yang, Meng Li, Pei-Hua Song, Zong-Yin Huang, Xing-Jiu Chinese Acad Sci Inst Solid State Phys Key Lab Environm Opt & Technol HFIPS Hefei 230031 Peoples R China Chinese Acad Sci Inst Solid State Phys Environm Mat & Pollut Control Lab HFIPS Hefei 230031 Peoples R China Univ Sci & Technol China Dept Mat Sci & Engn Hefei 230026 Peoples R China Hefei Comprehens Natl Sci Ctr Inst Environm Hefei 230088 Peoples R China

Simultaneous quantification of multiple heavy metal ions remains a significant challenge in electrochemical methods, as complex high-throughput data from signal interference cannot be accurately analyzed through individual expertise and calibration curves. In this study, machine learning techniques were introduced to co-detect Cd(II) and Cu(II), with their electrochemical interference mechanisms explored on highly active Co2P/ CoP heterostructures. The random forest (RF) model initially identified key feature variables in response currents, which were subsequently input into the convolutional neural network (CNN) to uncover the relationship between electrochemical signals and ion concentrations, demonstrating excellent reliability with R2 values of 0.996 for both Cd(II) and Cu(II). The root mean square error (RMSE) values for Cd(II) and Cu(II) were 0.0177 and 0.0206 mu M, respectively, indicating high predictive accuracy. The experiments and theory calculations revealed that Cu(II) preferentially bonded with P sites over Cd(II). Enhanced electron transfer from Co to P atoms and weakened Cu-P bonds facilitated Cu(II) reduction and desorption from Co2P/CoP, thereby boosting electrochemical signals, while Cd(II) signals were inhibited due to active site loss. Herein, the integration of machine learning provides robust support for simultaneous detection of multiple analytes, accelerating the practical application of electrochemical methods in environmental monitoring.

关键词： Active P sites Competitive adsorption Key feature currents random forest algorithm Convolutional neural network algorithm

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Classification of Underwater Sediments in Lab Based on LiDAR Full-Waveform Data

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JOURNAL OF MARINE SCIENCE AND ENGINEERING 2025年第4期13卷 624-624页

作者： Du, Libin Wan, Dawei Meng, Xiangqian Li, Wenjing Liang, Guangxin Li, Hongyu Shandong Univ Sci & Technol Coll Marine Sci & Engn Qingdao 266590 Peoples R China

The classification of shallow sea sediments based on airborne LiDAR bathymetry represents a significant advancement in marine science and engineering. Airborne LiDAR is a highly valuable tool for the classification of seabed sediments, offering high accuracy and mobility. However, accurately classifying shallow marine sediments remains a challenging endeavor due to the difficulties associated with differentiation and the inherent limitations in accuracy. To achieve the accurate classification of underwater sediments, a feature selection method for underwater sediment classification is proposed in this paper and tested in a laboratory environment. The method inputs the original feature set into a classification algorithm that combines Sequential Forward Selection with random forests. The study demonstrates that the model achieves an overall classification accuracy of 94.1% and a Kappa coefficient of 91.11%, thereby enabling the accurate and efficient classification of underwater sediment. This approach can be employed as a supplementary technique for the precise classification of shallow marine sediments, offering valuable assistance in the examination of marine ecosystems.

关键词： underwater sediment classification sequential forward selection random forest algorithm LiDAR full-waveform data processing

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Mapping groundwater-related flooding in urban coastal regions

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JOURNAL OF HYDROLOGY 2025年 657卷

作者： Marshall, Montana Dubois, Emmanuel Cherif, Saleck Moulaye Ahmed Dubath, Candice Oumarou, Wague Mariethoz, Gregoire Perona, Paolo Swiss Fed Inst Technol Lausanne EPFL Platform Hydraul Constructions Lausanne Switzerland Reg Nouakchott Nouakchott City Hall Nouakchott Mauritania Mauritania Natl Ctr Water Resources Ctr Natl Ressource Eau CNRE Nouakchott Mauritania Univ Lausanne UNIL Inst Earth Surface Dynam Geostat Algorithms & Image Anal Res Grp GAIA Lausanne Switzerland

Coastal urban environments are facing an increasing risk of flooding from both surface water and groundwater due to globally elevated water levels, causing substantial socio-economic damages, threatening livelihoods, and hindering economic development. To date, assessing the risk of groundwater-related flooding has primarily relied on groundwater modeling and analytical solutions, but these approaches face the challenge of limited groundwater observations. In this context, spatio-temporal mapping of groundwater-related flooding in urban environments using satellite imagery offers a promising option to augment limited groundwater field measurements. However, the ability of satellite imagery to capture the dynamics of groundwater fluctuations, which can induce saturation excess flooding, has not been assessed previously. This study is the first to compare flood detection estimates derived from Sentinel-2 satellite imagery with those obtained by analyzing the difference between topography and the piezometric surface, using in-situ groundwater level measurements. This research uses the coastal city of Nouakchott, Mauritania as a case study, and covers the period from 2015 to 2023. The satellite images were processed to map flooded areas using a supervised classification (random forest algorithm) and water detection spectral indices (MNDWI and NDPI). The novelty of this study lies in the successful mapping of flooding in a data-scarce coastal urban environment. For the first time, this study demonstrated that satellite imagery, combined with machine learning methods, effectively captures the spatio-temporal dynamics of groundwater-related flooding and complements low-frequency in- situ groundwater level measurements. Results showed that flooded area peaked during the wet season (July to September) due to rainfall infiltration, and continuously decreased during the rest of the year. This work also demonstrated that mapping groundwater-related flooding based on groundwater

关键词： Saturation excess flooding Coastal city Groundwater rise Sentinel-2 random forest algorithm Supervised classification

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Establishment and Evaluation of Atmospheric Water Vapor Inversion Model Without Meteorological Parameters Based on Machine Learning

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SENSORS 2025年第2期25卷 420-420页

作者： Liu, Ning Shen, Yu Zhang, Shuangcheng Zhu, Xuejian Changan Univ Coll Geol Engn & Geomat Xian 710054 Peoples R China

Precipitable water vapor (PWV) is an important indicator to characterize the spatial and temporal variability of water vapor. A high spatial and temporal resolution of atmospheric precipitable water can be obtained using ground-based GNSS, but its inversion accuracy is usually limited by the weighted mean temperature, Tm. For this reason, based on the data of 17 ground-based GNSS stations and water vapor reanalysis products over 2 years in the Hong Kong region, a new model for water vapor inversion without the Tm parameter is established by deep learning in this paper, the research results showed that, compared with the PWV information calculated by the traditional model using Tm parameter, the accuracy of the PWV retrieved by the new model proposed in this paper is higher, and its accuracy index parameters BIAS, MAE, and RMSE are improved by 38% on average. At the same time, the PWV was inverted by radiosonde data in the study area as a reference to verify the water vapor inversion results of the new model, and it was found that the BIAS of the new model is only 0.8 mm, which has high accuracy. Further, compared with the LSTM model, the new model is more universal when the accuracy is comparable. In addition, in order to evaluate the spatial and temporal variation characteristics of the atmospheric water vapor retrieved by the new model, based on the rainstorm event caused by typhoon in Hong Kong of September 2023, the ERA5 GSMaP rainfall products and inverted PWV information were comprehensively used for analysis. The results show that the PWV increased sharply with the arrival of the typhoon and the occurrence of a rainstorm event. After the rain stopped, the PWV gradually decreased and tended to be stable. The spatial and temporal variation in the PWV have a strong correlation with the occurrence of extreme rainstorm events. This shows that the PWV inverted by the new model can respond well to extreme rainstorm events, which proves the feasibility and reliabilit

关键词： ground-based GNSS random forest algorithm atmospheric precipitable water accuracy assessment spatial and temporal characteristics of water vapor

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