Machinability investigation of natural fibers reinforced polymer matrix composite under drilling: Leveraging machine learning in bioengineering applications

AIP Advances, Journal Year: 2024, Volume and Issue: 14(4)

Published: April 1, 2024

The growing demand for fiber-reinforced polymer (FRP) in industrial applications has prompted the exploration of natural fiber-based composites as a viable alternative to synthetic fibers. Using jute–rattan composite offers potential environmentally sustainable waste material decomposition and cost reduction compared conventional fiber materials. This article focuses on impact different machining constraints surface roughness delamination during drilling process FRP composite. Inspired by this unexplored research area, emphasizes influence various Response methodology designs experiment using drill bit material, spindle speed, feed rate input variables measure factors. technique order preference similarity ideal solution method is used optimize parameters, predicting delamination, two machine learning-based models named random forest (RF) support vector (SVM) are utilized. To evaluate accuracy predicted values, correlation coefficient (R2), mean absolute percentage error, squared error were used. RF performed better comparison with SVM, higher value R2 both testing training datasets, which 0.997, 0.981, 0.985 roughness, entry exit respectively. Hence, study presents an innovative through learning techniques.

Language: Английский

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A two-step merging strategy for incorporating multi-source precipitation products and gauge observations using machine learning classification and regression over China

Hydrology and earth system sciences, Journal Year: 2022, Volume and Issue: 26(11), P. 2969 - 2995

Published: June 15, 2022

Abstract. Although many multi-source precipitation products (MSPs) with high spatiotemporal resolution have been extensively used in water cycle research, they are still subject to various biases, including false alarm and missed bias. Precipitation merging technology is an effective means alleviate this uncertainty. However, how efficiently improve detection efficiency intensity simultaneously a problem worth exploring. This study presents two-step strategy based on machine learning (ML) algorithms, gradient boosting decision tree (GBDT), extreme (XGBoost), random forest (RF). It incorporates six state-of-the-art MSPs (GSMaP, IMERG, PERSIANN-CDR, CMORPH, CHIRPS, ERA5-Land) rain gauges the accuracy of identification estimation from 2000 2017 over China. Multiple environment variables spatial autocorrelation combined process. The first employs classification models identify wet dry days then combines regression predict amounts classified days. merged results compared traditional methods, multiple linear (MLR), ML models, gauge-based Kriging interpolation. A total 1680 (70 %) randomly chosen for model training 692 (30 performance evaluation. show that (1) (MSMPs) outperformed all original terms statistical categorical metrics, which substantially alleviates temporal biases. modified Kling–Gupta (KGE), critical success index (CSI), Heidke Skill Score (HSS) improved by 15 %–85 %, 17 %–155 21 %–166 respectively. (2) plays significant role merging, considerably improves accuracy. (3) MSMPs obtained proposed method superior MLR, interpolation, models. XGBoost algorithm recommended more large-scale data owing its computational efficiency. (4) performs better when higher-density training. it has strong robustness can also obtain than even gauge number reduced 10 % (237). provides accurate reliable under complex climatic topographic conditions. could be applied other areas well if available.

Language: Английский

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Improving rainfall-runoff modeling in the Mekong river basin using bias-corrected satellite precipitation products by convolutional neural networks

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 630, P. 130762 - 130762

Published: Jan. 24, 2024

Accurate rainfall-runoff (RR) modeling is crucial for effective Mekong River Basin (MRB) water resource management. Satellite precipitation products (SPPs) can offer valuable data such modeling; however, these often exhibit biases that may adversely affect hydrological simulations. This study aimed to improve RR using bias-corrected SPPs and the Soil Water Assessment Tool (SWAT) model MRB. A convolutional neural network-based deep learning framework was employed correct in four (TRMM, PERSIANN-CDR, CHIRPS, CMORPH), resulting respective (ADJ_TRMM, ADJ_CDR, ADJ_CHIR, ADJ_CMOR). The were compared against a gauge-based dataset terms of rainfall analysis, their performance within SWAT assessed over calibration (2004-2013) validation (2014-2015). Bias-corrected demonstrated superior with ADJ_TRMM outperforming other products. results showed satisfactory across all stations, Nash-Sutcliffe Efficiency (NSE) ranging from [0.76-0.87]. Integrating into significantly increased simulations MRB, indicated by higher NSE values [0.72-0.85] uncorrected [-0.37 0.85] at Kratie station. Besides, inconsistent between analysis observed, ADJ_CDR model. These highlight significance applications, especially areas limited ground-based data, need further research refine bias correction methods address limitations

Language: Английский

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Enhancing wildfire mapping accuracy using mono-temporal Sentinel-2 data: A novel approach through qualitative and quantitative feature selection with explainable AI

Ecological Informatics, Journal Year: 2024, Volume and Issue: 81, P. 102601 - 102601

Published: April 16, 2024

Accurate wildfire severity mapping (WSM) is crucial in environmental damage assessment and recovery strategies. Machine learning (ML) remote sensing technologies are extensively integrated employed as powerful tools for WSM. However, the intricate nature of ML algorithms often leads to 'black box' systems, obscuring decision-making process significantly limiting stakeholders' ability comprehend basis predictions. This opacity hinders efforts enhance performance risks exacerbating overfitting. present study proposes an innovative WSM approach that incorporates qualitative quantitative feature selection techniques within Explainable AI (XAI) framework. The methodology aims precision provide insights into factors contributing model decisions, thereby increasing interpretability predictions streamlining models improve performance. To achieve this objective, we SHapley Additive exPlanations (SHAP)-Forward Stepwise Selection (FSS) method demonstrate its efficacy elucidating impacts predictors on algorithm performance, accuracy, designed Utilizing post-fire imagery from Sentinel-2 (S2), analyzed ten bands generate 225 unique spectral indices utilizing five different calculations: normalized, algebraic sum, difference, ratio, product forms. Combined with original S2 bands, resulted 235 potential classifications. A random forest was subsequently developed using these optimized through extensive hyperparameter tuning, achieving overall accuracy (OA) 0.917 a Kappa statistic 0.896. most influential were identified SHAP values, FSS narrowing them down 12 critical effective WSM, evidenced by stabilized OA values (0.904 0.881, respectively). Further validation ninefold spatial cross-validation technique demonstrated method's consistent across data partitions, ranging 0.705 0.894 0.607 0.867. By providing more accurate comprehensible XAI-based research contributes broader field monitoring disaster response, underscoring analysis models' capabilities.

Language: Английский

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Identifying Flood Source Areas and Analyzing High-Flow Extremes Under Changing Land Use, Land Cover, and Climate in the Gumara Watershed, Upper Blue Nile Basin, Ethiopia

Climate, Journal Year: 2025, Volume and Issue: 13(1), P. 7 - 7

Published: Jan. 1, 2025

Changes in land use and cover (LULC) climate increasingly influence flood occurrences the Gumara watershed, located Upper Blue Nile (UBN) basin of Ethiopia. This study assesses how these factors impact return period-based peak floods, source areas, future high-flow extremes. Merged rainfall data (1981–2019) ensemble means four CMIP5 CMIP6 models were used for historical (1981–2005), near-future (2031–2055), far-future (2056–2080) periods under representative concentration pathways (RCP4.5 RCP8.5) shared socioeconomic (SSP2-4.5 SSP5-8.5). Historical LULC years 1985, 2000, 2010, 2019 projected business-as-usual (BAU) governance (GOV) scenarios 2035 2065 along with to analyze peaks. Flood simulation was performed using a calibrated Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS) model. The unit response (UFR) approach ranked eight subwatersheds (W1–W8) by their contribution magnitude at main outlet, while flow duration curves (FDCs) annual maximum (AM) series changes For observation period, values 211.7, 278.5, 359.5, 416.7, 452.7 m3/s estimated 5-, 10-, 25-, 50-, 100-year periods, respectively, condition. During this W4 W6 identified as major contributors high index values. These findings highlight need prioritize targeted interventions mitigate downstream flooding. In highest is expected SSP5-8.5 scenario combined BAU-2065 scenario. underscore importance strategic management adaptation measures reduce risks. methodology developed study, particularly application RF-MERGE studies, offers valuable insights into existing knowledge base on modeling.

Language: Английский

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Comparison of bias-corrected multisatellite precipitation products by deep learning framework

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2023, Volume and Issue: 116, P. 103177 - 103177

Published: Jan. 3, 2023

Despite satellite-based precipitation products (SPPs) providing a worldwide span with high spatial and temporal resolution, their efficiency in disaster risk forecasting, hydrological, watershed management remains challenge due to the significant dependence of rainfall on spatiotemporal pattern geographical features each area. This research proposes an effective deep learning-based solution that combines convolutional neural network benefit encoder-decoder architecture eliminate pixel-by-pixel bias enhance accuracy daily SPPs. work uses five gridded products, four which are (TRMM, CMORPH, CHIRPS, PERSIANN-CDR) one is gauge-based (APHRODITE). The Lancang-Mekong River Basin (LMRB), international basin, was chosen as region because its diverse climate spread spanning six countries. According results analyses, TRMM product exhibits better performance than other three learning model proved efficacy by successfully reducing spatial–temporal gap between SPPs APHRODITE. In addition, ADJ-TRMM performed best corrected items, followed ADJ-CDR ADJ-CHIRPS products. study's findings indicate SPP has advantages disadvantages across LMRB. aftermath discontinuation APHRODITE 2015, we believe framework will be for generating more up-to-date dependable dataset LMRB research.

Language: Английский

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Downscaling and merging multiple satellite precipitation products and gauge observations using random forest with the incorporation of spatial autocorrelation

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 632, P. 130919 - 130919

Published: Feb. 15, 2024

Language: Английский

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Development of Multi-Source Weighted-Ensemble Precipitation: Influence of bias correction based on recurrent convolutional neural networks

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 629, P. 130621 - 130621

Published: Jan. 6, 2024

Accurate precipitation information is the cornerstone of regional hydroclimatic studies, and merging data from various sources provides a means to enhance accuracy. This study aims apply technique referred as Multi-Source Weighted-Ensemble Precipitation (MSWEP) merge gauge-, satellite-, model-based products for Taiwan (MSWEP_TW). To correct known biases in satellite precipitation, long short-term memory (LSTM) emerging convolutional (ConvLSTM) networks are employed. Afterward, how correction influences performance merged assessed. The reveals that LSTM with spatial coherence scheme can show similar effectiveness ConvLSTM increasing correlations gauge-based by ∼10%. MSWEP_TW proven outperform original product (i.e., MSWEP version 2.8), higher weights satellite- over gauge-scarce regions verified. Further, this confirms provide more accurate than gauge-only interpolation, promoting advantage ungauged areas. Lastly, enhancement made directly contributes development satellite-only low latency, suggesting their usefulness near real-time applications.

Language: Английский

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Precipitation data merging via machine learning: Revisiting conceptual and technical aspects

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 637, P. 131424 - 131424

Published: May 25, 2024

The development of accurate precipitation products with wide spatio-temporal coverage is crucial for a range applications. In this context, data merging (PDM) that entails the blending satellite-based estimates ground-based measurements holds prominent position, while currently there an increasing trend in deployment machine learning (ML) algorithms such endeavors. light recent advances field, work discusses key aspects PDM problem associated with: a) conceptual formulation problem, closely related to training ML models and their predictive capacity, b) selection fused, latency final product operational applicability method, c) efficiency single-step two-step approaches, former one treating via only regression latter combined use classification algorithms. By formulating as prediction we define assess two different strategies models, termed full per time step strategy, which entail building single or several respectively. Furthermore, performance allows predictions both spatial temporal dimensions, assessed context merging. each three scenarios, popular ensemble tree-based algorithms, i.e., random forest, gradient boosting extreme algorithm, are employed resulting nine merged products. To provide empirical evidence, employ datacube composed by daily observations, reanalysis estimates, well auxiliary covariates, from 1009 uniformly distributed cells (representative sampling area 25 × km), over four countries around world (Australia, USA, India Italy). large-scale experiment indicates that: (i) strategy competitive alternative since it enables methods improved accuracy, respect metrics reproduction statistics, but also higher capability applicability, (ii) much better occurrence characteristics, reflected improvement relevant categorical metrics, probability autocorrelation coefficient, (iii) no significant difference was noticed

Language: Английский

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Estimation of the Visibility in Seoul, South Korea, Based on Particulate Matter and Weather Data, Using Machine-learning Algorithm

Aerosol and Air Quality Research, Journal Year: 2022, Volume and Issue: 22(10), P. 220125 - 220125

Published: Jan. 1, 2022

Visibility is an important indicator of air quality and any consequent meteorological climate change. Therefore, visibility in Seoul, which the most polluted city South Korea, was estimated using machine learning (ML) algorithms based on (temperature, relative humidity, precipitation) particulate matter (PM10 PM2.5) data acquired from automatic weather station, compared with observed visibility. Meteorological data, at 1-h intervals between 2018 2020, were used. Through validation each ML algorithm, extreme gradient boosting (XGB) algorithm found to be suitable for estimations (bias = 0 km, root mean square error (RMSE) 0.08 r 1 training set). Among used XGB importance PM2.5 humidity variables high (51% 19%, respectively), whereas precipitation wind speed had low (approximately 1%). The estimation accuracy test dataset good –0.11 RMSE 2.08 0.94); higher dry season –0.06 1.79 0.96) than rainy –0.17 2.34 0.91). results this study indicated a correlation previous studies. proposed method promotes accurate areas poor visibility, thus, it can assess public health quality.

Language: Английский

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