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Effectiveness of machine learning algorithms for estimating montane vegetation from high-resolution satellites: a case study between Al-Baha and Baljurashi area, Saudi Arabia

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INTERNATIONAL JOURNAL OF IMAGE AND DATA FUSION 2025年第1期16卷

作者： Alharbi, Basma Salama Univ Umm Alqura Dept Geog POB 715 Mecca 21955 Saudi Arabia

machine learning (ML) algorithms are used to estimate vegetation cover in humid environments. However, their effectiveness in arid and semiarid environments remains limited, particularly in rugged mountainous areas. This study aimed to evaluate the effectiveness of ML algorithms in estimating and classifying montane vegetation cover and monitoring spatiotemporal changes in vegetation cover from 2005 to 2020 using high-resolution satellite images (SPOT-5/6/7). All data were processed and analysed using the maximum likelihood classifier (MLC), random forest (RF), and support vector machine (SVM) algorithms, and accuracy assessment. The results of the ML algorithms did not significantly differ because the highest agreement percentage between the RF and SVM algorithms was 90.27%. The vegetation coverage of the study area was 37.93% according to the SVM algorithm. Approximately 21.13% of the region's total area was covered with scattered low green plants and the vegetation cover was highest in 2005 and lowest in 2010. The SVM algorithm classified the vegetation cover types with high efficiency and divided them into forests, agricultural lands with limited spread, shrubs, and grasses, covering 27%. This research highlights the necessity of remote sensing and ML techniques in monitoring montane vegetation cover and evaluating their effectiveness in improving accuracy.

关键词： Montane vegetation SPOT satellite imagery machine learning algorithm remote sensing applications

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Comparison of machine learning algorithms for Estimating Global Lake Clarity With Landsat TOA Data

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Wang, Qiang Liu, Ge Song, Kaishan Wen, Zhidan Shang, Yingxin Li, Sijia Fang, Chong Tao, Hui Chinese Acad Sci Northeast Inst Geog & Agroecol Changchun 130102 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Liaocheng Univ Sch Environm & Planning Liaocheng 252000 Peoples R China

The Secchi disk depth (SDD) is a measure of water clarity appropriate to assess a lake's trophic level. Optical satellite remote sensing has been widely used to detect water SDD at regional, continental, or global scales, with the Google Earth Engine (GEE) providing an excellent platform for fast and large-scale monitoring of SDD for global lakes. Hence, this study evaluates whether retrieving SDD for global lakes is possible by applying machine learning algorithms using Landsat top-of-atmosphere (TOA) reflectance embedded in GEE. Eleven algorithms were calibrated (5498 samples), and their performances were validated (2749 samples) with a global dataset of Landsat TOA reflectance and in situ SDD matchups. The results indicated that the smallest modeling error was derived from gradient boosting decision tree (GBDT) ( $R<^>learning =0.98$ , root-mean-square error (RMSE) =0.27 m, mean absolute percentage error (MAPE) =10.52%, and mean absolute error (MAE) =0.07 m), followed by XGBoost (XGB) and particle swarm optimization-random forest (PSO-RF). Furthermore, it was demonstrated that combining GBDT, XGB, PSO-RF, and Landsat TOA reflectance provides a robust way to monitor SDD across global lakes. Eventually, global SDD maps for 2020 were produced using Landsat Operational Land Imager (OLI) TOA imagery, with SDD changing range from 0.01 to 13.1 m (mean =3.3 m). The machine learning models in this study can be applied to archived Landsat imagery to assess SDD trends of global lakes and capture annual or seasonal variations.

关键词： Lakes Reflectivity Water quality machine learning Monitoring Reservoirs Remote sensing machine learning algorithm Secchi disk depth (SDD) top-of-atmosphere (TOA)

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Discrimination accuracy of haploid and diploid maize seeds using NIR spectroscopy coupled with different machine learning algorithms and data pretreatment methods

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SPECTROSCOPY LETTERS 2025年

作者： Kahriman, Fatih Polat, Adem Tiryaki, Ali Murat Eskizeybek, Volkan Fidan, Sertug Songur, Umut Canakkale Onsekiz Mart Univ Fac Agr Dept Field Crops Canakkale Turkiye Canakkale Onsekiz Mart Univ Fac Engn Dept Elect & Elect Engn Canakkale Turkiye Canakkale Onsekiz Mart Univ Fac Engn Dept Comp Engn Canakkale Turkiye Canakkale Onsekiz Mart Univ Fac Engn Dept Mat Sci & Engn Canakkale Turkiye BAF Elect Software Agr Inc Canakkale Technopark Canakkale Turkiye

Spectral data collected at the single seed level allows determination of the biochemical content of the seed sample, as well as to identify the seed class. NIR (Near Infrared) spectroscopy provides a more precise method for differentiating haploid and diploid seeds in maize than traditional visual examination. In this study, classification models that can be used in the separation of haploid and diploid maize seeds were developed using spectra collected between 900-1700 nm from a single maize seed. In the study, 427 diploid and 311 haploid samples obtained by crossing 10 donor materials and 3 inducer lines and separated by eye according to the Navajo marker were used. Spectral measurements were conducted over the wavelength range of 900 to 1700 nm for each sample. The robust PCA (Principal Component Analysis) method was used to detect spectral outliers. Spectral data were treated with none, FD (First Derivative), SD (Second Derivative), SNV (Standard Normal Variate), and their binary combinations. Logistic Regression, Support Vector machine with a linear kernel (SVM-C), Random Forest, and XGBoost methods were employed as machine learning techniques. The performance of the developed machine learning models was assessed using metrics such as Sensitivity, Specificity, Recall, F1-Score, and Accuracy. The Boosting method demonstrated the best performance with 94.9% accuracy, 95.1% sensitivity, 94% specificity, and an F1 Score of 96%, particularly when using raw reflectance data. These results obtained from raw data show that high accuracy can be achieved in classification models without requiring additional preprocessing steps. D2 preprocessing was found to be unsuitable for intact seed spectra, whereas SNV and D1 applications improved the classification success of other modeling techniques. The study revealed that the Boosting-Raw combination is a powerful and feasible method for classifying haploid and diploid samples.

关键词： Classification machine learning algorithm Zea mays

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Identification and experimental validation of key genes in osteoarthritis based on machine learning algorithms and single-cell sequencing analysis

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HELIYON 2024年第17期10卷 e37047页

作者： Yu, Enming Zhang, Mingshu Xi, Chunyang Yan, Jinglong Harbin Med Univ Affiliated Hosp 2 Dept Orthoped Harbin Peoples R China

Purpose: Osteoarthritis (OA) is a prevalent cause of disability in older adults. Identifying diagnostic markers for OA is essential for elucidating its mechanisms and facilitating early diagnosis. Methods: We analyzed 53 synovial tissue samples (n = 30 for OA, n = 23 for the control group) from two datasets in the Gene Express Omnibus (GEO) database. We identified differentially expressed genes (DEGs) between the groups and applied dimensionality reduction using six machine learning algorithms to pinpoint characteristic genes (key genes). We classified the OA samples into subtypes based on these key genes and explored the differences in biological functions and immune characteristics among subtypes, as well as the roles of the key genes. Additionally, we constructed a protein-protein interaction network to predict small molecules that target these genes. Further, we accessed synovial tissue sample data from the single-cell RNA dataset GSE152805, categorized the cells into various types, and examined variations in gene expression and their correlation with OA progression. Validation of key gene expression was conducted in cellular experiments using the qPCR method. Results: Four genes AGMAT, MAP3K8, PERT, and XIST, were identified as characteristic genes of OA. All can independently predict the occurrence of OA. With these genes, the OA samples can be clustered into two subtypes, which showed significant differences in functional pathways and immune infiltration. Eight cell types were obtained by analyzing the single-cell RNA data, with synovial intimal fibroblasts (SIF) accounting for the highest proportion in each sample. The key genes were found over-expressed in SIF and significantly correlated with OA progression and the content of immune cells (ICs). We validated the relative levels of key genes in OA and normal cartilage tissue cells, which showed an expression trend consistency with the bioinformatics result except for XIST. Conclusion: Four genes, AGMAT, MAP

关键词： Osteoarthritis machine learning algorithm Differentially expressed genes Key genes Immune response

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Open switch fault diagnosis of cascade H-bridge multi-level inverter in distributed power generators by machine learning algorithms

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ENERGY REPORTS 2021年 7卷 8929-8942页

作者： Ali, Murad Din, Zakiud Solomin, Evgeny Cheema, Khalid Mehmood Milyani, Ahmad H. Che, Zhiyuan Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China South Ural State Univ Dept Elect Stat Grid & Syst Power Supply Chelyabinsk Russia King Abdulaziz Univ Dept Elect & Comp Engn Jeddah 21589 Saudi Arabia

In recent years, multi-level inverters have had remarkable applications in renewable energy sources, high voltage, and other high-power applications. The multi-level inverter has advantages like minimum harmonic distortion and can operate on several voltage levels. A multi-level inverter is being utilized for multipurpose applications such as transportation, communication, industrial manufacturing, aerospace active power filter, Static Var Compensator, and machine drive. Power electronics equipment reliability is very important, and to ensure a multi-level inverter system's stable operation;it is important to detect and locate faults as quickly as possible. It is difficult to diagnose a fault in a multi-level inverter using a mathematical model because it consists of many switching devices, in this context and to improve fault diagnosis accuracy and efficiency of a cascaded multi-level inverter (CHMLI), a fault diagnosis strategy based on the probability principal component analysis (PPCA) might be utilized. Different machine learning algorithms are used to classify and diagnose the faults under different conditions in a cascaded H-bridge multi-level inverter (CHMLI). This paper presents the comparison of two different machine learning algorithms, such as support vector machine (SVM) and k-Nearest neighbors algorithm (k-NN), based on probabilistic principal component analysis (PPCA) for the effective open switch fault diagnosis in CHMLI employed in distributed generator units. PPCA is a useful technique used for optimizing and data processing without changing the input data's original properties and characteristics. Using the phase shift pulse width modulation technique, the output voltage signals under different switching fault conditions if the CHMLI are taken as fault features. Both algorithms are used to identify and locate the fault under different modes in CHMLI of distributed generator units. The proposed fault diagnosis methods are compared using simulations

关键词： Renewable energy resources Multi-level inverter Distributed generator Fault diagnosis machine learning algorithm

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Prediction of asphaltene stability in crude oils using machine learning algorithms

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2023年第1期235卷

作者： Ali, Syed Imran Lalji, Shaine Mohammadali Awan, Zahoor Qasim, Muhammad Alshahrani, Thamraa Khan, Firoz Ullah, Sami Ashraf, Almas NED Univ Engn & Technol Dept Petr Engn Karachi Pakistan NED Univ Engn & Technol Dept Food Engn Karachi Pakistan CancerClarity LLC Nyc NY USA Princess Nourah bint Abdulrahman Univ Coll Sci Dept Phys POB 84428 Riyadh 11671 Saudi Arabia King Fahd Univ Petr & Amp Minerals KFUPM Interdisciplinary Res Ctr Renewable Energy & Power Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals KFUPM KA CARE Energy Res & Innovat Ctr ERIC Dhahran 31261 Saudi Arabia NED Univ Engn & Technol Dept Humanities Karachi Pakistan

The main aim of this study is to predict asphaltene stability in crude oil as a function of its SARA (Saturates, Aromatics, Resins and Asphaltenes) values using machine learning algorithms. For this purpose, four machine learning algorithms namely, Linear discriminant analysis (LDA), Linear regression (LR), decision tree (DT), and random forest (RF) were applied to predict the asphaltene stability of 95 crude oils. SARA values of these oil samples were taken as input parameters for the algorithms. The dataset was also visualized comprehensively using different plots. For ML model implementation, 70% of the dataset was used during the training of the model while the remaining 30% dataset was used during the testing phase. For the model's reliability, a 10-fold cross-validation method was applied. Moreover, Recursive feature elimination (RFE) was also applied to determine the effect of feature elimination on the accuracy of models. The performance of all ML algorithms was determined using various statistical metrics. For all ML models, the accuracy was found in the range of 60-80% during the training and testing phase with LDA having the highest accuracy of about 78% and DT with the lowest, i.e., 62% in the training phase. On the other hand, DT accuracy was found to be the highest whereas LDA accuracy was found to be the lowest during the testing phase. The overall accuracy of LDA was observed to be the highest. The Resins feature was found to be the most important parameter for predicting the asphaltene stability in crude oils by all ML algorithms. Moreover, for models robustness, all ML models were run at two different training-to-testing ratios i.e. 80:20 and 60:40. At both ratios, all ML models generally yielded accuracy in the range of 0.6 and 0.8 during training and testing phases similar to the base case i.e. 70:30. Finally, the moderate accuracy of ML models and overall outcome of the study suggests that SARA values alone are not capable enough to determine th

关键词： Asphaltene machine learning algorithm Prediction Unstable Stable SARA

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Implementation of machine learning for the design of spiral shaped multiband monopole antenna for MICS/IEEE802.11a/IEEE802.11b applications

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JOURNAL OF ELECTROMAGNETIC WAVES AND APPLICATIONS 2025年第3期39卷 318-343页

作者： Chatterjee, Debajyoti Kundu, Anjan Kumar Univ Engn & Management Dept CSE Jaipur India Univ Calcutta Inst Radio Phys & Elect 92 APC Rd Kolkata 700009 W Bengal India

This paper presents a method to reduce the size of a compact antenna for MICS and IEEE 802.11a/b applications. Initially a monopole antenna (45 x 40 x 1.6 mm(3)) is designed for the ISM band (2.4-2.5 GHz). A spiral meandered single patch is incorporated to lower the operational frequency to 600 MHz assuring significant antenna size reduction. Further enhancements include a two-sided spiral extension and a T-shaped arm around the microstrip feed, enabling operation across three frequency bands achieving an overall 84% size reduction with improved gain. The prototype meets FCC standards for Specific Absorption Rate (SAR). To optimize gain, machine learning models along-with LASSO, Ridge, and Random Forest regression algorithm are used. The LASSO model proves most effective, achieving gains of 6.6629 dBi at 400 MHz, 7.6225 dBi at 2.45 GHz, and 8.7569 dBi at 5.5 GHz, with fractional bandwidths of 22%, 27%, and 8% respectively.

关键词： Miniaturization multiband antenna MICS/IEEE802.11a/IEEE802.11b specific absorption rate (SAR) machine learning algorithm regression model

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Information theory-guided machine learning to estimate seismic response of non-linear SDOF structures

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ENGINEERING STRUCTURES 2025年 336卷

作者： De Iuliis, M. Miceli, E. Castaldo, P. Politecn Torino Dept Struct Geotech & Bldg Engn DISEG Turin Italy

This study presents a parametric numerical analysis for the selection of the best seismic parameters characterising seismic records to model the dynamic response of non-linear single-degree-of-freedom (SDOF) structural systems by using machine learning (ML) techniques. This analysis is carried out using appropriate indices within Information Theory (IT), which allow for estimation of the amount of usable information from input data. Specifically, 400 artificial seismic excitations were generated, and, for each one, 23 seismic parameters were evaluated. Subsequently, step-by-step numerical analyses were conducted to study the seismic responses of 1000 equivalent elastic perfectly-plastic SDOF systems with different mechanical properties. The "conditional information" index was thus evaluated for both peak relative displacement and hysteretic energy response, given the input values of specific seismic parameters. The same data were treated using supervised learning techniques with 20 ML algorithms: linear regression, decision trees, support vector machine (SVM), boosted trees, bagged trees and artificial neural networks (ANN). Each analysis considered the identical set of seismic parameters, used for the conditional information index, to verify whether a higher theoretical amount of information, obtainable from the input parameters, can lead to a more efficient ML modelling. Finally, the most effective model estimation, derived from a single ML algorithm with the best combination of the input parameters, have been compared with the results of the parametric step-by-step analyses performed for some natural ground motions. The results validate the proposals and show that a higher amount of information, gained from the input parameters, generally corresponds to a better performance estimation of the ML models. This allows for the identification of which and how many seismic parameters should be considered as the best-performing combination of the input parameters for the

关键词： machine learning algorithm Artificial excitation Non-linear SDOF system Information theory Seismic parameters Peak relative displacement Hysteretic energy

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Computational analysis of performances for a hydrogen enriched compressed natural gas engine' by advanced machine learning algorithms

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FUEL 2023年第1期347卷

作者： Rao, Anas Chen, Tianhao Liu, Yongzhen Ma, Fanhua Tsinghua Univ State Key Lab Automobile Safety & Energy Beijing 100084 Peoples R China China Univ Geosci Wuhan Gemmol Inst Wuhan 430074 Peoples R China Univ Engn & Technol Dept Mech Engn Lahore 54890 Pakistan

The support vector machine along with particle swarm optimization and artificial neural network have already been implemented for the regression study of a hydrogen enriched compressed natural gas (HCNG) fueled spark ignition engine. The main issues were that the support vector machine along with particle swarm optimization requires more computational time while the artificial neural network is sensitive to the hidden layers. Therefore, these algorithms are not effectively suitable for online control of the electronic control unit. The theme of this study is to evaluate the multiple mathematical engine' models with respect to swiftness and accuracy. The experiments have been conducted under a wide range of operating conditions in a heavy-duty SI engine to obtain the data sets. The brake specific fuel consumption, engine torque and NOx emissions have been trained and analyzed by eight different machine learning algorithms along with nineteen sub-models. The effectiveness of these algorithms has been evaluated with respect to the root mean squared error and prediction time. The analysis indicate that the gaussian process regression (GPR) is optimal with respect to the computational efficiency (i.e., RMSE = 0.3823 g/kWh) and time (t < 16.019 sec). Furthermore, it operates without the iteration of the hidden layers. The mean simulation time for the gaussian process regression is 20,192 times lower as compared to the support vector machine along with particle swarm optimization algorithm. Since the gaussian process regression (GPR) is fast, optimal and insensitive to the hidden layers, so it is an ideal tool for online calibration of the electronic control unit.

关键词： machine learning algorithm Gaussian process regression Support vector machine Extreme learning machine Back propagation neural network Particle swarm optimization

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Enhanced Estimate of Chromophoric Dissolved Organic Matter Using machine learning algorithms from Landsat-8 OLI Data in the Pearl River Estuary

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REMOTE SENSING 2023年第8期15卷 1963-1963页

作者： Huang, Yihao Pan, Jiayi Devlin, Adam T. Jiangxi Normal Univ Sch Geog & Environm Nanchang 330022 Peoples R China Minist Educ Key Lab Poyang Lake Wetland & Watershed Res Nanchang 330022 Peoples R China Chinese Univ Hong Kong Inst Space & Earth Informat Sci Shatin Hong Kong Peoples R China Univ Hawaii Manoa Cooperat Inst Marine & Atmospher Res Sch Ocean & Earth Sci & Technol Honolulu HI 96822 USA Univ Hawaii Manoa Dept Oceanog Honolulu HI 96822 USA

Chromophoric Dissolved Organic Matter (CDOM) plays a critical role in the carbon and biogeochemical cycles within aquatic ecosystems. Satellite imagery can be employed to determine aquatic CDOM concentrations, highlighting the need for effective and precise algorithms for this task. In this study, a cruise survey dataset containing CDOM absorption coefficients and water-leaving radiances in the Pearl River estuary (PRE) was utilized to develop machine learning algorithms for CDOM retrieval from Landsat-8 Operational Land Imager (OLI) observations. Based on OLI wavelength bands, five bands and six band-ratios were chosen as input parameters for the machine learning models. Six machine learning models were trained to develop CDOM algorithms, including Support Vector Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN). The results indicated that, among the six machine learning models, the XGBoost algorithm performed best, with the highest R-2 value of 0.9 and the lowest CDOM root mean square error (RMSE) of 0.37 m(-1), outperforming empirical algorithms. The XGBoost algorithm identified B4/B1 as the most critical input parameter, contributing 71%, followed by B3/B2 with a 16% contribution, where B1, B2, B3, and B4 are the wavelength bands of the OLI. These two band-ratios accounted for most of the contributions, suggesting their significant role in CDOM retrieval from Landsat OLI images. By employing the developed XGBoost algorithm, CDOM spatial patterns at six instances were derived from Landsat-8 OLI image reflectance, illustrating CDOM variations in the PRE influenced by various factors. Further analysis revealed that, in the PRE, tides and winds are the primary driving forces behind the spatial and temporal variability of CDOM. At present, the exploration of employing machine learning algorithms to infer CDOM concentrations in this region remains relatively limited;therefore

关键词： machine learning algorithm Chromophoric Dissolved Organic Matter (CDOM) Landsat-8 OLI Pearl River estuary

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