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Maize crop residue cover mapping using Sentinel-2 MSI data and random forest algorithms

国际水土保持研究（英文） 2025年第1期13卷 189-202页

作者： Jia Du Pierre-Andre Jacinthe Kaishan Song Longlong Zhang Boyu Zhao Hua Liu Yan Wang Weijian Zhang Zhi Zheng Weilin Yu Yiwei Zhang Dapeng Jiang Northeast Institute of Geography and Agroecology CASChangchunJilin130102China Department of Earth Sciences Indiana University-Purdue UniversityIndianapolisIN46202USA

The return of crop residues to cultivated fields has numerous agronomic and soil quality benefits and,therefore,the areal extent of crop residue cover(CRC)could provide a rapid measure of the sustainability of agricultural production systems in a *** the limitations of traditional CRC methods,a new method is proposed for estimating the spatial and temporal distribution of maize residue cover(MRC)in the Jilin Province,NE *** method used random forest(RF)algorithms,13 tillage indices and 9 textural feature indicators derived from Sentinel-2 *** tillage indices with the best predictive performance were STI and NDTI(R2 of 0.85 and 0.84,respectively).Among the texture features,the best-fitting was Band8AMean-5*5(R2 of 0.56 and 0.54 for the line-transect and photographic methods,respectively).Based on MSE and InNodePurity,the optimal combination of RF algorithm for the line-transect method was STI,NDTI,NDI7,NDRI5,SRNDI,NDRI6,NDRI7 and Band3Mean-3****,the optimal combination of RF algorithm for the photographic method was STI,NDTI,NDI7,SRNDI,NDRI6,NDRI5,NDRI9 and Band3Mean-3**** distribution of MRC in the Jilin Province,estimated using the RF prediction model,was higher in the central and southeast sections than in the *** distribution was in line with the spatial heterogeneity of maize yield in the *** findings showed that the RF algorithm can be used to map regional MRC and,therefore,represents a useful tool for monitoring regional-scale adoption of conservation agricultural practices.

关键词： random forest algorithm Maize residue cover Sentinel-2 remotely sensed data Line-transect method Photographic method

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A study on financial early warning for technology companies incorporating big data and random forest algorithms

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INTERNATIONAL JOURNAL OF GRID AND UTILITY COMPUTING 2024年第3-4期15卷 343-351页

作者： Wang, Xuemei Shandong Foreign Trade Vocat Coll Dept Accounting & Finance Qingdao Shandong Peoples R China

This study establishes a model to predict the financial risk and the possibility of fraud of technology enterprises by using the random forest algorithm and SMOTE algorithm. By testing and identifying data sets, the model shows good accuracy and efficiency, with an ACC index of 97.2%, an accuracy of 96.8%, a recall rate of 1, and an F1 index of 98.4%. Compared with the traditional model, the ratio of cash income to main business income is the highest, which shows that the prediction accuracy of this model is obviously better than that of the traditional model. The results show that this model will have some practical value in evaluating the financial status of science and technology enterprises.

关键词： financial fraud financial malpractice machine learning random forest algorithm SMOTE algorithm

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Solar Power Plant Capacity Monitoring Using random forest Machine Learning algorithm

Solar Power Plant Capacity Monitoring Using Random Forest Ma...

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2024 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2024

作者： Ogar, Emmanuel Ede Chaitusaney, Surachai Benjapolakul, Watit Center of Excellence in Artificial Intelligence Machine Learning and Smart Grid Technology Chulalongkorn University Faculty of Engineering Department of Electrical Engineering Bangkok Thailand

ISBN: (纸本)9798350352290

The natural abundance of solar energy and its favorable environmental impacts have led to growing acceptance and utilization as the world transitions from a fossil fuel-based economy to a cleaner, more sustainable, and ecologically friendly alternative. An increasing number of solar photovoltaic projects have emerged to capitalize on these benefits. However, factors such as weather conditions, exposure to natural elements, device deterioration, improper installations, and maintenance concerns can eventually compromise these systems' power-generating performance and dependability over time. This study demonstrates how the random forest machine learning classifier can be used to monitor and categorize the performance of solar PV plants to ensure their reliability and efficiency. The study collected and preprocessed a field dataset containing various environmental and electrical parameters. These parameters include plant capacity, active power, generating capacity, ambient temperature, global radiation, and module temperature. This data was used to train a random forest machine learning algorithm to categorize the operational condition of the solar power plant. The results demonstrate that the model efficiently classified the operational condition of the solar power plant by effectively identifying conditions such as optimal mode, moderate sub-optimal mode, severe sub-optimal mode, critical mode, emergency mode, and offline mode, achieving 99.92% accuracy on the test set. The proposed method provides an effective technique for estimating and monitoring the generating capacity and operational condition of solar power plants in near real time, thereby serving as a distinctive instrument for boosting the efficiency and reliability of solar power generation. © 2024 IEEE.

关键词： Artificial Intelligence Capacity monitoring Machine learning random forest algorithm Solar cell

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Hydroponic using Deep Water Culture for Lettuce Farming using random forest Compared with Decision Tree algorithm 8

Hydroponic using Deep Water Culture for Lettuce Farming usin...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Jegan, D. Surendran, R. Madhusundar, N. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350367904

This research aims to optimize lettuce cultivation in a Deep Water Culture (DWC) hydroponic system by leveraging machine learning techniques. By analyzing various parameters such as temperature, pH, and nutrient concentration, the study seeks to identify optimal conditions for maximizing lettuce growth and yield. A random forest algorithm is employed to predict optimal parameter settings and compare its performance with a Decision Tree algorithm. The random forest's ensemble approach is expected to enhance prediction accuracy and robustness. By optimizing hydroponic parameters through machine learning, this research contributes to sustainable and efficient agriculture, reducing resource consumption and maximizing crop yield. © 2024 IEEE.

关键词： decision tree algorithm deep-water culture hydroponic lettuce farming random forest algorithm

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Performance of ANN, random forest and XGBoost methods in predicting the flexural properties of wood beams reinforced with carbon-FRP

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WOOD MATERIAL SCIENCE & ENGINEERING 2024年第3期20卷 657-668页

作者： Turker, Yasemin Simsek Kilincarslan, Semsettin Ince, Ebru Yilmaz Suleyman Demirel Univ Fac Engn & Nat Sci Dept Civil Engn Isparta Turkiye Isparta Univ Appl Sci Comp Programming Isparta Turkiye

Wooden material can be used in different areas due to its various positive properties. Glued Laminated wooden elements (glulam) are wood composite materials widely used especially in the construction industry. Carbon fiber-reinforced polymers (FRP) are widely used to increase the bearing capacity values of glulam beams and improve their overall load-displacement behavior. This study was carried out in two stages. In the first stage, the bending properties of glulam timbers of different sizes with wide spans reinforced with carbon fiber-reinforced polymers were experimentally examined. In the next stage, the obtained data were predicted with three different machine learning techniques (ANN, random forest and XGBoost). As a result of the study, it was determined that as the section dimensions increased, the bending properties increased, and the reinforcement was effective by approximately 22%. All three different prediction techniques used could make predictions with high accuracy. However, it was determined that the best prediction was made with random forest (R2: 0.9892). Therefore, the bending properties of reinforced beams of different sizes can be predicted with machine learning (ML) techniques.

关键词： Wood structures FRP random forest algorithm artificial neural network XGBoost machine learning techniques

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Evaluating total inorganic nitrogen in coastal waters through fusion of multi-temporal RADARSAT-2 and optical imagery using random forest algorithm

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2014年第1期33卷 192-202页

作者： Liu, Meiling Liu, Xiangnan Li, Jin Ding, Chao Jiang, Jiale China Univ Geosci Sch Informat Engn Beijing 100083 Peoples R China Natl Marine Data & Informat Serv Tianjin 300171 Peoples R China

Satellites routinely provide frequent, large-scale, near-surface views of many oceanographic variables pertinent to plankton ecology. However, the nutrient fertility of water can be challenging to detect accurately using remote sensing technology. This research has explored an approach to estimate the nutrient fertility in coastal waters through the fusion of synthetic aperture radar (SAR) images and optical images using the random forest (RF) algorithm. The estimation of total inorganic nitrogen (TIN) in the Hong Kong Sea, China, was used as a case study. In March of 2009 and May and August of 2010, a sequence of multi-temporal in situ data and CCD images from China's HJ-1 satellite and RADARSAT-2 images were acquired. Four sensitive parameters were selected as input variables to evaluate TIN: single-band reflectance, a normalized difference spectral index (NDSI) and Hy and VH polarizations. The RF algorithm was used to merge the different input variables from the SAR and optical imagery to generate a new dataset (i.e., the TIN outputs). The results showed the temporal-spatial distribution of TIN. The TIN values decreased from coastal waters to the open water areas, and TIN values in the northeast area were higher than those found in the southwest region of the study area. The maximum TIN values occurred in May. Additionally, the estimation accuracy for estimating TIN was significantly improved when the SAR and optical data were used in combination rather than a single data type alone. This study suggests that this method of estimating nutrient fertility in coastal waters by effectively fusing data from multiple sensors is very promising. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Multisensor data fusion random forest algorithm Total inorganic nitrogen RADARSAT-2 images Optical images

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Predicting the performance and emissions of an HCCI-DI engine powered by waste cooking oil biodiesel with Al2O3 and FeCl3 nano additives and gasoline injection - A random forest machine learning approach

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FUEL 2024年第PartB期357卷

作者： Leo, G. M. Lionus Jayabal, Ravikumar Srinivasan, D. Das, M. Chrispin Ganesh, M. Gavaskar, Thodda St Josephs Coll Engn Dept Mech Engn Chennai Tamil Nadu India SIMATS Saveetha Sch Engn Dept Thermal Engn Chennai Tamil Nadu India Loyola Inst Technol Dept Mech Engn Chennai Tamil Nadu India St Josephs Inst Technol Dept Mech Engn Chennai Tamil Nadu India

This study presents a novel method for predicting the performance and emissions of a Homogeneous Charge Compression Ignition-Direct Injection (HCCI-DI) engine fuelled by waste cooking oil biodiesel (WCOB) blended with Aluminum oxide (Al2O3) and Ferric chloride (FeCl3) nano additives and premixed with gasoline. The test fuels considered were pure diesel, WCOB, B50 (a 50-50 blend of diesel and WCOB), and the nano-additives at concentrations of 50 ppm and 100 ppm. All experiments were performed on a 4.4 kW HCCI-DI engine operating at 1500 rpm. The results revealed that, utilization of WCOB showed a substantial reduction in emissions. Hydrocarbon (HC), carbon monoxide (CO) and smoke emissions decreased by 54.17 %, 50 %, and 22.69 %. Nevertheless, oxides of nitrogen (NOx) emissions increased by 18.32 %. When introducing gasoline (20 %) as a premix in HCCI-DI engines, a favourable shift in emission and efficiency metrics was observed. The brake thermal efficiency (BTE) increased 4.23 %. Moreover, NOx and smoke emissions decreased by 4.3 % and 39.21 %. Furthermore, compared to conventional diesel-fueled combustion, the integration of the nano additives manifested promising results. After introducing Al2O3 into neat fuel, The BTE improved by 11.27 % for direct injection (DI) combustion and 18.31 % for HCCI-DI combustion. Additionally, there was a marked decrease in exhaust emissions. FeCl3 nano additive into the test fuel significantly reduced HC, CO, and smoke emissions. Furthermore, the random forest machine learning approach demonstrated an extensive accuracy in forecasting both engine performance and emissions for the HCCI-DI engine. The innovative combination of cutting-edge machine learning techniques and combustion technology used. This model to understand the complicated correlations between input parameters and engine outputs and account for the system's intrinsic non-linearities and interactions. This dramatically improves standard prediction approaches, often ass

关键词： Waste cooking oil biodiesel Aluminum oxide nano additive Ferric chloride nano additive Emissions random forest algorithm

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Optimizing maize germination forecasts with random forest and data fusion techniques

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PEERJ COMPUTER SCIENCE 2024年 10卷 e2468-e2468页

作者： Wu, Lili Xing, Yuqing Yang, Kaiwen Li, Wenqiang Ren, Guangyue Zhang, Debang Fan, Huiping Henan Agr Univ Coll Sci Zhengzhou Peoples R China Henan Univ Sci & Technol Coll Food & Bioengn Luoyang Peoples R China Zhengzhou Wangu Machinery Co LTD Zhengzhou 450041 Peoples R China Henan Agr Univ Coll Food Sci & Technol Zhengzhou Peoples R China

Traditional methods for detecting seed germination rates often involve lengthy experiments that result in damaged seeds. This study selected the Zheng Dan-958 maize variety to predict germination rates using multi-source information fusion and a random forest (RF) algorithm. Images of the seeds and internal cracks were captured with a digital camera. In contrast, the dielectric constant of the seeds was measured using a fl at capacitor and converted into voltage readings. Features such as color, shape, texture, crack count, and normalized voltage were used to form feature vectors. Various prediction algorithms, including random forest (RF), radial basis function (RBF), neural networks (NNs), support vector machine (SVM), and extreme learning machine (ELM), were developed and tested against standard germination experiments. The RF model stood out, with a training time of 5.18 s and the highest accuracy of 92.88%, along with a mean absolute error (MAE) of 0.913 and a root mean square error (RMSE) of 1.163. The study concluded that the RF model, combined with multi-source information fusion, offers a feasible and nondestructive method for quickly and accurately predicting maize seed germination rates.

关键词： Maize seeds Germination rate Non-destructive prediction random forest algorithm Image processing

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Intrusion detection system based firefly algorithm-random forest for cloud computing

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第24期34卷 e7220-e7220页

作者： Moharamkhani, Elaheh Hendi, Mohammad Yahyaei Feriz Bandar, Eisa Izadkhasti, Amir Raza, Rzgar Sirwan Inst Higher Educ Saeb Dept Comp Engn Abhar Iran Islamic Azad Univ Dept Comp Engn South Tehran Branch Tehran Iran Iran Univ Sci & Technol Dept Comp Engn Tehran Iran Islamic Azad Univ Dept Comp Engn Isfahan Khorasgan Branch Esfahan Iran Univ Human Dev Coll Sci & Technol Dept Comp Sci Sulaymaniyah Iraq

Cloud computing is an active environment for accessing shared resources. Security in this environment, is one of the most important issues and concerns. An intrusion detection system is used to provide security and privacy. The intrusion detection system distinguishes between attacks and normal activities and prevents attacks on the cloud environment and notifies managers and cloud providers by creating alerts. Intrusion detection systems are usually implemented with different solutions. In this article, we present an intrusion detection system based on firefly algorithm and random forest algorithm that has the ability to detect intrusion with high accuracy in cloud computing, the results of model testing and evaluation, efficient and appropriate indicates the existence of the proposed method.

关键词： feature selection firefly algorithm intrusion detection system machine learning random forest algorithm

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Using quantile mapping and random forest for bias-correction of high-resolution reanalysis precipitation data and CMIP6 climate projections over Iran

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INTERNATIONAL JOURNAL OF CLIMATOLOGY 2024年第12期44卷 4495-4514页

作者： Raeesi, Maryam Zolfaghari, Ali Asghar Kaboli, Seyed Hasan Rahimi, Mohammad de Vente, Joris Eekhout, Joris P. C. Semnan Univ Fac Desert Studies Semnan Iran Spanish Res Council Soil & Water Conservat Res Grp CEBAS CSIC Murcia Spain

Climate change is expected to cause important changes in precipitation patterns in Iran until the end of 21st century. This study aims at evaluating projections of climate change over Iran by using five climate model outputs (including ACCESS-ESM1-5, BCC-CSM2-MR, CanESM5, CMCC-ESM2 and MRI-ESM2-0) of the Coupled Model Intercomparison Project phase 6 (CMIP6), and performing bias-correction using a novel combination of quantile mapping (QM) and random forest (RF) between the years 2015 and 2100 under three shared socioeconomics pathways (SSP2-4.5, SSP3-7.0 and SSP5-8.5). First, bias-correction was performed on ERA5-Land reanalysis data as reference period (1990-2020) using the QM method, then the corrected ERA5-Land reanalysis data was considered as measured data. Based on the corrected ERA5-Land reanalysis data (1990-2020) and historical simulations (1990-2014), the future projections (2015-2100) were also bias-corrected utilizing the QM method. Next, the accuracy of the QM method was validated by comparing the corrected ERA5-Land reanalysis data with model outputs for overlapping years between 2015 and 2020. This comparison revealed persistent biases;hence, a combination of QM-RF method was applied to rectify future climate projections until the end of the 21st century. Based on the QM result, CMCC-ESM2 revealed the highest RMSE in both SSP2-4.5 and SSP3-7.0 amounting to 331.74 and 201.84 mmyear(-1), respectively. Particularly, the exclusive use of the QM method displayed substantial errors in projecting annual precipitation based on SSP5-8.5, notably in the case of ACCESS-ESM1-5 (RMSE = 431.39 mmyear(-1)), while the RMSE reduced after using QM-RF method (197.75 mmyear(-1)). Obviously, a significant enhancement in results was observed upon implementing the QM-RF combination method in CMCC-ESM2 under both SSP2-4.5 (RMSE = 139.30 mmyear(-1)) and SSP3-7.0 (RMSE = 151.43 mmyear(-1)) showcasing approximately red

关键词： annual precipitation projection bias-correction methods data mining ERA5-Land reanalysis data quantile mapping random forest algorithm

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