Machine Learning Regression Methods Research Articles

Predicting RNA polymerase II transcriptional elongation pausing and associated histone code.

RNA Polymerase II (Pol II) transcriptional elongation pausing is an integral part of the dynamic regulation of gene transcription in the genome of metazoans. It plays a pivotal role in many vital biological processes and disease progression. However, experimentally measuring genome-wide Pol II pausing is technically challenging and the precise governing mechanism underlying this process is not fully understood. Here, we develop RP3 (RNA Polymerase II Pausing Prediction), a network regularized logistic regression machine learning method, to predict Pol II pausing events by integrating genome sequence, histone modification, gene expression, chromatin accessibility, and protein-protein interaction data. RP3 can accurately predict Pol II pausing in diverse cellular contexts and unveil the transcription factors that are associated with the Pol II pausing machinery. Furthermore, we utilize a forward feature selection framework to systematically identify the combination of histone modification signals associated with Pol II pausing. RP3 is freely available at https://github.com/AMSSwanglab/RP3.

Application of Oversampling Techniques for Enhanced Transverse Dispersion Coefficient Estimation Performance Using Machine Learning Regression

The advection–dispersion equation has been widely used to analyze the intermediate field mixing of pollutants in natural streams. The dispersion coefficient, manipulating the dispersion term of the advection–dispersion equation, is a crucial parameter in predicting the transport distance and contaminated area in the water body. In this study, the transverse dispersion coefficient was estimated using machine learning regression methods applied to oversampled datasets. Previous research datasets used for this estimation were biased toward width-to-depth ratio (W/H) values ≤ 50, potentially leading to inaccuracies in estimating the transverse dispersion coefficient for datasets with W/H > 50. To address this issue, four oversampling techniques were employed to augment the dataset with W/H > 50, thereby mitigating the dataset’s imbalance. The estimation results obtained from data resampling with nonlinear regression method demonstrated improved prediction accuracy compared to the pre-oversampling results. Notably, the combination of adaptive synthetic sampling (ADASYN) and eXtreme Gradient Boosting regression (XGBoost) exhibited improved accuracy compared to other combinations of oversampling techniques and nonlinear regression methods. Through the combined ADASYN–XGBoost approach, it is possible to enhance the transverse dispersion coefficient estimation performance using only two variables, W/H and bed friction effects (U/U*), without adding channel sinuosity; this represents the effects of secondary currents.

The search for sparse data in molecular datasets: Application of active learning to identify extremely low volatile organic compounds

The formation of aerosol particles in the atmosphere is driven by the gas to particle conversion of extremely low volatile organic compounds (ELVOC), organic compounds with a particularly low saturation vapor pressure (pSat). Identifying ELVOCs and their chemical structures is both experimentally and theoretically challenging: Measuring the very low pSat of ELVOCs is extremely difficult, and computing pSat for these often large molecules is computationally costly. Moreover, ELVOCs are underrepresented in available datasets of atmospheric organic species, which reduces the value of statistical models built on such data. We propose an active learning (AL) approach to efficiently identify ELVOCs in a data pool of atmospheric organic species with initially unknown pSat. We assess the performance of our AL approach by comparing it to traditional machine learning regression methods, as well as ELVOC classification based on molecular properties. AL proves to be a highly efficient method for ELVOC identification with limitations on the type of ELVOC it can identify. We also show that traditional machine learning or molecular property-based methods can be adequate tools depending on the available data and desired degree of efficiency.

A low-cost machine learning process for gait measurement using an electrostatic sensors network

Continuous in-house measurement of gait of elderly People is relevant for health professionals. To be adopted by most, the system must be low-cost and non-intrusive. In this paper we present a solution for measuring the walking velocity based on a network of 4 electric potential sensors. In our experiments, we also add PIR sensors used in our previous work for comparative purposes. A temporary Depth camera is used for training the model on walking velocity. The first results presented are obtained without machine learning. Then a machine learning regression method is tested to reduce the uncertainty of the sensors. The results show that the electric potential sensors are suitable for the in-house measurement of walking speed of elderly people. The uncertainty is lower than the target of 0.15 m s-1 known as the upper limit for detecting a reduction in speed due to illness. As for the PIR sensors, electric potential sensors consume very little energy, they are inexpensive, they can be embedded and hidden in the home which makes them less -intrusive and furthermore have better accuracy.

Cuffless blood pressure estimation from photoplethysmography using deep convolutional neural network and transfer learning

Blood pressure (BP) monitoring is an essential indicator for diseases of the cardiovascular system. Early detection of abnormalities in BP helps to significantly reduce the risk of diseases such as chronic heart failure, stroke and hypertension. In this study, we propose a new framework for systolic, diastolic and mean arterial blood pressure (SBP, DBP and MBP) estimation using PPG signals and its derivatives. The proposed framework includes the extraction of deep features by pre-trained CNN models based on transfer learning using images of signal waveforms. The most distinctive features are obtained by using the RFE feature selection algorithm on the extracted deep features. The selected deep features are exposed to a range of well-known machine and deep learning regression methods. The proposed BP estimation models have been evaluated with statistical metrics, visual analytical tools and international gold standards such as AAMI and BHS. Experimental results show that the first derivative of PPG, the VPG input image, the deep features obtained with DenseNet121 and the bi-directional gated recurrent unit (Bi-GRU) algorithm provide the best BP estimation performance. The results reveal that the SBP, DBP and MBP estimation models are grade-A according to the BHS protocol and meet the AAMI standard. The presented framework has been compared with well-established studies using the MIMIC II dataset, and the comparative analysis confirms that the proposed method outperforms existing techniques. The deep learning model for BP estimation offers a highly accurate, fast and practical system, especially in the monitoring of patients at risk of hypertension.

Improved Regression Analysis with Ensemble Pipeline Approach for Applications across Multiple Domains

In this research, we introduce two new machine learning regression methods: the Ensemble Average and the Pipelined Model. These methods aim to enhance traditional regression analysis for predictive tasks and have undergone thorough evaluation across three datasets, Kaggle House Price, Boston House Price, and California Housing, using various performance metrics. The results consistently show that our models outperform existing methods in terms of accuracy and reliability across all three datasets. The Pipelined Model, in particular, is notable for its ability to combine predictions from multiple models, leading to higher accuracy and impressive scalability. This scalability allows for their application in diverse fields like technology, finance, and healthcare. Furthermore, these models can be adapted for real-time and streaming data analysis, making them valuable for applications such as fraud detection, stock market prediction, and IoT sensor data analysis. Enhancements to the models also make them suitable for big data applications, ensuring their relevance for large datasets and distributed computing environments. It is important to acknowledge some limitations of our models, including potential data biases, specific assumptions, increased complexity, and challenges related to interpretability when using them in practical scenarios. Nevertheless, these innovations advance predictive modeling, and our comprehensive evaluation underscores their potential to provide increased accuracy and reliability across a wide range of applications. The results indicate that the proposed models outperform existing models in terms of accuracy and robustness for all three datasets. The source code can be found at https://huggingface.co/DebajyotyBanik/Ensemble-Pipelined-Regression/tree/main

SPATIAL PREDICTION OF RECEIVED SIGNAL STRENGTH FOR CELLULAR COMMUNICATION USING SUPPORT VECTOR MACHINE AND K-NEAREST NEIGHBOURS REGRESSION

Abstract. Signal strength maps are of great importance for cellular system providers in network planning and operation. Accurate prediction of signal strength is important for solving problems such as link quality. In this study, Received Signal Strength (RSS) prediction model is proposed for the 900 MHz band in the Van Yüzüncü Yıl University campus environment by using machine learning regression methods such as K- Nearest Neıghbours (KNN) and Support Vector Regression (SVR) together with Geographic Information Systems. For the training of this model, signal strength values taken from the RF Spectrum Analyser at different locations and distances were used. In addition, spatial data sets such as the digital elevation model, location of base stations and measurement stations, building heights and location, and land use/cover were used in the model. The effect of these data sets on RSS power is included in the model. The model aims to predict RSS accurately, visualize the estimated signal strength, and analyze the signal field strength coverage. Different kernels from the SVR model such as Polynomial, , and Sigmoid were tested. To increase the success of the model, appropriate parameter values were selected and configured according to SVR and KNN methods. For 900 MHz, the performances of SVR and KNN models were compared and the results of the models were verified using root mean squares (RMSE). Among the measured data, the lowest prediction is found in KNN Manhattan. According to the results of the simulation was observed that the SVR model created with spatial data performs better for Signal Strength. Finally, the lowest RMSE value (1.71 dB) was obtained from the Sigmoid kernel in the best signal strength estimation SVR model. The SVR model is recommended for Campus Area signal strength estimation.

Machine Learning Approaches for Predicting the Ablation Performance of Ceramic Matrix Composites

Materials used in aircraft engines, gas turbines, nuclear reactors, re-entry vehicles, and hypersonic structures are subject to severe environmental conditions that present significant challenges. With their remarkable properties, such as high melting temperatures, strong resistance to oxidation, corrosion, and ablation, minimal creep, and advantageous thermal cycling behavior, ceramic matrix composites (CMCs) show great promise as a material to meet the strict requirements in these kinds of environments. Furthermore, the addition of boron nitride nanoparticles with continuous fibers to the CMCs can offer thermal resistivity in harsh conditions, which will improve the composites’ strength and fracture toughness. Therefore, in extreme situations, it is crucial to understand the thermal resistivity period of composite materials. To forecast the ablation performance of composites, we developed six machine learning regression methods in this study: decision tree, random forest, support vector machine, gradient boosting, extreme gradient boosting, and adaptive boosting. When evaluating model performance using metrics including R2 score, root mean square error, mean absolute error, and mean absolute percentage error, the gradient boosting and extreme gradient boosting machine learning regression models performed better than the others. The effectiveness of machine learning models as a useful tool for forecasting the ablation behavior of ceramic matrix composites was effectively explained by this study.

Detection of seizures with ictal tachycardia, using heart rate variability and patient adaptive logistic regression machine learning methods: A hospital-based validation study.

Automated seizure detection of focal epileptic seizures is needed for objective seizure quantification to optimize the treatment of patients with epilepsy. Heart rate variability (HRV)-based seizure detection using patient-adaptive threshold with logistic regression machine learning (LRML) methods has presented promising performance in a study with a Danish patient cohort. The objective of this study was to assess the generalizability of the novel LRML seizure detection algorithm by validating it in a dataset recorded from long-term video-EEG monitoring (LTM) in a Brazilian patient cohort. Ictal and inter-ictal ECG-data epochs recorded during LTM were analyzed retrospectively. Thirty-four patients had 107 seizures (79 focal, 28 generalized tonic-clonic [GTC] including focal-to-bilateral-tonic-clonic seizures) eligible for analysis, with a total of 185.5 h recording. Because HRV-based seizure detection is only suitable in patients with marked ictal autonomic change, patients with >50 beats/min change in heart rate during seizures were selected as responders. The patient-adaptive LRML seizure detection algorithm was applied to all elected ECG data, and results were computed separately for responders and non-responders. The patient-adaptive LRML seizure detection algorithm yielded a sensitivity of 84.8% (95% CI: 75.6-93.9) with a false alarm rate of .25/24 h in the responder group (22 patients, 59 seizures). Twenty-five of the 26 GTC seizures were detected (96.2%), and 25 of the 33 focal seizures without bilateral convulsions were detected (75.8%). The study confirms in a new, independent external dataset the good performance of seizure detection from a previous study and suggests that the method is generalizable. This method seems useful for detecting both generalized and focal epileptic seizures. The algorithm can be embedded in a wearable seizure detection system to alert patients and caregivers of seizures and generate objective seizure counts helping to optimize the treatment of the patients.

Machine learning analysis and nowcasting of marine fog visibility using FATIMA Grand Banks campaign measurements

Introduction: This study presents the application of machine learning (ML) to evaluate marine fog visibility conditions and nowcasting of visibility based on the FATIMA (Fog and turbulence interactions in the marine atmosphere) campaign observations collected during July 2022 in the North Atlantic in the Grand Banks area and vicinity of Sable Island, northeast of Canada.Methods: The measurements were collected using instrumentation mounted on the Research Vessel Atlantic Condor. The collected meteorological parameters were: visibility (Vis), precipitation rate, air temperature, relative humidity with respect to water, pressure, wind speed, and direction. Using all variables, the droplet number concentration was used to qualitatively indicate and assess characteristics of the fog using the t-distributed stochastic neighbor embedding projection method (t-SNE), which clustered the data into groups. Following t-SNE analysis, a correlation heatmap was used to select relevant meteorological variables for visibility nowcasting, which were wind speed, relative humidity, and dew point depression. Prior to nowcasting, the input variables were preprocessed to generate additional time-lagged variables using a 120-minute lookback window in order to take advantage of the intrinsic time-varying features of the time series data. Nowcasting of Vis time series for lead times of 30 and 60 minutes was performed using the ML regression methods of support vector regression (SVR), least-squares gradient boosting (LSB), and deep learning at visibility thresholds of Vis < 1 km and < 10 km.Results: Vis nowcasting at the 60 min lead time was best with LSB and was significantly more skillful than persistence analysis. Specifically, using LSB the overall nowcasts at Vis 1 < km and Vis 10 < km were RMSE = 0.172 km and RMSE = 2.924 km, respectively. The nowcasting skill of SVR for dense fog (Vis ≤ 400 m) was significantly better than persistence at all Vis thresholds and lead times, even when it was less skillful than persistence at predicting high visibility.Discussion: Thus, ML techniques can significantly improve Vis prediction when either observations or modelbased accurate time-dependent variables are available. The results suggest that there is potential for future ML analysis that focuses on modeling the underlying factors of fog formation.

Study of the efficiency of machine learning algorithms based on data of various rocks

Background: Absolute permeability plays an important role in studying the fluids flow in porous media during the development of oil and gas reservoirs, the injection of CO2 into reservoirs for storage, the monitoring of pollutants migration in underground aquifers, and the modeling of catalytic systems. Therefore, an accurate and fast evaluation of its values is an important task.
 Aim: The purpose of this article is to study the applicability of machine learning methods for predicting the absolute permeability of carbonate samples, as well as ways to improve the prediction of permeability.
 Materials and methods: The input data is 408 small volumes extracted from four cylindrical carbonate samples composed almost entirely of calcite. Input data includes total and connected porosity, specific surface area, radii of all and only connected pores, coordination number, throat radius and length, tortuosity, and absolute permeability. Permeability prediction is carried out using regression machine learning methods such as random forest, extremely random trees and extended gradient boosting. Parameters (data) of small volumes were determined using pore-scale modeling of water flow in their pore space applying a specialized Avizo software.
 Results: Data of small volumes extracted from fractured and non-fractured samples were analyzed, and the results showed that there are good relationships between many parameters of small volumes. For example, the connected and total porosity have a second-order polynomial relationship with a high correlation coefficient. Using the above-mentioned regression machine learning methods, absolute permeability values were predicted when input data divided into training and testing data in a ratio of 80/20 and 70/30.
 Conclusion: Using the logarithm of permeability instead of permeability itself and considering fractured and non-fractured samples separately, can increase the accuracy of absolute permeability prediction using the above-mentioned machine learning methods up to 90%. The extremely random trees method is the most accurate among the three machine learning methods considered for our task.

Forecasting Commodity Prices

A thorough project report on the study and forecasting of commodities prices via the application of machine learning and linear regression methods is summarized in this abstract. The paper explores the importance of commodity price forecasting for agricultural decision-making, discussing how changes in pricing affect everyday life and agricultural productivity. The significance of comprehending the world's commodities markets is examined, with a focus on the use of machine learning algorithms specifically, linear regression for time series forecasting. To forecast correlations between variables, the process includes procedures like feature extraction, selection, and classification. The predicted results, such as R-squared values, trend detection, and risk assessment, are presented along with a suggested system that uses machine learning to classify and comprehend data from the commodities market. The usefulness of a thorough model selection framework is discussed in the report's conclusion, with an emphasis on the principle components technique and linear regression for commodity price prediction.(Food and Agriculture Organization of the United Nations 2013) Keywords— Commodity prices, Agricultural authorities, Commodity market data, forecasting, linear regression, Python, Jupyter Notebook.

Evaluation of covid-19 mortality using machine learning regression methods based on health system indicators

Evaluation of covid-19 mortality using machine learning regression methods based on health system indicators

MAKİNE ÖĞRENMESİNDE REGRESYON MODELLERİNİN TAHMİN PERFORMANSLARININ KARŞILAŞTIRILMASI: TÜRKİYE ÜRÜN İHTİSAS BORSASI BUĞDAY ENDEKSİ ÜZERİNE BİR UYGULAMA

Agricultural commodity prices have significant impacts on economies by leading to changes and regulations in both fiscal and monetary policies. These also have effects on household economies and consumer purchasing power particularly in developing countries. Thereby, instability and variability in these prices constitute adverse effects on these economies. On the other hand, assets of the commodity markets become popular just as bonds and stocks. Because of this growing interest, needs for managing risks, stable prices and lowering transaction costs has led to establishment of the commodity exchanges. In this context, Turkey put the licensed warehousing system into operation by founding the Turkish Mercantile Exchange (TMEX) to operate trades of Electronic Warehouse Receipts (EWRs). In this study, a model including US Dollar-Turkish Lira exchange rate (USD/TRY), Brent crude-oil prices, overnight interest rate and a daily dataset for the 01/04/2021-20/02/2023 period were used to assess several machine learning regression methods in predicting the TMEX Wheat Index (TMXWHT). As verified by comparisons with actual values and considering performance evaluation criteria, all methods yielded successful outcomes, furthermore, tree-based methods revealed better overall performance.

A hybrid AHP-PROMETHEE II onshore wind farms multicriteria suitability analysis using kNN and SVM regression models in northeastern Greece

Wind energy presents a high growth potential in the EU as an emission reduction strategy and to achieve the climate neutrality goal by 2050. Wind farms suitability analysis is one of the primary goals in the spatial planning of wind energy developments. This research paper introduces a hybrid spatial multicriteria GIS-based framework that combines Analytic Hierarchy Process (AHP), PROMETHEE II and Machine Learning algorithms to determine and predict the most efficient onshore wind farm locations by generating suitability index mappings. The methodology allows to overcome PROMETHEE II limitations in raster driven suitability analysis, utilizing machine learning regression methods as the k Nearest Neighbor and Support Vector Machines to predict a graduating mapping of suitability index for wind farm locations in northeastern Greece. The best configured models presented a RMSE of 0.0344 and 0.0154 respectively, indicating a quite high predictive performance. Suitability results indicate that 56.10 % of the feasible locations in the Thrace area present a positive outranking character for the kNN model and 56.79 % for the SVR model. The proposed framework, enriched by PROMETHEE II capabilities, assists energy and spatial planners in identifying suitable sites for wind farm siting and enables rational decision making that enhances efficient wind energy investments.

Multiple Stock Prediction Based on Linear and Non-linear Machine Learning Regression Methods

In contemporary times, the pressing issue of global environmental pollution has prompted the exploration of alternative energy sources by various industries, aiming to mitigate the adverse environmental impacts caused by traditional energy production. Correspondingly, investors in the financial market have increasingly redirected their capital towards the new energy sector. Within this context, the present research endeavors to employ machine learning techniques for the prediction of Tesla's stock price. This study leverages multiple linear regression, polynomial regression, and lag models to construct models based on the datasets of TSLA, MPC, and UNG stock prices spanning the period of 2019-2020. By discerning potential patterns among these variables, the objective is to anticipate the future trajectory of TSLA stock price. According to machine learning methods, Tesla's stock price can be predicted, and the daily price of Tesla is influenced by the opening price, high price, low price and trading volume of the stock on that day. In addition, the share prices of energy companies related to Tesla also have an impact on Tesla's share price on that day. Specifically, Tesla's stock price is influenced by Natural Gas Company (UNG), which has an opposite relationship. Although common sense economics says that the crude oil market will be closely related to the new energy market. However, the results of this study demonstrated that Tesla's stock price is less influenced by Crude Oil Company (MPC).

Improving the Accuracy of Satellite-Derived Bathymetry Using Multi-Layer Perceptron and Random Forest Regression Methods: A Case Study of Tavşan Island

The spatial and spectral information brought by the Very High Resolution (VHR) and multispectral satellite images present an advantage for Satellite-Derived Bathymetry (SDB), especially in shallow-water environments with dense wave patterns. This work focuses on Tavşan Island, located in the Sea of Marmara (SoM), and aims to evaluate the accuracy and reliability of two machine learning (ML) regression methods, Multi-Layer Perceptron (MLP) and Random Forest (RF), for bathymetry mapping using Worldview-2 (WV-2) imagery. In situ bathymetry measurements were collected to enhance model training and validation. Pre-processing techniques, including water pixel extraction, sun-glint correction, and median filtering, were applied for image enhancement. The MLP and RF regression models were then trained using a comprehensive dataset that included spectral bands from the satellite image and corresponding ground truth depth values. The accuracy of the models was assessed using metrics such as Root-Mean-Square Error (RMSE), Mean Absolute Error (MAE), and R2 value. The RF regression model outperformed the MLP model, with a maximum R2 value of 0.85, lowest MAE values from 0.65 to 1.86 m, and RMSE values from 0.93 to 2.41 m at depth intervals between 6 and 9 m. These findings highlight the effectiveness of ML regression methods, specifically the RF model, for SDB based on remotely sensed images in wave-dense shallow-water environments.

Forecast of the outlet turbidity and filtered volume in different microirrigation filters and filtration media by using machine learning techniques

Different media filters and filtration media are used to eliminate suspended particles in microirrigation and therefore prevent clogging in the emitter. The water volume by filtration cycle is a parameter related to the filter and media capacity to retain particles, while turbidity is a variable related to particles in suspension in the water. Since turbidity can be measured easily and quickly, it is commonly mentioned in recommendations for the reuse of effluents in microirrigation. There are currently no models that are reliable enough to predict the filtered volume in each irrigation filter and outlet turbidity when using different filter types and media configurations. The object of this work was to propose a model that can detect early the filtered volume and the turbidity at the outlet values. This investigation presents an effective machine learning method, the Random Forest regression (RFR) in combination with the population-inspired metaheuristic optimization algorithm, called Differential Evolution (DE), for estimating the output turbidity and the filtered volume from a dataset with 1,016 samples of distinct media filters that use reclaimed effluent. The same experimental dataset was also fitted with Elastic-net, Lasso and Ridge regression machine learning methods also in combination with DE optimizer for comparison. This optimization performs the parameter tuning in the RFR using the training dataset, which considerably improves the accuracy of the regression. To achieve this, the most relevant operation input variables are tracked and analyzed: the kind of medium and filter, filtration velocity (v), height of the filter bed (H), cycle duration and the electrical conductivity for the filter inlet (ECi), pHi, dissolved oxygen (DOi), water temperature (Ti) and the input turbidity (Turbi). There are two kinds of results. Firstly, the importance ranking of the input variables on the outlet turbidity and filtered volume is presented using the DE/RFR model. Secondly, an innovative model for the prediction of the outlet turbidity and filtered volume was built and a regression with optimized parameters was done and coefficients of determination of 0.9331 and 0.8712 for filtered volume and outlet turbidity were obtained with this DE/RFR–based model, respectively. Additionally, the outcomes from the Elastic-net, Lasso and Ridge models are worse than DE/RFR–relied model estimations. The DE/RFR-based model’s strong performance was confirmed by the agreement between experimental data and the latter results.

Leaf Area Index Inversion of Spartina alterniflora Using UAV Hyperspectral Data Based on Multiple Optimized Machine Learning Algorithms

The leaf area index (LAI) is an essential biophysical parameter for describing the vegetation canopy structure and predicting its growth and productivity. Using unmanned aerial vehicle (UAV) hyperspectral imagery to accurately estimate the LAI is of great significance for Spartina alterniflora (S. alterniflora) growth status monitoring. In this study, UAV hyperspectral imagery and the LAI of S. alterniflora during the flourishing growth period were acquired. The hyperspectral data were preprocessed with Savitzky–Golay (SG) smoothing, and the first derivative (FD) and the second derivative (SD) spectral transformations of the data were then carried out. Then, using the band combination index (BCI) method, the characteristic bands related to the LAI were extracted from the hyperspectral image data obtained with the UAV, and spectral indices (SIs) were constructed through the characteristic bands. Finally, three machine learning (ML) regression methods—optimized support vector regression (OSVR), optimized random forest regression (ORFR), and optimized extreme gradient boosting regression (OXGBoostR)—were used to establish LAI estimation models. The results showed the following: (1) the three ML methods accurately predicted the LAI, and the optimal model was provided by the ORFR method, with an R2 of 0.85, an RMSE of 0.19, and an RPD of 4.33; (2) the combination of FD SIs improved the model accuracy, with the R2 value improving by 41.7%; (3) the band combinations screened using the BCI method were mainly concentrated in the red and near-infrared bands; (4) the higher LAI was distributed on the seaward side of the study area, while the lower LAI was located at the junction between the S. alterniflora and the tidal flat. This study serves as both theoretical and technological support for research on the LAI of S. alterniflora and as a solid foundation for the use of UAV remote sensing technologies in the supervisory control of S. alterniflora.

Machine-Learning-Based Approach to Optimize CO2-WAG Flooding in Low Permeability Oil Reservoirs

One of the main applications of carbon capture, utilization, and storage (CCUS) technology in the industry is carbon-dioxide-enhanced oil recovery (CO2-EOR). However, accurately and rapidly assessing their application potential remains a major challenge. In this study, a numerical model of the CO2-WAG technique was developed using the reservoir numerical simulation software CMG (Version 2021), which is widely used in the field of reservoir engineering. Then, 10,000 different reservoir models were randomly generated using the Monte Carlo method for numerical simulations, with each having different formation physical parameters, fluid parameters, initial conditions, and injection and production parameters. Among them, 70% were used as the training set and 30% as the test set. A comprehensive analysis was conducted using eight different machine learning regression methods to train and evaluate the dataset. After evaluation, the XGBoost algorithm emerged as the top-performing method and was selected as the optimal approach for the prediction and optimization. By integrating the production prediction model with a particle swarm optimizer (PSO), a workflow for optimizing the CO2-EOR parameters was developed. This process enables the rapid optimization of the CO2-EOR parameters and the prediction of the production for each period based on cumulative production under different geological conditions. The proposed XGBoost-PSO proxy model accurately, reliably, and efficiently predicts production, thereby making it an important tool for optimizing CO2-EOR design.