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Global sensitivity analysis of water level response to harmonic aquifer disturbances through a Monte-Carlo based surrogate model with random forest algorithm

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JOURNAL OF HYDROLOGY 2024年 641卷

作者： Xing, Yixuan Liu, Quan Hu, Rui Gu, Hongbiao Taherdangkoo, Reza Ptak, Thomas Univ Gottingen Geosci Ctr D-37075 Gottingen Germany Hohai Univ Sch Earth Sci & Engn Nanjing 211100 Peoples R China Nanjing Tech Univ Coll Transportat Engn Nanjing 211816 Peoples R China TU Bergakademie Freiberg Inst Geotech D-09599 Freiberg Germany

Oscillatory water levels are often observed in monitoring wells, when aquifers are disturbed by periodical pressure diffusion (PD) sources, such as oscillatory pumping and ocean tide, and by volumetric strain (VS) sources including the far-field seismic wave and earth tide. The measured water level responses are extensively utilized to interpret aquifer properties and seismic hydrological phenomena. However, owing to wellbore effects, water levels are not always truly representative of pore pressure in aquifers, and can be significantly attenuated or even unobservable in wells. To clarify how water level responds to these disturbances, this study analyzes a global parameter sensitivity of the coupled well-aquifer model based on the assumption of a confined homogeneous aquifer. Parameter uncertainties concerning aquifer properties, source characteristics, and wellbore construction are first analyzed separately for the PD and VS sources. Subsequently, Monte-Carlo based surrogate models linking parameters and response indices are developed using the random forest algorithm. For the PD source, sensitivity results indicate that hydraulic conductivity is the most influential parameter for the yes/no responses. When hydraulic conductivity exceeds 10-4-4 m/s, water level observations basically represent pore pressure in aquifers. Significant amplitude changes and phase delay occur when the period of disturbances is less than 50 s, majorly due to water column inertia. For VS sources, water level responses are typically observed in aquifers with hydraulic conductivity larger than 10-7-7 m/s and when disturbed by a source with amplitude greater than 10-3-3 MPa. However, pronounced amplitude changes and phase shifts occur when the source period varies within 100 s. Consideration of the impact of wellbore effects is recommended, when interpreting water level responses induced by all periodic aquifer disturbances, especially in low-conductivity aquifers with conductivities less t

关键词： Global sensitivity analysis Water level response Harmonic aquifer disturbance Coupled well-aquifer model Wellbore effects random forest algorithm

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A Credit Card Fraud Detection Method Based on Mahalanobis Distance Hybrid Sampling and random forest algorithm

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IEEE ACCESS 2024年 12卷 162788-162798页

作者： Xie, Zhichao Huang, Xuan Jiangxi Univ Finance & Econ Sch Software & Internet Things Engn Nanchang 330031 Peoples R China

Currently, the continuous expansion of the credit card business and the increasingly fierce competition have made all kinds of fraud risks in the overall credit card business the biggest threat. How to detect credit card fraud accurately and effectively through machine learning algorithms has become a research hotspot and challenge in this field. This project addresses the problems of existing credit card fraud detection methods and, based on the domain knowledge of credit card fraud and machine learning theory, proposes a method based on Mahalanobis distance SMOTE-ENN hybrid sampling and random forest for credit card fraud detection. First, fraud detection experiments were conducted by selecting credit card fraud datasets published on the Kaggle platform. Then, to further confirm the effectiveness of the method, the method was used for the credit card customer default dataset in Taiwan for further experimental proof. Finally, the experimental results in this paper were compared with the best experimental results in other articles, reflecting that the method based on Mahalanobis distance SMOTE-ENN hybrid sampling and random forest can be more effective for credit card fraud detection compared with other methods. The proposed method demonstrates a strong application potential and practical effect in credit card fraud detection, which not only improves the accuracy and efficiency of fraud detection but also provides a wide range of insights and methodological references for the solution of similar problems.

关键词： Credit cards Fraud Data models Covariance matrices Sampling methods Euclidean distance Correlation Credit card fraud data balancing processing Mahalanobis distance SMOTE-ENN hybrid sampling random forest algorithm

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Salt Land classification based on pansharpening images using random forest algorithm

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JOURNAL OF SPATIAL SCIENCE 2024年第2期69卷 621-647页

作者： Diastarini, Diastarini Virtriana, Riantini Harto, Agung Budi Bandung Inst Technol Fac Earth Sci & Technol Geodesy & Geomat Engn Bandung Indonesia

The analysis and policymaking of national salt production require quality Salt Land maps with more detailed object information. This study successfully classified Salt Land into Active and Inactive based on pansharpening images using random forest algorithm. The results show: 1) the best pansharpening method is UNB PanSharp;2) the texture variables have more significant contributions to enhance classification accuracy;3) scheme 5 is the most efficient and recommended scheme in research, achieves an OA of 66.699% and a Kappa coefficient of 39.4%. This study can provide a good reference for developing Salt Land mapping and policies for practitioners or stakeholders.

关键词： Salt Land pansharpening random forest algorithm

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Rapid quantitative analysis of coal composition using laser-induced breakdown spectroscopy coupled with random forest algorithm

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ANALYTICAL SCIENCES 2024年第9期40卷 1709-1722页

作者： Du, Hongkun Ke, Shaoying Zhang, Wei Qi, Dongfeng Sun, Tengfei Shandong Univ Technol Ctr Adv Laser Mfg CALM Sch Mech Engn Zibo 255000 Shandong Peoples R China Minnan Normal Univ Sch Phys & Informat Engn Key Lab Light Field Manipulat & Syst Integrat Appl Zhangzhou 363000 Fujian Peoples R China

Coal is the primary energy source in China, widely used in energy production, industrial processes, and chemical engineering. Due to the complexity and diversity of coal quality, there is an urgent need for new technologies to achieve rapid and accurate detection and analysis of coal, aiming to improve coal resource utilization and reduce pollutant emissions. This study proposes a rapid quantitative analysis of coal using laser-induced breakdown spectroscopy combined with the random forest algorithm. Firstly, a Q-switched Nd: YAG laser at 1064 nm was employed to ablate coal samples, generating plasma, and spectral data were collected using a spectrometer. Secondly, the study explores the impact of different parameters in the preprocessing method (wavelet transform) on the predictive performance of the random forest model. It identifies elements related to coal ash content and calorific value along with their spectral information. Subsequently, to further validate the predictive performance of the model, a comparison is made with models established using support vector machine, artificial neural network, and partial least squares. Finally, under optimal parameters for spectral information preprocessing (wavelet transform with Db4 as the base function and 3 decomposition levels), a model combining wavelet transform with random forest is established to predict and analyze the ash content and calorific value of coal. The results demonstrate that the Wavelet Transform-random forest model exhibits excellent predictive performance (coal ash content: R2 = 0.9470, RMSECV = 4.8594, RMSEP = 4.8450;coal calorific value: R2 = 0.9485, RMSECV = 1.5996, RMSEP = 1.5949). Therefore, laser-induced breakdown spectroscopy combined with the random forest algorithm is an effective method for rapid and accurate detection and analysis of coal. The predicted coal composition values show high accuracy, providing insights and methods for coal composition monitoring and analysis.

关键词： Laser-induced breakdown spectroscopy random forest algorithm Coal analysis

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Application of the random forest algorithm to predict the corrosion losses of carbon steel over the first year of exposure in various regions of the world

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CORROSION ENGINEERING SCIENCE AND TECHNOLOGY 2023年第3期58卷 205-213页

作者： Gavryushina, Marina Marshakov, Andrey Panchenko, Yuliya Russian Acad Sci IPCE RAS AN Frumkin Inst Phys Chem & Electrochem Moscow Russia

The random forest (RF) algorithm was used to develop two models for predicting the first-year corrosion losses (C-1) of carbon steel in open air in various regions of the world. The first RF model built using combined databases of international programmes ISO CORRAG, MICAT and ECE/UN and tests conducted in Russia is intended for estimation of C-1 in various types of atmospheres in various regions of the world. The second RF model enables the prediction of C-1 in continental areas of the world. The accuracy of C-1 predictions by the two RF and two dose-response functions, i.e. the function presented in ISO 9223 standard and the new version for a non-marine atmosphere, was compared. The reliability of the two RF models was shown to be significantly higher than that of the dose-response functions with exception of the predictions for corrosion losses of carbon steel in regions of Russia with a cold climate.

关键词： atmospheric corrosion steel machine learning random forest algorithm dose-response function

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Investigation of Nuclear Binding Energy and Charge Radius Based on random forest algorithm

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原子能科学技术 2023年第4期57卷 704-712页

作者： CAI Boshuai YU Tianjun LIN Xuan ZHANG Jilong WANG Zhixuan YUAN Cenxi Sino-French Institute of Nuclear Engineering and Technology Sun Yat-sen UniversityZhuhai 519082China

The random forest algorithm was applied to study the nuclear binding energy and charge *** regularized root-mean-square of error(RMSE)was proposed to avoid overfitting during the training of random *** for nuclides with Z,N>7 is reduced to 0.816 MeV and 0.0200 fm compared with the six-term liquid drop model and a three-term nuclear charge radius formula,*** interest is in the possible(sub)shells among the superheavy region,which is important for searching for new elements and the island of *** significance of shell features estimated by the so-called shapely additive explanation method suggests(Z,N)=(92,142)and(98,156)as possible subshells indicated by the binding *** the present observed data is far from the N=184 shell,which is suggested by mean-field investigations,its shell effect is not predicted based on present *** significance analysis of the nuclear charge radius suggests Z=92 and N=136 as possible *** effect is verified by the shell-corrected nuclear charge radius model.

关键词： nuclear binding energy nuclear charge radius random forest algorithm

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An investigation of an athlete injury likelihood monitoring system using the random forest algorithm and DWT

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TECHNOLOGY AND HEALTH CARE 2024年第4期32卷 2657-2671页

作者： Li, Jiahao Beijing Sports Univ Competit Sports Coll Beijing Peoples R China

BACKGROUND: The main goal of sports science is to monitor sports injuries. Nevertheless, the existing sports injury monitoring projects have many expensive instruments and excessively extended monitoring periods, which makes it difficult to expand sports injury monitoring on a large scale. OBJECTIVE: The advancement of machine learning algorithms opens up new avenues for the tracking of sports injuries. METHODS: A training set of sports injuries was created using the Discrete Wavelet Transform (DWT) and random forest algorithms. Next, a basic analytic framework was created based on the lower-body movement of runners, and an athlete's injury likelihood monitoring system was established. First off, the wearable gyroscope device can efficiently plot the motion displacement curve and monitor the three-dimensional mechanics of the athlete's hips, thighs, and calves. Secondly, the system has a higher computational efficiency and an advantage over other classifier-based systems in terms of testing and training times RESULTS: The suggested system framework identifies athletes' injury propensity, providing preventive recommendations based on displacement curves, and offering a low total cost and high testing accuracy, making it easy to implement and cost-effective. CONCLUSION: All things considered, the sports injury monitoring device is very accurate and reasonably priced, making it appropriate for widespread use.

关键词： Discrete wavelet transform random forest algorithm three-dimensional mechanics gyroscope computational efficiency

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Severity Identification of Graves Orbitopathy via random forest algorithm

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HORMONE AND METABOLIC RESEARCH 2024年第10期56卷 706-711页

作者： Wang, Minghui Li, Gongfei Dong, Li Hou, Zhijia Zhang, Ju Li, Dongmei Beijing Tongren Hosp Beijing Tongren Eye Ctr Beijing Ophthalmol & Visual Sci Key Lab Beijing Peoples R China Univ Cologne Fac Med Dept Ophthalmol Cologne Germany Univ Cologne Univ Hosp Cologne Cologne Germany Univ Tubingen Dept Neurol & Stroke Tubingen Germany Capital Med Univ Beijing Tongren Hosp Beijing Tongren Eye Ctr 1 Dongjiaominxiang Beijing 100730 Peoples R China Beijing Ophthalmol Visual Sci Key Lab 1 Dongjiaominxiang Beijing 100730 Peoples R China

This study aims to establish a random forest model for detecting the severity of Graves Orbitopathy (GO) and identify significant classification factors. This is a hospital-based study of 199 patients with GO that were collected between December 2019 and February 2022. Clinical information was collected from medical records. The severity of GO can be categorized as mild, moderate-to-severe, and sight-threatening GO based on guidelines of the European Group on Graves' orbitopathy. A random forest model was constructed according to the risk factors of GO and the main ocular symptoms of patients to differentiate mild GO from severe GO and finally was compared with logistic regression analysis, Support Vector Machine (SVM), and Naive Bayes. A random forest model with 15 variables was constructed. Blurred vision, disease course, thyroid-stimulating hormone receptor antibodies, and age ranked high both in mini-decreased gini and mini decrease accuracy. The accuracy, positive predictive value, negative predictive value, and the F1 Score of the random forest model are 0.83, 0.82, 0.86, and 0.82, respectively. Compared to the three other models, our random forest model showed a more reliable performance based on AUC (0.85 vs. 0.83 vs. 0.80 vs. 0.76) and accuracy (0.83 vs. 0.78 vs. 0.77 vs. 0.70). In conclusion, this study shows the potential for applying a random forest model as a complementary tool to differentiate GO severity.

关键词： Graves orbitopathy severity random forest algorithm

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MF-RF: A detection approach based on multi-features and random forest algorithm for improved collusive interest flooding attack

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IET INFORMATION SECURITY 2023年第3期17卷 360-376页

作者： Yue, Meng Peng, Silin Feng, Wenzhi Civil Aviat Univ China Coll Safety Sci & Engn Tianjin 300300 Peoples R China Civil Aviat Univ China Coll Elect Informat & Automation Tianjin Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing Peoples R China

A new type of Collusive Interest Flooding Attack (CIFA), Improved Collusive Interest Flooding Attack (I-CIFA), which originates from CIFA with a stronger concealment, higher attack effect, lower attack cost, and wider attack range in Named Data Networking (NDN). In order to detect this attack, the present study explores new detection features and establishes a sample set of attack features with different granularities, and accordingly, the Pearson coefficient is used to validate the correlation between the proposed features and the network states. Finally, the random forest model is designed to detect the I-CIFA attack. To evaluate the performance of the approach, extensive experiments are conducted in ndnSIM platform. Test results show that the proposed detection approach outperforms other existing approaches with a detection rate of 98.1%, error rate of 1.9%, and false positive rate of 1.5%.

关键词： Other computer networks Other learning models (inc. Naive Bayes) attack multifeatures detection ndnSIM platform attack effect Information networks NDN attack range Pearson coefficient Computer network security Data security I-CIFA attack detection improved collusive interest flooding attack named data networking computer network security MF-RF computer crime random forests Internet performance evaluation random forest algorithm attack cost Computer communications

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Research on Prediction of Dam Seepage and Dual Analysis of Lag-Sensitivity of Influencing Factors Based on MIC Optimizing random forest algorithm

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KSCE JOURNAL OF CIVIL ENGINEERING 2023年第2期27卷 508-520页

作者： Liu, Yongtao Zheng, Dongjian Wu, Xin Chen, Xingqiao Georgakis, Christos T. Qiu, Jianchun Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China Hohai Univ State Key Lab Hydrol Water Resources & Hydraul Eng Nanjing 210098 Peoples R China Aarhus Univ Dept Civil & Architectural Engn DK-8000 Aarhus C Denmark Yangzhou Univ Coll Hydraul Sci & Engn Yangzhou 225009 Peoples R China

The seepage of the dam is an important representation of the operation characteristics of the dam, and there are many factors affecting the seepage with a certain lag. It is still difficult to predict its change and sensitivity because of complex operating conditions. At present, the lag-sensitivity of influence factors of the dam seepage has not been studied. The time series influence factors of seepage are determined by HTRT (hydrostatic-thermal-rainfall-time) model in this paper. To avoid the pseudo fitting of conventional methods, HTRT model nested random forest algorithm is used to establish the predicting model of the dam seepage. And MIC algorithm is used to achieve the dual purposes of time lag and sensitivity analysis. Firstly, the time lag of relationship between seepage and its influencing factors is characterized, and the most appropriate lag time of the HTRT model factors is determined. Secondly, independent correlation analysis on all influencing factors is carried out and the sensitivity of each factor is analyzed by MIC. Meanwhile, the sensitivity of the factors to seepage is quantitatively analyzed by the two parameters of %IncMSE and IncNodePurity of RF algorithm. The sensitivity of influencing factors is analyzed by comparing MIC results with RF results. Combined with the case, taking the error of fitting prediction as the evaluation index of seepage prediction, the prediction accuracy of MIC-RF model, RF model and MIC-BPNN (Back Propagation neural network) model is calculated and compared. Case study showed that MIC- RF monitoring model has high prediction accuracy, strong adaptability and high robustness in dam seepage, and the sensitivity and time lag of influencing factors can be quantitatively analyzed. The water pressure and rainfall of the lag time are 14 days and 16 days respectively. The sensitivity study of the time series influencing factors of seepage showed that the water pressure component is the main controlling factor of seepage, a

关键词： Seepage prediction Dam MIC random forest algorithm Influencing factors Time lag

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