The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling

Ao, Yile; Li, Hongqi; Zhu, Lei; Ali, Sikandar; Yang, Zhongguo

doi:10.1016/j.petrol.2018.11.067

Cited by 201 publications

(76 citation statements)

References 49 publications

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“…It is reasonable because RF has many advantages such as the following: (i) it can be effectively applied to largescale datasets as it provides the facility for size reduction without deleting unwanted variables from the training dataset; (ii) it can handle thousands of input features and variables at a time; (ii) it has an embedded efficient technique for estimating missing or null values. Hence, it is possible to maintain a level of accuracy (i.e., consistent performance) even when a large portion of the data is missing; (iv) it is able to perform a good parallel simulation because the number of trees generated and computed is completely independent of each other; and (v) this model can minimize errors as the results are synthesized from different "learners" (random forest trees) [46]. e results of this study are also comparable with other previous published works [46][47][48].…”

Section: Advances In Civil Engineeringmentioning

confidence: 99%

“…Hence, it is possible to maintain a level of accuracy (i.e., consistent performance) even when a large portion of the data is missing; (iv) it is able to perform a good parallel simulation because the number of trees generated and computed is completely independent of each other; and (v) this model can minimize errors as the results are synthesized from different "learners" (random forest trees) [46]. e results of this study are also comparable with other previous published works [46][47][48].…”

Section: Advances In Civil Engineeringmentioning

confidence: 99%

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Estimation of Soil Cohesion Using Machine Learning Method: A Random Forest Approach

Nguyen

Pham

2021

Advances in Civil Engineering

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Soil cohesion (C) is one of the critical soil properties and is closely related to basic soil properties such as particle size distribution, pore size, and shear strength. Hence, it is mainly determined by experimental methods. However, the experimental methods are often time-consuming and costly. Therefore, developing an alternative approach based on machine learning (ML) techniques to solve this problem is highly recommended. In this study, machine learning models, namely, support vector machine (SVM), Gaussian regression process (GPR), and random forest (RF), were built based on a data set of 145 soil samples collected from the Da Nang-Quang Ngai expressway project, Vietnam. The database also includes six input parameters, that is, clay content, moisture content, liquid limit, plastic limit, specific gravity, and void ratio. The performance of the model was assessed by three statistical criteria, namely, the correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE). The results demonstrated that the proposed RF model could accurately predict soil cohesion with high accuracy (R = 0.891) and low error (RMSE = 3.323 and MAE = 2.511), and its predictive capability is better than SVM and GPR. Therefore, the RF model can be used as a cost-effective approach in predicting soil cohesion forces used in the design and inspection of constructions.

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Section: Advances In Civil Engineeringmentioning

confidence: 99%

Section: Advances In Civil Engineeringmentioning

confidence: 99%

Estimation of Soil Cohesion Using Machine Learning Method: A Random Forest Approach

Nguyen

Pham

2021

Advances in Civil Engineering

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“…Breiman first made formal definition of the RF in 2001, which is a bagging of uncorrelated CART trees learned with randomized node optimization [70][71][72]. First, the algorithm generates N bootstrap samples for the training dataset.…”

Section: Random Forest and Agc Estimationmentioning

confidence: 99%

Estimating Forest Aboveground Carbon Storage in Hang-Jia-Hu Using Landsat TM/OLI Data and Random Forest Model

Zhang

Zhou

et al. 2019

Forests

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Dynamic monitoring of carbon storage in forests resources is important for tracking ecosystem functionalities and climate change impacts. In this study, we used multi-year Landsat data combined with a Random Forest (RF) algorithm to estimate the forest aboveground carbon (AGC) in a forest area in China (Hang-Jia-Hu) and analyzed its spatiotemporal changes during the past two decades. Maximum likelihood classification was applied to make land-use maps. Remote sensing variables, such as the spectral band, vegetation indices, and derived texture features, were extracted from 20 Landsat TM and OLI images over five different years (2000, 2004, 2010, 2015, and 2018). These variables were subsequently selected according to their importance and subsequently used in the RF algorithm to build an estimation model of forest AGC. The results showed the following: (1) Verification of classification results showed maximum likelihood can extract land information effectively. Our land cover classification yielded overall accuracies between 86.86% and 89.47%. (2) Additionally, our RF models showed good performance in predicting forest AGC, with R2 from 0.65 to 0.73 in the training and testing phase and a RMSE range between 3.18 and 6.66 Mg/ha. RMSEr in the testing phase ranged from 20.27 to 22.27 with a low model error. (3) The estimation results indicated that forest AGC in the past two decades increased with density at 10.14 Mg/ha, 21.63 Mg/ha, 26.39 Mg/ha, 29.25 Mg/ha, and 44.59 Mg/ha in 2000, 2004, 2010, 2015, and 2018. The total forest AGC storage had a growth rate of 285%. (4) Our study showed that, although forest area decreased in the study area during the time period under study, the total forest AGC increased due to an increment in forest AGC density. However, such an effect is overridden in the vicinity of cities by intense urbanization and the loss of forest covers. Our study demonstrated that the combined use of remote sensing data and machine learning techniques can improve our ability to track the forest changes in support of regional natural resource management practices.

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“…It uses ensemble learning techniques to train the model by constructing multiple decision trees (DTs). The main idea is to combine multiple DTs to determine the output for the trained model, which is more robust and efficient than relying on a single DT [19], [20].…”

Section: Introductionmentioning

confidence: 99%

Toward Effective Planning and Management Using Predictive Analytics Based on Rental Book Data of Academic Libraries

et al. 2020

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Large scale data and predictive analytics are the most challenging tasks in the field of academic data mining. Academic libraries are a great source of information and knowledge to provide a wide range of services to meet end-user requirements. Due to the rapid changes in the educational environment and availability of huge library rental book data, it is required to utilize data mining and machine learning techniques in the context of the academic library to extract and analyze underlying knowledge from rental book data, which is important to facilitate library administration to drive better future decisions to improve and manage library resources effectively. These are the following resources, such as managing future demands of the library books, selection and arrangement of the books, operational efficiency, and also improve the quality of interaction between the library and end-users, etc. This work uses and analyzes a real dataset collected from the library of Jeju National University, the Republic of Korea. The dataset contains 2,211,413 rental book records including 173671 unique book records, 57203 unique number of the rental user, and 78 data parameters. In this paper, we propose a novel model to analyze and predict library rental book data to facilitates library administration in order to plan and manage library resources effectively and provide better services to end-users. The proposed model consists of two different modules; library data analysis and prediction modules. Firstly, we use data mining techniques to analyze and extract useful underlying patterns from library rental book data, which can lead to plan and manage library resources effectively. Secondly, a novel prediction model is proposed based on Deep Neural Network (DNN), Support Vector Regressor (SVR), and Random Forest (RF) to predict future usage of the academic libraries rental books. The performance results of the implemented regression models are evaluated in terms of MAE, MSE, and RMSE. In this paper, it is found that the DNN model performs significantly better than SVR and RF. The experimentation results show that the proposed model improves the future usage of library books to facilitate library administration to plan and manage library resources effectively. Based on the proposed model results, the academic library administration can easily plan and manage resources effectively to provide quality services to end-users. INDEX TERMS Data mining, academic libraries, big data, machine learning, predictive analysis.

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The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling

Cited by 201 publications

References 49 publications

Estimation of Soil Cohesion Using Machine Learning Method: A Random Forest Approach

Estimation of Soil Cohesion Using Machine Learning Method: A Random Forest Approach

Estimating Forest Aboveground Carbon Storage in Hang-Jia-Hu Using Landsat TM/OLI Data and Random Forest Model

Toward Effective Planning and Management Using Predictive Analytics Based on Rental Book Data of Academic Libraries

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