Improving atmospheric corrosion prediction through key environmental factor identification by random forest-based model

Zhi, Yuanjie; Jin, Zhipeng; Lü, Lin; Yang, Tao; Zhou, Dianwu; Pei, Zibo; Wu, Dequan; Fu, Dongmei; Zhang, Dawei; Li, Yong

doi:10.1016/j.corsci.2020.109084

Cited by 59 publications

(26 citation statements)

References 48 publications

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“…In addition, there is no need to make variable selection. The algorithm represents a convenient method of calculating the nonlinear effects of variables and can evaluate the importance of independent variables [ 41 , 42 ]. Thus, this method has good applicability for regression analysis based on big data.…”

Section: Introductionmentioning

confidence: 99%

Factors of the Ecosystem Service Value in Water Conservation Areas Considering the Natural Environment and Human Activities: A Case Study of Funiu Mountain, China

Guo

Gao

Zhou

et al. 2021

IJERPH

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Water conservation areas play an important role in regional ecological security patterns. The Funiu Mountain water conservation area is located in the densely populated central region of China, where human disturbance to the ecosystem is strong and ecosystem services are facing a very serious situation. Identifying and evaluating the factors leading to changes in the ecosystem service value (ESV) of the Funiu Mountain water conservation area can provide scientific guidance for ecological management and sustainable development. Using multi-source data and machine learning methods, our research reveals the characteristics of the spatio-temporal variation in the ESV, constructs a system of ESV influencing factors from the comprehensive perspectives of the natural environment and human activities, and discusses the comprehensive effects of the influencing factors on the Funiu Mountain area from 2000 to 2015. The results are as follows. (1) From 2000 to 2005, the ESV increased 375 million yuan, and from 2005 to 2015, it decreased 154 million yuan. (2) Hydrological regulation, biodiversity maintenance, soil conservation, gas regulation, and climate regulation were the main types of ecosystem services in the Funiu Mountain area. (3) The ESV was influenced by the comprehensive effects of the natural environment and human activities. Population was the most important influencing factor of the ESV; in addition, the normalized difference vegetation index (NDVI), precipitation, and economic factors had important influences on the ESV. (4) With the intensification of human activities, humanistic factors have surpassed the relatively stable natural factors, becoming the main factors of the ESV. With economic development, the effect of human activities on the ESV may be further intensified in the future.

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Section: Introductionmentioning

confidence: 99%

Factors of the Ecosystem Service Value in Water Conservation Areas Considering the Natural Environment and Human Activities: A Case Study of Funiu Mountain, China

Guo

Gao

Zhou

et al. 2021

IJERPH

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“… Influencing variables in the model could be eliminated. [ 48 ] Random Forest Analysis (RFA) It can process a large number of variables and reduce their dimensionality. Results interpretation is affected.…”

Section: Methodsmentioning

confidence: 99%

Atmospheric corrosion maps as a tool for designing and maintaining building materials: A review

Santa

Tamayo

Correa

et al. 2022

Heliyon

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“…Machine learning (ML) approaches have been transforming materials research by changing the paradigm from "trial-and-error" to a data-driven methodology, especially in high-entropy alloys (Wen et al, 2019;Zhang et al, 2020aZhang et al, , 2020b, perovskite catalysts (Weng et al, 2020), shape memory alloys (Xue et al, 2016) and copper alloys (Zhang et al, 2020a(Zhang et al, , 2020b(Zhang et al, , 2021Wang et al, 2019). Corrosion behavior research has also begun to focus on the ML prediction for corrosion rate of low-alloy steel (Yan et al, 2020;Diao et al, 2021) and carbon steel (Zhi et al, 2021;Pei et al, 2020). Diao et al (2021) used both the chemical composition of low-alloy steel and environmental factors to predict the corrosion rate using the random forest algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…Diao et al (2021) used both the chemical composition of low-alloy steel and environmental factors to predict the corrosion rate using the random forest algorithm. For the atmospheric corrosion prediction of carbon steel, Zhi et al (2021) constructed support vector machine models based on the corrosion rates and environmental factors from long-term exposure tests, while Pei et al (2020) identified the relative humidity, temperature and rainfall to highly impact the random forest algorithm. For low alloy steel and carbon steel, the corrosion rate increases linearly with corrosion time.…”

Section: Introductionmentioning

confidence: 99%

“…Seawater is roughly equivalent in strength to a 3.5 Wt.% solution of sodium chloride, but the composition is much more complex, containing several major constituents and almost all naturally occurring elements. Localized corrosion usually results from a complex interaction of electrochemical and chemical reactions at the substrate/electrolyte interface, as well as from mass transport processes within the solution (Mohamed et al , 1999; Alamri, 2020; Zhu et al , 2020; Sahu et al , 2020; Frankel and Sridhar, 2008). Direct experimental evidence of the local activation process for cobalt-based alloys is still lacking, and thermodynamics and kinetics cannot provide information on the detailed course of the reaction and the rate.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting the corrosion properties of cast and hot isostatic pressed CoCrMo/W alloys in seawater by machine learning

Jiang

Yan

2022

ACMM

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Purpose Cobalt-based alloys exhibit a unique combination of wear resistance, strength and corrosion resistance. Localized corrosion of such alloys in seawater system can be several orders of magnitude faster than general corrosion, and direct experimental evidence of the local activation process is still lacking, which makes the accurate prediction for properties difficult, especially for long-term corrosion. The purpose of this study is revealing the relationship between multiple environments and corrosion properties to predict the corrosion of cobalt-based alloys. Design/methodology/approach A data-driven method for the prediction of the corrosion behavior of cast and hot isostatic-pressed CoCrMo/W alloys in seawater is proposed. The gradient boosting regression models calculate mean relative errors (MREs) of 0.160 and 0.435 by evaluating a hold-out set for breakdown potential (Eb) and maximum current density (imax), respectively, considering various compositions, synthesis methods and corrosion environments. Findings The models can be used to estimate the “unseen” cobalt-based alloy after immersion in 3.5 Wt.% NaCl solution for one, two, four and eight months to obtain high precision with MREs of 7.8% and 9.8% for Eb and imax, respectively. Originality/value Machine learning method provides novel and promising insights for the prediction of localized corrosion properties.

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Improving atmospheric corrosion prediction through key environmental factor identification by random forest-based model

Cited by 59 publications

References 48 publications

Factors of the Ecosystem Service Value in Water Conservation Areas Considering the Natural Environment and Human Activities: A Case Study of Funiu Mountain, China

Factors of the Ecosystem Service Value in Water Conservation Areas Considering the Natural Environment and Human Activities: A Case Study of Funiu Mountain, China

Atmospheric corrosion maps as a tool for designing and maintaining building materials: A review

Predicting the corrosion properties of cast and hot isostatic pressed CoCrMo/W alloys in seawater by machine learning

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