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Application of machine learning models in groundwater quality assessment and prediction: progress and

Frontiers of Environmental Science & Engineering 2022, Volume 18, Issue 3, doi: 10.1007/s11783-024-1789-2

Abstract:

● The application of ML in groundwater quality assessment and prediction

Keywords： Groundwater quality assessment Groundwater quality prediction Machine learning Groundwater modeling

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Spatial prediction of soil contamination based on machine learning: a review

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 8, doi: 10.1007/s11783-023-1693-1

Abstract:

● A review of machine learning (ML) for spatial prediction of soil

Keywords： Soil contamination Machine learning Prediction Spatial distribution

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Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

Frontiers of Medicine 2022, Volume 16, Issue 3, Pages 496-506 doi: 10.1007/s11684-021-0828-7

Abstract: The fracture risk of patients with diabetes is higher than those of patients without diabetes due to hyperglycemia, usage of diabetes drugs, changes in insulin levels, and excretion, and this risk begins as early as adolescence. Many factors including demographic data (such as age, height, weight, and gender), medical history (such as smoking, drinking, and menopause), and examination (such as bone mineral density, blood routine, and urine routine) may be related to bone metabolism in patients with diabetes. However, most of the existing methods are qualitative assessments and do not consider the interactions of the physiological factors of humans. In addition, the fracture risk of patients with diabetes and osteoporosis has not been further studied previously. In this paper, a hybrid model combining XGBoost with deep neural network is used to predict the fracture risk of patients with diabetes and osteoporosis, and investigate the effect of patients’ physiological factors on fracture risk. A total of 147 raw input features are considered in our model. The presented model is compared with several benchmarks based on various metrics to prove its effectiveness. Moreover, the top 18 influencing factors of fracture risks of patients with diabetes are determined.

Keywords： XGBoost deep neural network healthcare risk prediction

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Reliability prediction and its validation for nuclear power units in service

Jinyuan SHI,Yong WANG

Frontiers in Energy 2016, Volume 10, Issue 4, Pages 479-488 doi: 10.1007/s11708-016-0425-7

Abstract: In this paper a novel method for reliability prediction and validation of nuclear power units in serviceThe accuracy of the reliability prediction can be evaluated according to the comparison between the predictedFurthermore, the reliability prediction method is validated using the nuclear power units in North American

Keywords： nuclear power units in service reliability reliability prediction equivalent availability factors

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Position-varying surface roughness prediction method considering compensated acceleration in milling

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 4, Pages 855-867 doi: 10.1007/s11465-021-0649-z

Abstract: Aiming at surface roughness prediction in the machining process, this paper proposes a position-varyingsurface roughness prediction method based on compensated acceleration by using regression analysis.i>R-square proving the effectiveness of the filtering features, is selected as the input of the predictionMoreover, the prediction curve matches and agrees well with the actual surface state, which verifies

Keywords： surface roughness prediction compensated acceleration milling thin-walled workpiece

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Trend prediction technology of condition maintenance for large water injection units

Xiaoli XU, Sanpeng DENG

Frontiers of Mechanical Engineering 2010, Volume 5, Issue 2, Pages 171-175 doi: 10.1007/s11465-009-0091-0

Abstract: Trend prediction technology is the key technology to achieve condition-based maintenance of mechanicalTo ensure the normal operation of units and save maintenance costs, trend prediction technology is studiedThe main methods of the technology are given, the trend prediction method based on neural network isThe industrial site verification shows that the proposed trend prediction technology can reflect the

Keywords： water injection units condition-based maintenance trend prediction

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Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

Frontiers of Structural and Civil Engineering Pages 994-1010 doi: 10.1007/s11709-023-0942-5

Abstract: Developing prediction models to support drivers in performing rectifications in advance can effectivelysubsequently, they are preprocessed and used to establish GRU-based multivariate multistep-ahead direct predictionIn addition, the effects of the activation function and input time-step length on the prediction performance

Keywords： dynamic prediction moving trajectory pipe jacking GRU deep learning

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Liquefaction prediction using support vector machine model based on cone penetration data

Pijush SAMUI

Frontiers of Structural and Civil Engineering 2013, Volume 7, Issue 1, Pages 72-82 doi: 10.1007/s11709-013-0185-y

Abstract: A support vector machine (SVM) model has been developed for the prediction of liquefaction susceptibilityThis paper examines the potential of SVM model in prediction of liquefaction using actual field coneUsing cone resistance ( ) and cyclic stress ratio ( ), model has been developed for prediction of liquefactionto simplify the model, requiring only two parameters ( and maximum horizontal acceleration ), for predictionThe study shows that SVM can be used as a practical tool for prediction of liquefaction potential, based

Keywords： earthquake cone penetration test liquefaction support vector machine (SVM) prediction

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An energy consumption prediction approach of die casting machines driven by product parameters

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 4, Pages 868-886 doi: 10.1007/s11465-021-0656-0

Abstract: The energy consumption prediction of die casting machines can support energy consumption quota, processTo fill this gap, this paper proposes an energy consumption prediction approach for die casting machinesFirstly, the system boundary of energy consumption prediction is defined, and subsequently, based onConsequently, a systematic energy consumption prediction approach for die casting machines, involvingThe results show that the prediction accuracy of production time and energy consumption reached 91.64%

Keywords： die casting machine energy consumption prediction product parameters

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Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 4, Pages 523-535 doi: 10.1007/s11705-021-2083-5

Abstract: Solubility prediction, as an alternative to experiments which can reduce waste and improve crystallizationHerein we used seven descriptors based on understanding dissolution behavior to establish two solubility predictionThe solubility data of 120 active pharmaceutical ingredients (APIs) in ethanol were considered in the predictionFurthermore, a comparison with traditional prediction methods including the modified solubility equationThe highest accuracy shown by the testing set proves that the ML models have the best solubility prediction

Keywords： solubility prediction machine learning artificial neural network random decision forests

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The prediction of adsorption isotherms of ester vapors on hypercrosslinked polymeric adsorbent

Liuyan WU,Lijuan JIA,Xiaohan LIU,Chao LONG

Frontiers of Environmental Science & Engineering 2016, Volume 10, Issue 3, Pages 482-490 doi: 10.1007/s11783-015-0826-6

Abstract: Adsorption isotherms of methyl acetate, ethyl acetate, propyl acetate, isopropyl acetate and ethyl propionate on hypercrosslinked polymeric resin (ND-100) were measured at 303K, 318K and 333K,respectively, and well fitted by Dubinin–Astakhov (DA) equation. The plots of the adsorbed volume ( ) versus the adsorption potential ( ) at three different temperatures all fell basically onto one single curve for every ester. A predicted model based on DA equation was obtained on the basis of adsorption equilibrium data of methyl acetate, ethyl acetate and ethyl propionate at 318K. The model equation successfully predicted the adsorption isotherms of methyl acetate, ethyl acetate and ethyl propionate on ND-100 at 303K, and 333K, and also gave accurate predictive results for adsorption isotherms of the other two ester compounds (propyl acetate and isopropyl acetate) on ND-100 at 303K, 318K and 333K. The results proved the effectiveness of DA model for predicting the adsorption isotherms of ester compounds onto ND-100. In addition, the relationship between physico-chemical properties of adsorbates and their adsorption properties was also investigated. The results showed that molecular weight, molar volume and molar polarizability had good linear correlations with the parameter (which represents adsorption characteristic energy) of DA equation.

Keywords： hypercrosslinked polymeric adsorbent adsorption isotherm ester prediction

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Prediction of the shear wave velocity

Amoroso SARA

Frontiers of Structural and Civil Engineering 2014, Volume 8, Issue 1, Pages 83-92 doi: 10.1007/s11709-013-0234-6

Abstract: The paper examines the correlations to obtain rough estimates of the shear wave velocity from non-seismic dilatometer tests (DMT) and cone penetration tests (CPT). While the direct measurement of is obviously preferable, these correlations may turn out useful in various circumstances. The experimental results at six international research sites suggest that the DMT predictions of from the parameters (material index), (horizontal stress index), (constrained modulus) are more reliable and consistent than the CPT predictions from (cone resistance), presumably because of the availability, by DMT, of the stress history index .

Keywords： horizontal stress index shear wave velocity flat dilatometer test cone penetration test

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A novel hybrid model for water quality prediction based on VMD and IGOA optimized for LSTM

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 7, doi: 10.1007/s11783-023-1688-y

Abstract:

● A novel VMD-IGOA-LSTM model has proposed for the prediction of

Keywords： Water quality prediction Grasshopper optimization algorithm Variational mode decomposition Long short-term

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Brain-inspired multimodal approach for effluent quality prediction using wastewater surface images and

Frontiers of Environmental Science & Engineering 2023, Volume 18, Issue 3, doi: 10.1007/s11783-024-1791-x

Abstract:

● A novel brain-inspired network accurately predicts sewage effluent quality.

Keywords： Wastewater treatment system Water quality prediction Data driven analysis Brain-inspired model Multimodal

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GLOBAL GENOMIC PREDICTION IN HORTICULTURAL CROPS: PROMISES, PROGRESS, CHALLENGES AND OUTLOOK

Frontiers of Agricultural Science and Engineering 2021, Volume 8, Issue 2,

Abstract:

Horticultural crops are a major source of high value nutritious food, and new improved cultivars developed through breeding are required for sustainable production in the face of abiotic and biotic stresses, and to deliver novel, premium products to consumers. However, grower confidence in the performance of new germplasm, particularly across environmental variability, is important for commercial adoption and germplasm-environment matching to optimize production.

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Title Author Date Type Operation

Application of machine learning models in groundwater quality assessment and prediction: progress and

Journal Article

Spatial prediction of soil contamination based on machine learning: a review

Journal Article

Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

Journal Article

Reliability prediction and its validation for nuclear power units in service

Jinyuan SHI,Yong WANG

Journal Article

Position-varying surface roughness prediction method considering compensated acceleration in milling

Journal Article

Trend prediction technology of condition maintenance for large water injection units

Xiaoli XU, Sanpeng DENG

Journal Article

Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

Journal Article

Liquefaction prediction using support vector machine model based on cone penetration data

Pijush SAMUI

Journal Article

An energy consumption prediction approach of die casting machines driven by product parameters

Journal Article

Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients

Journal Article

The prediction of adsorption isotherms of ester vapors on hypercrosslinked polymeric adsorbent

Liuyan WU,Lijuan JIA,Xiaohan LIU,Chao LONG

Journal Article

Prediction of the shear wave velocity

Amoroso SARA

Journal Article

A novel hybrid model for water quality prediction based on VMD and IGOA optimized for LSTM

Journal Article

Brain-inspired multimodal approach for effluent quality prediction using wastewater surface images and

Journal Article

GLOBAL GENOMIC PREDICTION IN HORTICULTURAL CROPS: PROMISES, PROGRESS, CHALLENGES AND OUTLOOK

Journal Article

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