Prediction of soil organic carbon in an intensively managed reclamation zone of eastern China: A comparison of multiple linear regressions and the random forest model

Zhang, Huan; Wu, Pingkeng; Yin, Anqi; Yang, Xiaohui; Zhang, Ming; Gao, Chao

doi:10.1016/j.scitotenv.2017.02.146

Cited by 158 publications

(71 citation statements)

References 74 publications

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“…The daily carbon fluxes and climate anomalies in this study can be further used to quantitatively examine how climate extremes in different season determine the annual carbon balance in forest ecosystems. Secondly, many studies had indicated that the impacts of heat and drought stress on forest carbon fluxes are tightly correlated (Kelly, ; Zhang, Shao, Jia, & Wei, ; Zhang, Wu, et al, ; Zscheischler, Mahecha, et al, ; Zscheischler, Michalak, et al, ). In fact, the impact of heat stress was dominated by heat‐induced water stress, which was mediated by soil moisture availability (Duarte et al, ; Reich et al, ; Walker et al, ).…”

Section: Discussionmentioning

confidence: 99%

“…The trees explain the variation of the response variable by repeatedly splitting the data into more homogeneous groups using combinations of explanatory variables. It can identify relatively important relevant variables regardless of the variable distribution and independence(Li et al, 2016;Zhang, Wu, et al, 2017) CART's ability to handle nonlinear relationships, strong interactions, and missing values made it a useful tool to analyze complex ecological data, especially in the synthesis of multisites The minimum number of data points in each leaf was set up to 15 to control the depth of the regression trees in this methodology. Predictor importance of all the explanatory variables was calculated to compare the relative predictive strength of all the variables.…”

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confidence: 99%

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Seasonal variability of forest sensitivity to heat and drought stresses: A synthesis based on carbon fluxes from North American forest ecosystems

Arain

Black

et al. 2019

Global Change Biology

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Climate extremes such as heat waves and droughts are projected to occur more frequently with increasing temperature and an intensified hydrological cycle. It is important to understand and quantify how forest carbon fluxes respond to heat and drought stress. In this study, we developed a series of daily indices of sensitivity to heat and drought stress as indicated by air temperature (Ta) and evaporative fraction (EF). Using normalized daily carbon fluxes from the FLUXNET Network for 34 forest sites in North America, the seasonal pattern of sensitivities of net ecosystem productivity (NEP), gross ecosystem productivity (GEP) and ecosystem respiration (RE) in response to Ta and EF anomalies were compared for different forest types. The results showed that warm temperatures in spring had a positive effect on NEP in conifer forests but a negative impact in deciduous forests. GEP in conifer forests increased with higher temperature anomalies in spring but decreased in summer. The drought‐induced decrease in NEP, which mostly occurred in the deciduous forests, was mostly driven by the reduction in GEP. In conifer forests, drought had a similar dampening effect on both GEP and RE, therefore leading to a neutral NEP response. The NEP sensitivity to Ta anomalies increased with increasing mean annual temperature. Drier sites were less sensitive to drought stress in summer. Natural forests with older stand age tended to be more resilient to the climate stresses compared to managed younger forests. The results of the Classification and Regression Tree analysis showed that seasons and ecosystem productivity were the most powerful variables in explaining the variation of forest sensitivity to heat and drought stress. Our results implied that the magnitude and direction of carbon flux changes in response to climate extremes are highly dependent on the seasonal dynamics of forests and the timing of the climate extremes.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

Seasonal variability of forest sensitivity to heat and drought stresses: A synthesis based on carbon fluxes from North American forest ecosystems

Arain

Black

et al. 2019

Global Change Biology

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“…Multiple linear regression (MLR) is a classical approach that has been largely applied for predicting the values of a dependent variable from predictor variables [34]. MLR is used to study the relation of a dependent variable (GWL) to two independent variables, such as rainfall and groundwater use, and the error term [30].…”

Section: Multiple Linear Regression Methodsmentioning

confidence: 99%

Surrogate Models for Sub-Region Groundwater Management in the Beijing Plain, China

Zhang

Yao

et al. 2017

Water

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Overexploitation of groundwater resources has caused groundwater-related problems all over the world. Effective groundwater governance is a favorable guarantee for its protection and sustainable utilization. Accurate prediction of groundwater level (GWL) or depth to groundwater (GWD) plays an important role in groundwater resource management. Due to the limitations and complexity of numerical models, this study aims to develop surrogate models that can dually control the GWL (or GWD) and groundwater quantity (GWQ) in each district of the Beijing Plain, China, using the methods of multiple linear regression (MLR) and back propagation artificial neural network (BP-ANN). This study used 180 monthly GWD data records, including the first 168 data records for model development (training) and the remaining 12 data records for model verification. The results indicate that the Nash-Sutcliffe efficiency coefficient (NSE) and correlation coefficient (R) for both the MLR and BP-ANN models are high in most districts and that the MLR models are more appropriate in this study. Fifteen scenarios under different conditions of groundwater use and precipitation are designed to demonstrate the applicability of the developed model in groundwater management. The surrogate models are effective tools that can be used by decision-makers for groundwater management.

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“…Machine learning focuses on estimation or prediction by considering an optimal model, whereas the latter concentrates on understanding the relationships between data. Recently, a few related studies applying a machine learning-based method have been reported in various research fields, such as environmental science, geomatics, and social science [39][40][41][42].…”

Section: Decision Tree Using Machine Learningmentioning

confidence: 99%

A Machine Learning Approach to the Residential Relocation Distance of Households in the Seoul Metropolitan Region

Kim

2018

Sustainability

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Abstract:This study aimed to evaluate the applicability of a machine learning approach to the description of residential mobility patterns of households in the Seoul metropolitan region (SMR). The spatial range and temporal scope of the empirical study were set to 2015 to review the most recent residential mobility patterns in the SMR. The analysis data used in this study included the Internal Migration Statistics microdata provided by the Microdata Integrated Service of Statistics Korea. We analysed the residential relocation distance of households in the SMR using machine learning techniques, such as ordinary least squares regression and decision tree regression. The results of this study showed that a decision tree model can be more advantageous than ordinary least squares regression in terms of explanatory power and estimation of moving distance. A large number of residential movements are mainly related to the accessibility to employment markets and some household characteristics. The shortest movements occur when households with two or more members move into densely populated districts. In contrast, job-based residential movements are relatively farther. Furthermore, we derived knowledge on residential relocation distance, which can provide significant information for the urban management of metropolitan residential districts and the construction of reasonable housing policies.

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Prediction of soil organic carbon in an intensively managed reclamation zone of eastern China: A comparison of multiple linear regressions and the random forest model

Cited by 158 publications

References 74 publications

Seasonal variability of forest sensitivity to heat and drought stresses: A synthesis based on carbon fluxes from North American forest ecosystems

Seasonal variability of forest sensitivity to heat and drought stresses: A synthesis based on carbon fluxes from North American forest ecosystems

Surrogate Models for Sub-Region Groundwater Management in the Beijing Plain, China

A Machine Learning Approach to the Residential Relocation Distance of Households in the Seoul Metropolitan Region

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