Carbon emission gradient model construction method

Abstract

The carbon emission gradient refers to the change rule of the carbon emission in space. In order to better understand and monitor the distribution condition of carbon emission so as to carry out carbon emission management and emission reduction work in a targeted manner, the invention provides a construction method of a carbon emission gradient model, and aims to solve the problems of low precision, incomplete data and the like of the existing carbon emission gradient model. The method comprises the following steps: collecting data related to carbon emission; the collected data is preprocessed; extracting features related to carbon emission from the preprocessed data; based on the extracted features, constructing a carbon emission gradient model through a gradient boosting tree model; the constructed model is evaluated; optimizing the model according to an evaluation result; and applying the optimized carbon emission gradient model to carbon emission monitoring, prediction and management. Through the method, a more accurate and reliable carbon emission gradient model can be constructed, so that an effective evaluation tool is provided for carbon emission management and emission reduction work.

Description

Technical Field

[0001] The present invention belongs to the field of low-carbon environmental protection, and specifically relates to a method for constructing a carbon emission gradient model. Background Art

[0002] Traditional carbon emission monitoring methods mainly rely on field surveys and simulation calculations, which have problems such as high cost, low efficiency, and insufficient data. Therefore, developing an efficient and accurate carbon emission gradient model construction method is of great significance for carbon emission monitoring, prediction, and management.

[0003] At present, machine learning technology has been widely used in the field of environmental science, and can reveal the potential relationship between data by building data-driven models. As a powerful ensemble learning method, the gradient boosting tree model can effectively handle complex nonlinear relationships and has strong predictive ability and robustness. Summary of the invention

[0004] This patent proposes a method for building a carbon emission gradient model based on a gradient boosting tree model, aiming to overcome the limitations of traditional methods and improve the accuracy and efficiency of carbon emission monitoring. By utilizing multi-source data, data preprocessing, feature extraction, model construction, model evaluation, model optimization and model application, an efficient and accurate carbon emission gradient model is constructed, providing a new technical means for environmental protection and carbon emission reduction. DETAILED DESCRIPTION

[0005] Data Collection:

[0006] First, it is necessary to collect data related to carbon emissions, including but not limited to carbon emissions, gradient data (such as the altitude and slope of the geographical location), meteorological data (such as temperature, wind speed, sea level pressure, relative humidity, visibility, etc.), economic activity data (such as industrial output value, energy consumption, etc.), and other possible influencing factors (such as population density, traffic flow, building density, green space coverage, etc.). The source of data can be field monitoring, satellite remote sensing, statistical yearbooks, government public data, etc. In particular, in the collection of gradient data, geographic information system (GIS) technology can be used to accurately divide the target area into spaces and conduct gradient analysis.

[0007] Data preprocessing:

[0008] The collected data needs to be preprocessed and cleaned to eliminate outliers, missing values, and data inconsistencies. For carbon emission data, continuity and consistency verification is required to ensure that it conforms to the actual carbon emission law. For gradient data and other influencing factor data, standardization and normalization are required to eliminate the impact of dimensional differences and numerical fluctuations on model construction.

[0009] Feature selection and extraction:

[0010] On the basis of data processing, feature selection and extraction are required to screen out key factors that have a significant impact on the construction of the carbon emission gradient model. This step can draw on feature selection techniques in machine learning, such as filtering, packaging, and embedding. In particular, the gradient boosting tree (GBDT) algorithm can be used to evaluate feature importance and screen out feature variables that have a significant contribution to the prediction of carbon emission gradients.

[0011] Model construction:

[0012] Based on the selected characteristic variables, a carbon emission gradient model is constructed. In this step, the gradient boosting tree algorithm can be used to train and verify the model. The gradient boosting tree is an integrated learning method that gradually optimizes the model performance through multiple iterations and has a strong ability to capture nonlinear relationships and complex patterns. In the process of model construction, attention should be paid to parameter tuning and model verification to ensure the accuracy and stability of the model. Specifically, the model can be constructed according to the following steps:

[0013] Parameter initialization: Set the initial parameters of the gradient boosting tree, such as learning rate, number of decision trees, depth of decision trees, etc.

[0014] Model training: Input the processed data into the gradient boosting tree model to train the model. During the training process, the parameters can be adjusted according to the changes in model performance to optimize the model effect.

[0015] Feature importance assessment: The feature importance assessment function in the gradient boosting tree algorithm is used to screen out feature variables that have significant contributions to the prediction of carbon emission gradients.

[0016] Model validation: Input the validation data set into the trained model to perform model validation and performance testing. Evaluate the accuracy and reliability of the model by comparing the model prediction results with the actual carbon emission gradient data.

[0017] Model optimization and adjustment:

[0018] Based on the model construction, the model can be optimized and adjusted to further improve the prediction accuracy and generalization ability of the model. Specific methods include increasing the number of decision trees, adjusting the depth of decision trees, optimizing parameter settings such as learning rate, and introducing regularization terms, pruning and other strategies to prevent model overfitting. It can also be combined with other machine learning algorithms or integrated learning methods to integrate models and improve performance.

[0019] Model application and effect evaluation:

[0020] Apply the constructed carbon emission gradient model to the actual scenario to predict and evaluate the carbon emission gradient. In this step, attention should be paid to the applicability and interpretability of the model to ensure that the model can play an effective role in practical applications, and the application effect of the model should be evaluated and monitored so as to adjust and optimize the model performance in a timely manner.

[0021] In particular, in the application process of the carbon emission gradient model, it can be combined with geographic information system (GIS) technology for visualization and analysis, helping decision makers to more intuitively understand the carbon emission distribution and gradient change trend in the target area. The model can also be applied to the formulation of carbon emission management and emission reduction strategies, providing scientific basis and technical support for the development of low-carbon environmental protection.

[0022] Accurate prediction and evaluation of the carbon emission gradient can be achieved by collecting and processing carbon emission related data, performing feature selection and extraction, building a carbon emission gradient model, optimizing and adjusting the model, and applying and evaluating the effects of the model.

Claims

1. A method for constructing a carbon emission gradient model based on a gradient boosting tree model, comprising the following specific steps: Data collection: Collect information related to carbon emissions from multiple sources, including climate data, land use data, and population data; Data preprocessing: data cleaning, outlier processing, missing value filling and data standardization of the collected data; Feature extraction: Through feature engineering methods, features related to carbon emissions, such as population density, industrial structure, traffic conditions, etc., are extracted; Model construction: The gradient boosting tree model is used to build a carbon emission gradient model by iteratively training the decision tree model; Model evaluation: The constructed gradient boosting tree model is evaluated through cross-validation and other methods, including the prediction accuracy, generalization ability and stability of the model; Model optimization: Perform optimization operations such as model parameter tuning and feature selection based on the evaluation results to improve the performance and accuracy of the model; Model application: Apply the optimized carbon emission gradient model to carbon emission monitoring, prediction and management to provide a scientific basis for reducing carbon emissions.

2. According to the carbon emission gradient model construction method according to claim 1, the gradient boosting tree model improves the predictive ability and robustness of the model by integrating multiple decision tree models.

3. According to the carbon emission gradient model construction method described in claim 1, the data preprocessing process includes outlier processing, missing value filling and data standardization processing to ensure the quality and consistency of the data.

4. According to the carbon emission gradient model construction method described in claim 1, the feature extraction process extracts features related to carbon emissions through feature engineering methods to improve the predictive ability and interpretability of the model.

5. According to the carbon emission gradient model construction method described in claim 1, model evaluation includes cross-validation, ROC curve analysis and other methods to comprehensively evaluate the performance and stability of the gradient boosting tree model.