Environmental Prediction Model Research Articles

Machine Learning-Powered Forecasting of Climate Conditions in Smart Greenhouse Containing Netted Melons

The greenhouse environment plays a crucial role in providing favorable conditions for crop growth, significantly improving their quality and yield. Accurate prediction of greenhouse environmental factors is essential for their effective control. Although artificial intelligence technologies for predicting greenhouse environments have been researched recently, there are limitations in applying these to general greenhouse environments due to computing resources or issues with interpretability. Moreover, research on environmental prediction models specifically for melon greenhouses is also lacking. In this study, machine learning models based on MLR (Multiple Linear Regression), SVM (Support Vector Machine), ANN (Artificial Neural Network), and XGBoost were developed to predict the internal temperature, relative humidity, and CO2 conditions of melon greenhouses 30 min in advance. The XGBoost model demonstrated high accuracy and stability, with an R2 value of up to 0.9929 and an RPD (Residual Predictive Deviation) of 11.8464. Furthermore, the analysis of the XGBoost model’s feature importance and decision trees revealed that the model learned the complex relationships and impacts among greenhouse environmental factors. In conclusion, this study successfully developed a predictive model for a greenhouse environment for melon cultivation. The model developed in this study can facilitate an understanding and efficient management of the greenhouse environment, contributing to improvements in crop yield and quality.

Multi-strategy enhanced snake optimizer for quantitative structure-activity relationship modeling

Quantitative structure-activity relationship modeling techniques are widely employed in diverse areas of chemistry, including environmental risk assessment of compounds and virtual screening of drugs. The modeling process entails optimizing the hyper parameters of the model and selecting appropriate molecular descriptors. It is a typical optimization problem with high-dimensional, nonlinear, continuous and discrete variables coexisting. To address this challenge, this paper proposes an enhanced snake optimizer with multiple strategies. First, new food emergence and temperature change mechanisms are introduced to balance the exploration and exploitation. Second, a death and regeneration mechanism is employed to enhance the algorithm's ability to escape from local optima. Third, new formulas for food searching, fighting, mating and eating are designed to simplify the complexity of the algorithm and improve its search efficiency and accuracy. Fourth, the performance of the proposed algorithm is evaluated using the CEC2017 benchmark functions, eight engineering cases with continuous variables, and six feature selection cases with discrete variables. Experimental results demonstrate that the algorithm significantly outperforms comparison algorithms. Finally, the algorithm was applied to quantitative structure-activity relationship modeling for environmental toxicity prediction. Compared with other algorithms, the algorithm can better configure hyper-parameters and select fewer and more effective features for environmental toxicity prediction model. It will be an effective tool to improve the performance of quantitative structure-activity relationship models.

Research on dynamic prediction method of temperature environment of unmanned aerial vehicle platform based on physical information and neural network

During the flight of an unmanned aerial vehicle, the temperature environment inside the cabin is very harsh, resulting in frequent malfunctions of airborne equipment. Therefore, it is necessary to predict the temperature environment experienced by airborne equipment during its life cycle in the early stages of design to support the formulation of design requirements, determine test verification conditions, used and maintained. In order to accurately predict the temperature environment of the unmanned aerial vehicle platform, this paper proposes a prediction method that integrates physical information and neural networks. In view of the thermal environment characteristics of unmanned aerial vehicle platform, the physical mechanism of the formation and transfer of the unmanned aerial vehicle thermal environment was analyzed. Based on the physical mechanism equations and combined with the measured data of unmanned aerial vehicles, an Elman neural network model was established to achieve high flight speed, speed, etc. parameters are characterized as a function of the temperature inside the unmanned aerial vehicle cabin, and finally the model is verified. The results show that the temperature environment prediction model integrates physical information and neural networks can accurately predict the dynamic changes in the temperature inside the unmanned aircraft cabin. The maximum error of the model is 2.76°C, which meets engineering needs.

Effects of Intraseasonal Oscillation on Timing and Subseasonal Predictability of Mei-yu Onset over the Yangtze River Basin

Abstract The time of rainy season onset is crucial information for policymakers, especially in densely populated regions such as the Yangtze River basin (YRB) in China. In this study, we proposed a new grid-based index to objectively detect mei-yu onset timing using reanalysis data and model predictions, and then we identified the key processes via which intraseasonal oscillation (ISO) affects the YRB mei-yu onset and its subseasonal predictability based on scale-decomposed moisture analysis. Climatologically, propagation of an ISO anticyclonic anomaly toward East China supports the moisture convergence required for rainy season onset over the YRB via interaction with the seasonal-mean moisture component. In the years of early mei-yu onset, the ISO was enhanced earlier in May and favored the moisture convergence anomaly in late May–early June, when the mei-yu started. In contrast, the enhanced ISO and associated moistening processes were observed later in June–early July in the years with delayed onset. The European Centre for Medium-Range Weather Forecasts and National Centers for Environmental Prediction models show skillful prediction of mei-yu onset at forecast lead times of 5–6 pentads, whereas the China Meteorological Administration model has limited skill of 3 pentads. The differences in model prediction skill are related to the accuracy of predicted moisture convergence anomalies induced by the ISO. The prediction bias in mei-yu onset timing (early or delayed) is also connected to bias in the occurrence timing of enhanced intraseasonal perturbations, suggesting the vital role of ISO in YRB mei-yu onset on the subseasonal time scale.

Construction of environmental vibration prediction model for subway transportation based on machine learning algorithm and database technology

Vibrations generated in the metro transport environment are mainly caused by, vibrations generated by the interaction between the metro and the track during operation. and the change of vibration factors will affect the normal operation of the subway. However, it is difficult to have a model that can achieve the characteristics of high accuracy, fast computing speed and wide range of use in the traditional metro rail transportation environment prediction. Therefore, this research uses database theory and machine learning algorithms to predict the vibration of subway transportation environment. The experimental results show that the average difference between the whole prediction value and the real value is 1.4 dB, of which the maximum difference error value is 0.29%, the maximum error difference is 8.2%, and the approximate value is 6.2 dB, and the four averages predicted in 40 m are relatively small as 1.6 dB, and the average error value of prediction ability between 40 and 100 m is 1.72 dB, and the experimental prediction value and real value are in good agreement. The agreement between the experimental prediction and the real value is very good. Therefore, the model is able to predict the vibration model of the subway transportation environment with a high degree of agreement and accuracy.

Environmental Prediction Model of Solar Greenhouse Based on Improved Harris Hawks Optimization-CatBoost

Solar greenhouses provide a favorable climate environment for the production of counter-seasonal crops in northern China. The greenhouse environment is a key factor affecting crop growth, so accurate prediction of greenhouse environment changes helps to precisely regulate the crop growth environment and helps to promote the growth of fruits and vegetables. In this study, an environmental prediction model based on the combination of a gradient boosting tree and the Harris hawk optimization algorithm (IHHO-Catboost) is constructed, and in response to the problems of the HHO algorithm, such as the fact that the adjustment of the search process is not flexible enough, it cannot be targeted to carry out a stage search, and sometimes it will fall into the local optimum to make the algorithm’s search accuracy relatively poor, an algorithm based on the improved Harris hawk optimization (IHHO) algorithm-based parameter identification method is constructed. The model considers the internal and external environmental and regulatory factors affecting crop growth, which include indoor temperature and humidity, light intensity, carbon dioxide concentration, soil temperature and humidity, outdoor temperature and humidity, light intensity, carbon dioxide concentration, wind direction, wind speed, and opening and closing of upper and lower air openings of the cotton quilt, and is input into a prediction model with a time series for training and testing. The experimental results show that the MAE (mean absolute error) values of temperature, relative humidity, carbon dioxide concentration, and light intensity of the model are reduced to 49.8%, 35.3%, 72.7%, and 32.1%, respectively, compared with LSTM (Long Short-Term Memory), which is a significant decrease in error. It shows that the proposed multi-parameter prediction model for solar greenhouse environments presents an effective method for accurate prediction of environmental data in solar greenhouses. The model not only improves prediction accuracy but also reduces dependence on large data volumes, reduces computational costs, and improves the transparency and interpretability of the model. Through this approach, an effective tool for greenhouse agriculture is provided to help farmers optimize the use of resources, reduce waste, and improve crop yield and quality, ultimately leading to a more efficient and environmentally friendly agricultural production system.

Edible Mushroom Greenhouse Environment Prediction Model Based on Attention CNN-LSTM

The large-scale production of edible mushrooms typically requires the use of greenhouses, as the greenhouse environment significantly affects the growth of edible mushrooms. It is crucial to effectively predict the temperature, humidity, and carbon dioxide fluctuations within the mushroom greenhouse for determining the environmental stress and pre-regulation of edible mushrooms. To address the nonlinearity, temporal dynamics, and strong coupling of the edible mushroom greenhouse environment, a temperature, humidity, and carbon dioxide prediction model based on the combination of the attention mechanism, the convolutional neural network, and the long short-term memory neural network (A-CNN-LSTM) is proposed. Experimental data were collected from both the inside and outside of the greenhouse, including environmental data and the on–off data of environmental control devices. After completing missing data using linear interpolation, denoising with Kalman filtering, and normalization, the recurrent neural network (RNN) model, long short-term memory (LSTM) model, and A-CNN-LSTM model were trained and tested on the time series data. These models were used to predict the environmental changes in temperature, humidity, and carbon dioxide inside the greenhouse. The results indicate that the A-CNN-LSTM model outperforms the other two models in terms of denoising, non-denoising, and different prediction time steps. The proposed method accurately predicts temperature, humidity, and carbon dioxide levels with errors of 0.17 °C (R2 = 0.974), 2.06% (R2 = 0.804), and 8.367 ppm (R2 = 0.993), respectively. These results indicate improved prediction accuracy for temperature, humidity, and carbon dioxide values inside the edible mushroom greenhouse. The findings provide a decision basis for the precise control of the greenhouse environment.

Study on stiffness matching strategy of longitudinal sleeper and fastener to reduce tunnel wall vibration

With the increase of the running time of the subway system, long-term use will lead to the reduction of damping, the change of the operating state of the wheels and the track, and the effect of vibration damping measures will gradually weaken. In order to maintain a low level of environmental vibration in vibration sensitive areas, the reconstruction of some existing lines with high level vibration is imminent. In order to solve this problem, this paper focuses on the reconstruction of the existing line of longitudinal sleeper track, which is commonly used in the middle and high level vibration reduction measure in the subway. Because of the high cost of reconstruction of the slab and its lower part, this paper tries to realize the reconstruction of the existing longitudinal sleeper track by changing the fastener system. In this paper, the environmental vibration prediction model of longitudinal sleeper track is established by using the theory of infinite long period structure and the 2.5-dimensional finite element and boundary element (2.5D FE-BE) environmental vibration prediction model. The validity of the numerical model is determined by comparing the field experiment with the numerical simulation. On the basis of the above, the stiffness matching of longitudinal sleeper and fastener is explored by using the verified numerical model. The reasons for the poor vibration reduction effect of the existing longitudinal sleeper matching floating rail fastener (low stiffness) are analyzed. And the matching strategy of the supporting stiffness for vibration reduction of longitudinal sleeper and fastener are summarized.

Modeling below-cloud scavenging of size-resolved particles in GEM-MACHv3.1

Abstract. Below-cloud scavenging (BCS) is the process of aerosol removal from the atmosphere between cloud base and the ground by precipitation (e.g., rain or snow), and affects aerosol number or mass concentrations, size distribution, and lifetime. An accurate representation of precipitation phases is important in treating BCS as the efficiency of aerosol scavenging differs significantly between liquid and solid precipitation. The impact of different representations of BCS on existing model biases was examined through implementing a new aerosol BCS scheme in the Environment and Climate Change Canada (ECCC) air quality prediction model GEM-MACH and comparing it with the existing scavenging scheme in the model. Further, the current GEM-MACH employs a single-phase precipitation for BCS: total precipitation is treated as either liquid or solid depending on a fixed environment temperature threshold. Here, we consider co-existing liquid and solid precipitation phases as they are predicted by the GEM microphysics. GEM-MACH simulations, in a local-area domain over the Athabasca oil sands areas, Canada, are compared with observed precipitation samples, with a focus on the particulate base cation NH4+; acidic anions NO3-, SO4=, HSO3- in precipitation; and observed ambient particulate sulfate, ammonium, and nitrate concentrations. Overall, the introduction of the multi-phase approach and the new scavenging scheme enhances GEM-MACH performance compared to previous methods. Including a multi-phase approach leads to altered SO4= scavenging and impacts the BCS of SO2 into the aqueous phase over the domain. Sulfate biases improved from +46 % to −5 % relative to Alberta Precipitation Quality Monitoring Program wet sulfate observations. At Canadian Air and Precipitation Monitoring Network stations the biases became more negative, from −10 % to −30 % for the tests carried out here. These improvements contrast with prior annual average biases of +200 % for SO4=, indicating enhanced model performance. Improvements in model performance (via scores for correlation coefficient, normalized mean bias, and/or fractional number of model values within a factor of 2 of observations) could also be seen between the base case and the two simulations based on multi-phase partitioning for NO3-, NH4+, and SO4=. Whether or not these improvements corresponded to increases or decreases in NO3- and NH4+ wet deposition varied over the simulation region. The changes were episodic in nature – the most significant changes in wet deposition were likely at specific geographic locations and represent specific cloud precipitation events. The changes in wet scavenging resulted in a higher formation rate and larger concentrations of atmospheric particle sulfate.

An Automated Graph-Based Neural Network Model for Predicting Urban Environmental Air Quality Using Spatio-Temporal Data Optimization

<p>Environmental protection and the need for accurate pollutant forecasting have become increasingly important as worries about environmental issues and the harmful effects of pollution have grown. Predictive accuracy of air pollutants is generally unsatisfactory due to the fact that conventional methodologies prioritise time series analysis over the important spatial transmission dynamics among neighbouring locations. To address this inherent limitation, our proposed solution introduces an innovative Time Series Prediction Network, augmented by the auto-optimization capabilities of a Spatio-Temporal Graph-based Neural Network. This groundbreaking network comprises distinct spatial and temporal modules. The spatial module harnesses a Graph Sampling and Aggregation Network to extract essential spatial information from the data. Simultaneously, the temporal module integrates a Bayesian approach with a Complex Valued Graph Gated Recurrent Unit (BCV-GRU), seamlessly incorporating a graph network into the Gated Recurrent Unit (GRU) to capture temporal intricacies. Moreover, to manage the challenge of model inaccuracy stemming from inappropriate hyperparameters, Bayesian optimization was employed. The efficacy of our proposed method was validated using real PM2.5 data from the USGS website, showcasing a significant enhancement in prediction accuracy. This study puts forth a robust and effective approach for forecasting PM2.5 concentrations, bridging gaps in existing methodologies and contributing substantially to the evolution of environmental prediction models.</p>

A deep learning model for predicting risks of crop pests and diseases from sequential environmental data

Crop pests reduce productivity, so managing them through early detection and prevention is essential. Data from various modalities are being used to predict crop diseases by applying machine learning methodology. In particular, because growth environment data is relatively easy to obtain, many attempts are made to predict pests and diseases using it. In this paper, we propose a model that predicts diseases through previous growth environment information of crops, including air temperature, relative humidity, dew point, and CO2 concentration, using deep learning techniques. Using large-scale public data on crops of strawberry, pepper, grape, tomato, and paprika, we showed the model can predict the risk score of crop pests and diseases. It showed high predictive performance with an average AUROC of 0.917, and based on the predicted results, it can help prevent pests or post-processing. This environmental data-based crop disease prediction model and learning framework are expected to be universally applicable to various facilities and crops for disease/pest prevention.

Cadmium accumulation in tropical island paddy soils: From environment and health risk assessment to model prediction

Cultivated soil quality is crucial because it directly affects food safety and human health, and rice is of primary concern because of its centrality to global food networks. However, a detailed understanding of cadmium (Cd) geochemical cycling in paddy soils is complicated by the multiple influencing factors present in many rice-growing areas that overlap with industrial centers. This study analyzed the pollution characteristics and health risks of Cd in paddy soils across Hainan Island and identified key influencing factors based on multi-source environmental data and prediction models. Approximately 27.07% of the soil samples exceeded the risk control standard screening value for Cd in China, posing an uncontaminated to moderate contamination risk. Cd concentration and exposure duration contributed the most to non-carcinogenic and carcinogenic risks to children, teens, and adults through ingestion. Among the nine prediction models tested, Extreme Gradient Boosting (XGBoost) exhibited the best performance for Cd prediction with soil properties having the highest importance, followed by climatic variables and topographic attributes. In summary, XGBoost reliably predicted the soil Cd concentrations on tropical islands. Further research should incorporate additional soil properties and environmental variables for more accurate predictions and to comprehensively identify their driving factors and corresponding contribution rates.

A method for predicting the ambient temperature distribution of high-temperature tunnels and influencing factors analyze

In high-temperature tunnels, it is difficult to predict the temperature distribution, and the effectiveness of comprehensive cooling measures is not yet clear. This study proposed a highly accurate tunnel environmental temperature prediction model using FDM and FEM and considering the heat transferred from rock, construction machinery, and personnel. The effects of air volume, air temperature, and insulating layer on tunnel ambient temperature were investigated. The following are the key findings: (1) The distribution of the environment temperature and the lining surface temperature of the two tunnels align with the ground temperature distribution. (2) The prediction model using FDM + FNM closely matches the actual monitoring data, with a maximum absolute error of only 2.52 °C. (3) Increasing air volume or decreasing the temperature of the wind can reduce the temperature of the environment, which has no effect on the temperature distribution of tunnels. (4) The thermal insulating layer impedes ground temperature heat transfer and alters the ambient temperature distribution. The distribution of ambient temperature tends to align as the thickness of the thermal insulating layer grows, while cooling efficiency drops when the thickness is greater than 6 cm.

Analysis of uncertainty and variation in underground train-induced vibration based on measured data

Ground-borne noise and vibration from metro trains can cause human distress/annoyance, affect the growth of animals and plants, and also negatively affect real estate property values. Before construction, the common prediction methods for the environmental vibration caused by metro trains are physical model prediction and analogy test. However, the rationality of the analogy test and the factors that need to be considered in the simulation prediction have not been systematically tested and studied. In order to solve this problem and provide experimental reference for the prediction model, one of China’s subway were systematically tested to investigate wheel roughness, rail roughness, train running speed, axle load changes, the vertical and lateral contributions, and the influence of random factors existing in the test on the vibration of subway operating environment, the following conclusions are obtained: (1) The test state randomness (random factors in the test, such as wheel-rail contact points, roughness phase, etc.) mainly affect frequencies below 31.5 Hz segment, mainly from the randomness of the wheel roughness phase or the variation of the spatial position of different contact points during actual operation, which result in large different of long-wave roughness of the wheel and rail; (2) The wheel-rail lateral force also has great influence on the environmental vibration. When establishing the environmental vibration prediction model, it is necessary to consider the state that the train reaches the straight line after passing through the curve, and the influence of track lateral irregularity; (3) The impact of wheel roughness on the environmental vibration is very large. Basically, it has an impact on the whole evaluation frequency band of environmental vibration.

Environmental risk thresholds and prediction models of Cd in Chinese agricultural soils

Soil environmental risk threshold of cadmium (Cd) is an important index in formulating soil protection policy. Environmental risk threshold refers to the maximal allowable critical concentration of hazardous substances in the environment. Although there is less study on how to determine soil Cd environmental risk threshold, it is a crucial indicator in formulating soil conservation policies and a key factor in assessing soil environmental quality. The main research content of the study is deducing the environmental risk threshold, aiming to provide scientific basis for the study of environmental quality standards of agricultural land and provide technical support for the protection of Cd pollution of agricultural land. The hazard concentration of 5 % species (HC5, which protects 95 % of species) was determined here using different toxicological data of Cd from 23 test endpoints, interspecific extrapolation using the species sensitivity distribution (SSD) method, and a prediction model was created on the basis of several soil parameters. According to the findings, Cd effective concentration (EC10) (Cd concentration which blocks 10 % of an endpoint's bioactivity) varied from 0.109 to 221 mg·kg−1, and the hormetic response induced by Cd reached 118 % displaying in the dose-response curve of Lolium perenne L.. Toxicology data was rectified by the aging factor considering biogeochemical processes of the newly added pollutants prior to SSD curves fitting. After that, the prediction model was created with the equation of LogHC5 = 0.147 pH + 0.067 OC -1.616. The field test properly validated the prediction model, demonstrating its ability to forecast Cd toxicity levels for various soil conditions. This study offers a scientifically sound methodology for determining the environmental risk limitation for Cd and identifies critical paths for the preservation of environmental species.

A quantitative exploration of the interactions and synergistic driving mechanisms between factors affecting regional air quality based on deep learning

Air pollution represents a significant environmental challenge on a global scale. The exploration of factors and mechanisms influencing air quality has garnered considerable attention and produced a substantial body of research. Nonetheless, the absence of effective research tools has resulted in a limited understanding of the nonlinear interactive effects of multiple factors on air quality, as well as their patterns and spatiotemporal variability characteristics. Furthermore, the efficacy of multifactor joint control strategies in practical air pollution management remains inadequate. A novel regional air environmental quality prediction model was developed utilizing a neural network with long short-term memory (LSTM) in deep learning based on an analysis of the spatiotemporal heterogeneity of regional air environmental quality in the Yangtze River Economic Belt (YEB). The model results were attributed and interpreted by integrating the SHapley Additive explanation (SHAP) method, focusing on the possible interactive effects and synergistic driving mechanisms of PM2.5 and O3 as the main pollutants and other factors on the air quality index (AQI) in a quantitative way, respectively. The SHAP interaction values of five factor pairs, namely PM2.5 and temperature, PM2.5 and precipitation, O3 and temperature, O3 and NO2, and O3 and PM2.5, are depicted in dependence scatter plots that exhibit three distinct trajectory shapes, namely linear, U-shaped, and irregular. The linear pattern of the dependence scatter plots for PM2.5 and precipitation in the YEB, midstream, and downstream regions, as well as for O3 and temperature in the upstream region, is evident. The irregular patterns of PM2.5 and precipitation, O3 and NO2, O3 and PM2.5 in the upstream, and PM2.5 and temperature in the downstream are observed to be correspondingly related. Additionally, a U-shaped pattern is exhibited by O3 and temperature in the YEB, midstream and downstream, PM2.5 and temperature in the upstream and midstream, O3 and NO2 in the YEB, midstream and downstream, and O3 and PM2.5 in the YEB. Of the three models, the U-shaped model's identification of inflection points and corresponding quantitative determination of factor thresholds hold greater practical implications for decision-making in air environment management. This pattern suggests that the synergistic effect of factor pairs initially decreases with changes in each factor but subsequently begins to increase after reaching inflection points. It is anticipated that this research will contribute to the advancement of theoretical inquiry into the nonlinear multifactor interaction effect and synergistic driving model of regional air quality. Additionally, it is expected to furnish a valuable foundation for decision-making in the development of joint air pollution prevention and control strategies aimed at addressing compound air pollution issues.

Tracking Control Method for Greenhouse Environment Prediction Model Based on Real-Time Optimization Error Constraints

Model predictive control, without strict constraints on the control model, effectively overcomes problems, such as poor system dynamic quality caused by time delay, can improve control accuracy to a certain extent, and can directly process input and output constraints of the system online. It is applied in greenhouse system control. The appropriate objective optimization function and its corresponding constraint conditions have a direct impact on the solution of the optimal control rate of the model predictive control. In response to this issue, this paper proposes a simple and fast optimal predictive tracking control method. Based on the current prediction model of the greenhouse system, which reflects the dynamic relationship between various control equipment actions and greenhouse environmental factors, a multi degree of freedom discrete time state space model with tracking errors is established. Based on this model, in establishing the corresponding objective optimization function, the gradient descent theory and the two-norm definition are applied, and combined with actual constraints, iterative constraint conditions for real-time error tracking updates are established. Compared with traditional constraint ranges, a constraint function with real-time update characteristics is formed, achieving more accurate constraint conditions. By using rolling optimization and iterative methods, the optimal control rate corresponding to the minimum value of the objective optimization function within a finite time is solved. Through simulation examples, it is demonstrated that the model predictive control with optimization constraints can achieve a more accurate prediction and tracking control of indoor environmental parameters. This method has the advantages of simple control, energy-saving optimization, stable control, and accurate tracking, providing a reference for online real-time prediction and tracking control of future greenhouse environmental parameters.

Coal-fired power plant CCUS project comprehensive benefit evaluation and forecasting model study

As an important means to reduce CO2 emissions from coal-fired power plants, CCUS project has received more and more attention. In this paper, the comprehensive benefits of CCUS project in coal-fired power plants are defined as economic benefits, environmental benefits and energy benefits, and comprehensive evaluation and prediction analysis of CCUS project in coal-fired power plants are made from these three aspects. The goodness-of-fit of the constructed comprehensive evaluation model can reach 0.99, and the ratio of economic, environmental and energy benefits is about 5: 9: 1. Among them, the best scenario of policy influence, economic development and technological progress can promote CCUS project with benefits of 6.51%, 6.56% and 13.07% respectively. Through case analysis, it can be calculated that when the service life of CCUS project reaches 21.7 years, the investment cost can be recovered from the economic benefits. In the prediction model, the goodness of fit of the economic benefit prediction model is up to 0.95, in which the carbon capture cost of CCUS technology, operation and maintenance cost and carbon price fluctuation play a major role. The goodness of fit of the environmental benefit prediction model can reach 0.90, in which the carbon capture capacity of CCUS project and the world environmental situation are the main influencing factors, and the goodness of fit of the energy benefit prediction model can reach up to 0.95, which is mainly affected by the energy consumption level of CCUS project. The research results show that CCUS project of coal-fired power plants at home and abroad has a good development prospect and remarkable comprehensive benefits. This paper puts forward relevant policy suggestions that are beneficial to the development of CCUS project in coal-fired power plants, with a view to providing empirical evidence for the evaluation and decision-making of CCUS project in coal-fired power plants.

A New Model of Environmental-Economic Coordination Prediction Using Credible Neural Network Integration and Big Data Analysis

Currently, a significant portion of sustained economic growth still depends on a high input of resources. We must fully understand the significance, difficulty, and long-term nature of resource conservation and environmental protection. We must also intensify our efforts to protect the ecological environment. We must gradually form a production mode, lifestyle, and consumption mode conducive to environmental protection. We must also establish a benign interactive relationship with the environment. This study offers a big data analysis and neural network integration optimization design strategy for a new kind of environmental and economic coordination prediction model. The data of the new type of environmental and economic impact with numerous parameters are preprocessed using big data analysis and principal component analysis. A neural network integration system is used to create the prediction model, and prediction research and error analysis are conducted to enhance the new kind of environmental and economic model. The simulation test analysis is completed lastly. According to the simulation findings, the proposed arithmetic has an accuracy that is 8.56% higher than that of the conventional arithmetic. A sustainable improvement management system with environmental objectives, environmental management planning plans, and environmental monitoring systems can be established with help from the new environmental and economic coordination prediction model, which can also assist in predicting the potential environmental impact caused by economic development activities. This will ensure sustainable development as a result of the mechanism.

Convolutional Neural Network Model to Predict Outdoor Comfort UTCI Microclimate Map

Although research on applying machine learning to the performance of the built environment has been advancing considerably, outdoor environment prediction models still need to be more accurate. In this study, I investigated hybrid-driven methods for developing environmental performance prediction models and studied how machine learning algorithms may interpret spatial information in the context of an environmental performance simulation challenge. The simulation of the Universal Thermal Climate Index (UTCI) for outdoor applications served as an example. Specifically, I designed two different network structures, each with six neural network models. These neural network models were built with various numbers of layers, convolutional kernel sizes, and convolutional kernel layers. As shown by these models’ training results, I investigated the effect of model parameter settings on performance. In addition, I conducted interpretable analysis through the visual observation of hidden internal layers. The use of multilayer and small convolutional kernels, as well as an increase in the amount of training data, may be the reason neural network prediction performance was improved. From the perspective of interpretability analysis, the convolutional layer can more accurately analyze building space problems, and full connection layers focus more on the regression between the spatial features and performance results. This “space analysis → data regression” network structure can be expanded to wind environment forecasting or heat environment in the future.