Prediction Performance of Separate Collection of Packaging Waste Yields Using Genetic Algorithm Optimized Support Vector Machines

Effects of environmental regulation on separate waste collection dynamics: empirical evidence from Italy

Aggregate stability is a useful soil physical dynamic index of soil resistivity to surface wind and water erosion in all ecosystems, especially, in arid and semi-arid regions. Two machine learning techniques including support vector machines (SVMs) and artificial neural networks (ANNs) were used to develop predictive models for the estimation of geometric mean diameter (GMD) of soil aggregates. An empirical multiple linear regression (MLR) model was also constructed as the benchmark to compare their performances. Furthermore, the influence of feature space dimension reduction using parallel genetic algorithm (PGA) on the prediction accuracy of all investigated techniques was evaluated. The ANN model achieved greater accuracy in GMD prediction as compared to the MLR and SVM models. The obtained ERROR% value in GMD prediction using the ANN model was 6.9%, while it was 15.7 and 10.6% for the MLR and SVM models, respectively. Feature selection using PGA improved the prediction accuracy of all investigated techniques. The coefficient of determination (R2) values between the measured and the predicted GMD values using PGA-based MLR, SVM, and ANN models increased by 20.0, 12.2, and 8.8% in comparison with the proposed MLR, SVM, and ANN models. In conclusion, it appears that the PGA-based ANN model could be considered as an alternative to conventional regression models for the GMD prediction.

Separate waste collection in higher education institutions with its technical and social aspects: A case study for a university campus

The municipal solid waste management reform plays a significant role in improvement of the country's ecology and contributes greatly to the country's economics by recycling waste and recovering recycled resources. The most important condition of the implementation of the reform is separated collection of the waste and the opportunity to recycle all the suitable waste. The author presents a list of basic legal acts which regulate the municipal solid waste management process and points out provisions related to the requirements and conditions for separate collection of waste. The study conducted proves that it is advisable for the population of the country to fulfil the conditions for the separate collection of municipal solid waste. The relevance of the study is backed by the data on the existing landfill areas in Russia and Moscow, their annual growth rate, and unfavorable situation regarding the collection of municipal solid waste in the regions of Russia. This includes large volumes of solid household waste produced and an extremely insignificant share of recyclable solid municipal waste in it in relation to its total volumes. The author gives recommendations on how to improve the municipal solid waste management which will make it possible to recycle the suitable waste, bring it back into the country's economy and, thus, move towards the circular economy.

To date, municipal solid waste disposal and separate waste collection are still problematic in Russia. To contribute to the solution to this problem, the study considers the activities of Tyumen Ecological Association, which is a regional operator for municipal solid waste management in Tyumen Oblast, as a social practice. Our goal is to test the possibilities of analyzing the structure of the social practice of waste management and tracking its dynamics using the automation of data collection and processing. In this case, we understand the practice of waste collection and management with reference to the theoretical framework of the theory of practices — as a system of interconnected elements: materials, competences, meanings. The structure of this practice was identified by analyzing its elements, such as materials, competences, meanings, and dynamics. The analysis included the posts of the regional operator's community in the VKontakte social network. The study used frequency analysis of words, “Word at a Glance” analysis, thematic clustering and evaluation of the dynamics of thematic clusters. As a result, the structure of the social practice of municipal solid waste management of the regional operator was revealed and the dynamics of the 10 most frequent thematic clusters was analyzed. We identified two configurations of practice elements in the activities of the regional operator. The first configuration is related to the standardized activities of waste accumulation, transportation, treatment, and disposal. The second configuration is related to the introduction of elements of separate waste collection. The study has concluded that the two configurations of the regional operator's practice elements described in the paper are still not interrelated. Moreover, special efforts might be needed to integrate separate waste collection into the activities of regional operator. The findings can be used by regional waste management operators, residents' organizations, and volunteer organizations to scale up separate waste collection.

Soil organic carbon (SOC) dataset augmentation, which enables the comprehensive monitoring of carbon sinks at regional and global scales, is vital for global carbon cycle management and soil fertility. SOC maps built by conventional laboratory or field measurements are time- and cost-consuming and especially difficult in forests. A new approach to build SOC maps with good accuracy and time efficiency and promptly respond to changes in SOC dynamics is, therefore, being identified. This study aimed to evaluate the ability of SOC estimation using a multiple linear regression model (MLR) and four machine learning algorithms: artificial neural networks (ANN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) with satellite data sources and soil nutrient indicator data to find the optimal method. The results indicate that the SVM and XGBoost models demonstrated the best predictive abilities (R2 = 0.70 and 0.74, %RMSE = 8.8 and 8.3, MAE = 0.176 and 0.155) when using remote sensing variables and soil property variables, respectively. Band7_IDM, Band 5, Band4_IDM, RVI, NSMI, NDVI, Band 6, Band 7, and Band 4 were the most valuable variables in the SVM model, while NDVI, DVI, GVMI, NSMI, and Band4_Dive in the XGBoost model. The SVM model using remote sensing data may be applied to build SOC maps in Vietnamese forests instead of using conventional methods with a high accuracy (R2 = 0.74).

Collecting urban solid waste (USW) is a critical and essential phase for proper waste management and valorization. To date, many data are available for large cities, but few studies have focused on low-density areas, such as mountainous regions. Considering this lack in the literature, this study aimed to compare two separate waste collection systems in two mountainous valleys in terms of effectiveness and GHG emissions. In the first scenario, a mixed collection system (door-to-door + centers) was used, while in the other, waste was separately collected by a full door-to-door system. The results suggested that the full door-to-door system achieved better performance than the mixed collection system (door-to-door + centers), with a fraction of separate waste collection compared to the unit equals (0.84 ± 0.01 vs. 0.79 ± 0.02). Moreover, the full door-to-door system represented the best option for collecting separate waste in mountainous areas in terms of GHG emissions, with 11.21 kgCO2, eq twaste−1 emitted vs. 15.62 kgCO2, eq twaste−1 in the case of the mixed system. Despite utilities emitting a higher amount of GHGs in the door-to-door scenario (4.66 kgCO2, eq inh−1 y−1), they were fully compensated for by the low GHG emissions from citizens in the mixed scenario (1.77 kgCO2, eq inh−1 y−1 vs. 6.65 kgCO2, eq inh−1 y−1). Given the low amount of data on this topic, this work could be considered as a pioneer study of waste management in mountainous areas by comparing the results of two systems regarding effectiveness and GHG emissions. The outcomes of this study could be helpful for waste utilities, institutional agencies, and local communities and also serve as a tool for decision-making in the case of comparing the different options for USW collection systems.

Artificial neural network models are a popular estimation tool for fitting nonlinear relationships because they require no assumptions about the form of the fitting function, non-Gaussian distributions, multicollinearity, outliers and noise in the data. The problems of back-propagation models using artificial neural networks include determination of the structure of the network and over-learning courses. According to data from 1981 to 2008 from 15 permanent sample plots on Dagangshan Mountain in Jiangxi Province, a back-propagation artificial neural network model (BPANN) and a support vector machine model (SVM) for basal area of Chinese fir (Cunninghamia lanceolata) plantations were constructed using four kinds of prediction factors, including stand age, site index, surviving stem numbers and quadratic mean diameters. Artificial intelligence methods, especially SVM, could be effective in describing stand basal area growth of Chinese fir under different growth conditions with higher simulation precision than traditional regression models. SVM and the Chapman–Richards nonlinear mixed-effects model had less systematic bias than the BPANN.

Research on lithology identification method based on mechanical specific energy principle and machine learning theory

The politically preferred solution to fulfil legal recycling demands is often implementing separate collection systems. However, experience shows their limitations, particularly in urban centres with a high population density. In response to the European Union landfill directive, mechanical biological waste treatment plants have been installed all over Europe. This technology makes it possible to retrieve plastic waste from municipal solid waste. Operators of mechanical biological waste treatment plants, both in Germany and the Netherlands, have started to change their mechanical separation processes to additionally produce plastic pre-concentrates. Results from mechanical biological waste treatment and separate collection of post-consumer packaging waste will be presented and compared. They prove that both the yield and the quality of plastic waste provided as feedstock for the production of secondary plastic raw material are largely comparable. An economic assessment shows which conditions for a technical sorting plant are economically attractive in comparison to separate collection systems. It is, however, unlikely that plastic recycling will ever reach cost neutrality.

Global solar irradiation (Hg) is a variable of great importance for different applications. Despite the great advance in the latter, Hg measurements are not readily available in many places. So, several models with different input variables have been developed and used to estimate Hg worldwide. Estimating Hg from models that use air temperature offers an interesting alternative in the absence of measurements. Four groups of models were used in this study: empirical models group (M) with ten models, linear regression models group (RM) with six models, artificial neural network (ANN) models, and support vector machine (SVM) models groups with six models for each one. All the methods take into account different air temperature combinations as input variables (Tmax, Tmin, ΔT, and Tmed). These models were applied to Campo Grande, Brazil, with 10 years data (2005–2017) in order to adjust and evaluate the Hg. The models were evaluated based on four statistical indexes: RMBE, RRMSE, MAPE, and WAI. The models were adjusted and validated using statistical techniques. The results show that the air temperature is an important entry point. Estimates with empirical models perform well (RMBE between 2.84 and 8.68%; RRMSE between 17.78 and 22.93%; MAPE between 17.23 and 23.31%; WAI between 0.85 and 0.90), with errors less than or equal to ANN models (RMBE between 13.94 and 22.67%; RRMSE between 25.37 and 34.95%; MAPE between 29.26 and 49.51%; WAI between 0.51 and 0.81) and SVM (RMBE between 5.80 and 10.15%; RRMSE between 18.87 and 30.34%; MAPE between 19.49 and 42.14%; WAI between 0.55 and 0.884). The regression models had errors with variations similar to the empirical and SVM models. The results show the need to adjust the local coefficients and further studies to further consolidate the models.

Data-driven regression methods are adept at modeling the spindle thermal elongation for error reduction purposes, but are limited in their generalization ability for the established model to take effect under different work conditions, where one prime cause is that the measurement errors and environmental disturbances would inevitably lead to successive outliers in the experimental training data, and the regression algorithms tend to over-fit to such outliers in the thermal datasets which are usually small. Here, an approach is proposed for the first time to detect and remove the suspected outliers in the spindle thermal data while preserve most of the informative data at the same time, so that the disturbances from the outliers can be minimized. The proposed approach for outliers detection is a combination of Boosting and SVR (BSOD), where the support vector machine for regression (SVR) is used as the weak learner in Boosting. Furthermore, the SVR, which is now widely considered as the most suitable regression tool for mapping the temperature-thermal elongation relationship for a spindle, is adopted to regression model the spindle thermal elongation, where the genetic algorithm (GA) is employed to determine the optimal parameter setting for the modeling. Effectiveness of the BSOD approach was verified using four experimental datasets from two different spindles of precision boring machines under different work conditions. The results showed that the BSOD approach reduced the mean squared error MSE of the genetic algorithm optimized support vector machine estimation for unseen thermal elongation datasets in two cases by 66.09% and 49.13%, respectively. This research provides a feasible way to enhance the modeling quality for the thermal elongation of spindles during working process.

Background Positive behaviour and user's collaboration improved by using choices orientation techniques; in Lucca's Hospital (North-West Tuscany Sanitary Authority) this approach has improved separate waste collection execution and has decreased cigarettes dumping in commons areas. Objectives Phase 1 (May 2019): amendment of choices architecture recyclable waste bins in commons areas and into the wards were boosted, with “islands” clearly visible from any point of common paths; in the transit areas were installed exclusively multi-material and paper bins, equipped with signs that clearly indicate which products deliver; residual waste bins were allowed exclusively in certain points.ashtrays at the hospital entrances were eliminated and replaced with signage indicating smoking cessation benefits and the presence of areas “put your cigarette out” along the structure approach routes. Phase 2 (scheduled for May 2020): “nudge” action Spreading in community (using press and social-media) and locally (using poster) of the slogan “In San Luca we are taking a step forward: don't lag behind!”. This message stimulate the perception of an ongoing change to join. Results A decrease in residual waste collection and in instances of non-compliance in separate waste collection was detected. Decorum in the outdoor environments improved, with a reduction of user's reports and extra-cleaning interventions. Conclusions The described actions have improved user's collaboration, management efficiency of waste disposal and cleaning service, perceived quality and cost. Key messages Imitating a social model appears to be successful motivator of virtuous behaviours. Choices architecture and “nudge” techniques may improve citizens’ care pathways governance.

Objectives To differentiate the primary site of brain metastases (BMs) is of high clinical value for the successful management of patients with BM. The purpose of this study is to investigate a combined radiomics model with computer tomography (CT) and magnetic resonance imaging (MRI) images in differentiating BMs originated from lung and breast cancer. Methods Pretreatment cerebral contrast enhanced CT and T1-weighted MRI images of 78 patients with 179 BMs from primary lung and breast cancer were retrospectively analyzed. Radiomic features were extracted from contoured BM lesions and selected using the Mann–Whitney U test and the least absolute shrinkage and selection operator (LASSO) logistic regression. Binary logistic regression (BLR) and support vector machine (SVM) models were built and evaluated based on selected radiomic features from CT alone, MRI alone, and combined images to differentiate BMs originated from lung and breast cancer. Results A total of 10 and 6 optimal radiomic features were screened out of 1288 CT and 1197 MRI features, respectively. The mean area under the curves (AUCs) of the BLR and SVM models using fivefolds cross-validation were 0.703 vs. 0.751, 0.718 vs. 0.754, and 0.781 vs. 0.803 in the training dataset and 0.708 vs. 0.763, 0.715 vs. 0.717, and 0.771 vs. 0.805 in the testing dataset for models with CT alone, MRI alone, and combined CT and MRI radiomic features, respectively. Conclusions Radiomics model based on combined CT and MRI features is feasible and accurate in the differentiation of the primary site of BMs from lung and breast cancer.

Study regionTwo hyper-arid regions (Atbara and Kassala stations) in Sudan. Study focusThe study aims to evaluate the potential of the D-vine Copula-based quantile regression (DVQR) model for estimating daily ETo during 2000–2015 based on various input structures. Further, the DVQR model was compared with Multivariate Linear quantile regression (MLQR), Bayesians Model Averaging quantile regression (BMAQR), Empirical Models (EMMs), and Classical Machine Learning (CML). Besides, the CML models including Random Forest (RF), Support Vector Machine (SVM), Extreme Learning Machine (ELM), Extreme Gradient Boosting (XGBoost), and M5 Model Tree (M5Tree) were employed. New hydrological insights for the regionThe original EMMs showed poor performance, which improved after calibration techniques. The DVQR, MLQR, and BMAQR models showed better performance than the calibrated EMMs. However, the DVQR model exhibited the highest accuracy than the MLQR and BMAQR models over two study sites. The M5Tree, SVM, and XGBoost models perfumed better than ELM and RF models at both study sites. The DVQR and XGBoost models showed equivalent performance (R2, NSE, and WIA > 0.99, MAE, and RMSE < 0.2) to the M5Tree and SVM models, but they had significantly more accuracy than the calibrated EMMs, MLQR, BMAQR, ELM, and RF models in two hyper-arid regions. Overall, the high dimensional DVQR model is recommended as a promising alternative technique for estimating daily ETo in hyper-arid climate conditions around the world.