Solid waste forecasting using modified ANFIS modeling

Globally, wind energy if properly harnessed, could serve as a source of energy generation in Africa. This study compared the performance of two Machine Learning (ML) algorithms (Linear regression and Random Forest) in predicting wind speed in five major cities in Africa (Yaoundé, Pretoria, Nairobi, Cairo and Abuja). Wind data were collected between January 1, 2000, and December 31, 2022, using the Solar Radiation Data Archive. The data preprocessing was carried out with 80% of the data used for training and 20% for validation. The performance of these ML algorithms was evaluated using Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and coefficient of determination (R2). The result shows that Nairobi (3.814795 m/s) closely followed by Cairo (3.606453 m/s) has the highest mean wind speed while Yaoundé (1.090512 m/s) has the lowest. Based on the performance metrics used, the two Machine Learning algorithms were competitive. Still, the Linear Regression (LR) algorithm outperformed the Random Forest Algorithm in predicting wind speed in all the selected major African cities. In Yaoundé (RMSE = 0.3892, MAE= 0.3001, MAPE =0.5030), Pretoria (RMSE=1.2339, MAE=0.9480, MAPE=0.7450) Nairobi (RMSE= 0.4223, MAE =0.6499, MAPE =0.1872), Nairobi (RMSE=0.6499, MAE=0.5171, MAPE =0.1872), Cairo (RMSE =1.0909, MAE =0.8544, MAPE =0.3541) and Abuja (RMSE = 0.70245, MAE =0.5441, MAPE= 0.4515) the Linear regression algorithms was found to outperformed Random Forest Regression. Therefore, the Linear regression algorithm is more reliable in predicting wind speed compared with the Random Forest regression.

Worldwide, fish industry wastes are an important contaminant having an impact on the environment. The recovery of value added products from these residues constitutes an important waste reduction strategy for the industry. In East Africa, Nile perch fish processing into chilled fish fillet for export along Lake Victoria generate large proportions of both solid and liquid wastes. However, no thorough auditing and characterization of the waste has been done that would guide potential value addition through bioconversions and waste management. Auditing by surveying and mapping the fish processing industries was conducted along the lake. Waste quantification was done using specific guidelines for assessment of fish wastes. Analysis of the waste was carried out using standard methods. Annual solid waste and wastewater generation was estimated at 36,000 tonnes and 1,838,000 m 3 , respectively. The wastewater generated was high strength with a total chemical oxygen demand of 12,400 mg/l and solid content of 5,580 mg/l. The wastewater contained 6,160 mg/l of lipids and 2,000 mg/l of protein. The Nutrient content was 20 mg/l of total phosphorous, 340 mg/l organic nitrogen and 61 mg/l of ammonia nitrogen. The current fish waste management systems in place were found to be neither efficient nor profitable, thus profitable options of fish waste utilization and waste reduction strategies are imperative. Modern and economically viable options of fish waste value addition, decision scheme and waste reduction strategies have been highlighted in this paper. In conclusion, large amounts of fish waste generated are a rich source of lipids and proteins, which could be utilized for production of value added products through bioconversions. Key words : Nile perch, fish waste, auditing, characterization, value addition, utilization.

The generation of solid waste has become an increasing environmental and public health problem, especially in developing countries. These problems associated with the generation of solid waste are part of social changes where households play an important role. Invariably, these social changes influence the size, structure and characteristics of given households. This paper presents the findings of a study carried out in Freetown municipal area in Sierra Leone to assess socioeconomic factors affecting household solid waste generation and composition in Freetown, Sierra Leone. Structured questionnaires were administered with respect to these socioeconomic factors in four (4) selected constituencies of the city. These are the most populated constituencies that generated 70% of the total quantity of solid waste in the city. Therefore, they are suitable samples of the study area. The rate of waste generation was determined by using door- to-door approach in five (5) selected households from each constituency through sorting and weighing of solid wastes respectively. The dependent variables were solid waste generation and composition, and the independent variables were family size, education, income levels among others. The data obtained were subjected to statistical analysis to determine relationships between independent variables and dependent variables through correlation. The results showed that the solid waste generation and composition in Freetown was significantly affected by average family size, employment status, monthly income, and number of room(s) occupied by households. In general, the paper adequately suggests new insights concerning the role of socioeconomic factors in affecting the generation and composition of household solid waste.

Operating room (OR) expenditures and waste generation are a priority, with several professional societies recommending the use of reprocessed or reusable equipment where feasible. The aim of this analysis was to compare single-use pulse oximetry sensor stickers ("single-use stickers") versus reusable pulse oximetry sensor clips ("reusable clips") in terms of annual cost savings and waste generation across all ORs nationally. This study did not involve patient data or research on human subjects. As such, it did not meet the requirements for institutional review board approval. An economic model was used to compare the relative costs and waste generation from using single-use stickers versus reusable clips. This model took into account: (1) the relative prices of single-use stickers and reusable clips, (2) the number of surgeries and ORs nationwide, (3) the workload burden of cleaning the reusable clips, and (4) the costs of capital for single-use stickers and reusable clips. In addition, we also estimated differences in waste production based on the raw weight plus unit packaging of single-use stickers and reusable clips that would be disposed of over the course of the year, without any recycling interventions. Estimated savings were rounded to the nearest $0.1 million. The national net annual savings of transitioning from single-use stickers to reusable clips in all ORs ranged from $510.5 million (conservative state) to $519.3 million (favorable state). Variability in savings estimates is driven by scenario planning for replacement rate of reusable clips, workload burden of cleaning (ranging from an additional expense of $618k versus a cost savings of $309k), and cost of capital-interest gained on investment of capital that is freed up by the monetary savings of a transition to reusable clips contributes between $541k (low-interest rates of 2.85%) and $1.3 million (high-interest rates of 7.08%). The annual waste that could be diverted from landfill by transitioning to reusable clips was found to be between 587 tons (conservative state) up to 589 tons (favorable state). If institutions need to purchase new vendor monitors or cables to make the transition, that may increase the 1-time capital disbursement. Using reusable clips versus single-use stickers across all ORs nationally would result in appreciable annual cost savings and waste generation reduction impact. As both single-use stickers and reusable clips are equally accurate and reliable, this cost and waste savings could be instituted without a compromise in clinical care.

Solid waste generation has been a great concern for the developing countries like Bangladesh. Municipal solid waste (MSW) generation is increasing in the mega cities in Bangladesh due to rapid urbanisation and population growth. This study has been conducted in Sylhet City Corporation (SCC), Bangladesh. In this study, data and information including amount of generated waste and supporting facilities for waste management in SCC were collected from SCC authority and different scientific literatures. The objectives of this study were to make a clear sketch of the solid waste management process of SCC including waste generation, collection and disposal of that waste at dumpsite. Impact of this municipal solid waste on environment and different types of living species like human beings, animals and plants were also a part of this study. After analysis, it is clear that this municipal waste has a great negative impact on environment and living species. For that reason, some actions were suggested to be taken to minimise the effect of this solid waste on environment and living species.

Accurate estimation of municipal solid waste (MSW) generation has become a crucial task in decision-making processes for the MSW planning and management systems. In this study, the Gaussian process regression (GPR) model tuned by Bayesian optimization was used to forecast the MSW generation of Turkey. The Bayesian optimization method, which can efficiently optimize the hyperparameters of kernel functions in the machine learning algorithms, was applied to reduce the computation redundancy and enhance the estimation performance of the models. Four socio-economic indicators such as population, gross domestic product per capita, inflation rate, and the unemployment rate were used as input variables. The performance of the Bayesian GPR (BGPR) model was compared with the multiple linear regression (MLR) and Bayesian support vector regression (BSVR) models. Different performance measures such as mean absolute deviation (MAD), root mean square error (RMSE), and coefficient of determination (R2) values were used to evaluate the performance of the models. The exponential-GPR model tuned by Bayesian optimization showed superior performance with minimum MAD (0.0182), RMSE (0.0203), and high R2 (0.9914) values in the training phase and minimum MAD (0.0342), RMSE (0.0463), and high R2 (0.9841) values in the testing phase. The results of this study can help decision-makers to be aware of social-economic factors associated with waste management and ensure optimal usage of their resources in future planning.

Today’s Solid Waste Management System needs better strategies to perform effective operations. The speed at which waste is being generated in urban regions is directly affecting the surrounding environment impacting the health of living beings. Advancements in deep learning has made time series forecasting very efficient making better predictions. Recurrent Neural Network (RNN) is a deep learning approach which helps in analyzing and forecasting time series data by holding the complication of sequence dependencies. In this study, a RNN model, Regularized Noise based Gated Recurrent Unit (RNGRU) has been proposed to analyze and predict solid waste generation in Australia. The dataset is collected from www. oecd.org which has waste data 20780 records from 1990 to 2018. Various input features such as Region name, Year, Waste generated in tonnes and technical indicators such as Moving Average (MA), Exponential Moving Average (EMA), Moving Average Convergence/Divergence (MACD), Relative Strength Index (RSI), On Balance Volume (OBV), Momentum (MTM), Daily waste variation are used to train the model. The proposed RNGRU model is compared with LSTM model and the best model is chosen based on the performance metrics, Mean Absolute Error(MAE), Mean Squared Error (MSE), and Root Mean Squared Error(RMSE). The outcome of the experiments shows that the RNGRU model is best for prediction with low error rates, MAE value 0.0147, MSE value 0.0010 and RMSE value 0.0900 respectively.

The studies on the solid waste generation and characterization with their relationships with different determinants are limited in Nepal which helps in the implementation of the solid waste management approach. This study is focused on the quantification of the municipal solid waste (MSW) generation rate and its characterization in the Baglung Municipality, Nepal. Factors affecting MSW generation with perceptions towards the MSW management were also evaluated through household survey. The study was carried out in the month of January 2021 in four wards of urban zone in the municipality. The average waste generation rate in 188 households (HHs), 20 institutions, and 20 commercial sites were 0.43 kg/c/d, 0.83 kg/institute/day and 2.75 kg/commercial site/day, respectively. The largest component, which accounted for about 74% household waste, 75% institutional waste and 52% commercial waste composed of organic waste. The analysis of variance (ANOVA) indicates a significant variation in between MSW generation rate with respect to household size and type of days. Regarding affordability of MSW management, most of the households can be able to afford service fee ranged from US$ 0.5 to 1.0 (55.38%). Most of the households dispose of solid waste in the municipal vehicle. From this study, the residents of the municipality are suggested to prepare compost manure at the source for the minimization of waste volume to be transported and disposed of. Higher production of recyclable MSW depicts there is a possibility of revenue generation and importance of MSW management in community mobilization in the municipality.

This article uses machine learning techniques as fuzzy and neuro-fuzzy ANFISs, to develop and compare prediction models capable of relating pregnant women’s exposure to air pollutants, such as Nitrogen Dioxide and Particulate Matter, the mother’s age, and the number of prenatal consultations to the incidence of premature birth. In the current literature, studies can be found that relate prematurity to the exposure of pregnant women to NO2, O3, and PM10; to Toluene and benzene, mainly in the window 5 to 10 days before birth; and to PM10 in the week before birth. Both models used logistic regression to quantify the effects of pollutants as a result of premature birth. Datasets from Brazil—Departamento de Informatica do Sistema Único de Saúde (DATASUS) and Companhia Ambiental do Estado de São Paulo (CETESB)—were used, covering the period from 2016 to 2018 and comprising women living in the city of São José dos Campos (SP), Brazil. In order to evaluate and compare the different techniques used, evaluation metrics were calculated, such as correlation (r), coefficient of determination (R2), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), and Mean Absolute Error (MAE). These metrics are widely used in the literature due to their ability to evaluate the robustness and efficiency of prediction models. For the RMSE, MAPE, MSE, and MAE metrics, lower values indicate that prediction errors are smaller, demonstrating better model accuracy and confidence. In the case of (r) and R2, a positive and strong result indicates alignment and better performance between the real and predicted data. The neuro-fuzzy ANFIS model showed superior performance, with a correlation (r) of 0.59, R2 = 0.35, RMSE = 2.83, MAPE = 5.35%, MSE = 8.00, and MAE = 1.70, while the fuzzy model returned results of r = 0.20, R2 = 0.04, RMSE = 3.29, MSE = 10.81, MAPE = 6.67%, and MAE = 2.01. Therefore, the results from the ANFIS neuro-fuzzy system indicate greater prediction capacity and precision in relation to the fuzzy system. This superiority can be explained by integration with neural networks, allowing data learning and, consequently, more efficient modeling. In addition, the findings obtained in this study have potential for the formulation of public health policies aimed at reducing the number of premature births and promoting improvements in maternal and neonatal health.

In recent years, the topic of the effects of improper municipal solid waste management on health has gained worldwide concern. Due to a lack of funding and resources, this problem is seen to be more prevalent in developing nations than in developed nations. It has been demonstrated that solid waste creation, segregation procedures, storage facilities, collection schedules, and disposal techniques are not viable in developing nations. In these nations, open burning and careless dumping are frequent occurrences. Waste that is burned or dumped in the open poses’ major health and environmental hazards. They have resulted in serious contamination of the air, water, and land. Pollution from municipal solid waste raises disease morbidity and death. Hazardous wastes that are not managed pose a risk to human health. Therefore, the purpose of this study is to evaluate how poor municipal solid waste management affects the health strategy in Ara Municipal Corporation, Bihar. Regression analysis has been used to examine the relationship between the generation of solid waste and diseases. To determine how these factors affect human health, the study has included the generation and disposal of solid waste as independent variables and associated diseases as dependent variables. The research shows that poor waste management is strongly associated with negative health impacts, and diseases are strongly associated with the negative effects of solid waste generation and disposal. For waste management efforts and related research, it involves strengthening sectoral integration, stepping up advocacy, and obtaining funding. To fully comprehend how poor solid waste management affects human health strategy in Ara city, more study is essential.

This paper presents the development of a general regression neural network (GRNN) model for the prediction of annual municipal solid waste (MSW) generation at the national level for 44 countries of different size, population and economic development level. Proper modelling of MSW generation is essential for the planning of MSW management system as well as for the simulation of various environmental impact scenarios. The main objective of this work was to examine the potential influence of economy crisis (global or local) on the forecast of MSW generation obtained by the GRNN model. The existence of the so-called structural breaks that occur because of the economic crisis in the studied period (2000-2012) for each country was determined and confirmed using the Chow test and Quandt-Andrews test. Two GRNN models, one which did not take into account the influence of the economic crisis (GRNN) and another one which did (SB-GRNN), were developed. The novelty of the applied method is that it uses broadly available social, economic and demographic indicators and indicators of sustainability, together with GRNN and structural break testing for the prediction of MSW generation at the national level. The obtained results demonstrate that the SB-GRNN model provide more accurate predictions than the model which neglected structural breaks, with a mean absolute percentage error (MAPE) of 4.0% compared to 6.7% generated by the GRNN model. The proposed model enhanced with structural breaks can be a viable alternative for a more accurate prediction of MSW generation at the national level, especially for developing countries for which a lack of MSW data is notable.

With the in-depth implementation of China’s “National Strategy for Building a Strong Transportation Network,” the scale of expressway construction has continued to expand. As a result, the number of high-fill and deep-cut subgrade projects under complex geological conditions has increased significantly, leading to a surge in landslide-related issues. Consequently, accurate prediction of slope displacement is of critical importance for early warning and prevention of landslide disasters. This study proposes a hybrid prediction model, VMD-MPA-LSSVM-LSTM (VMLL), which integrates Variational Mode Decomposition (VMD), Marine Predators Algorithm (MPA), Least Squares Support Vector Machine (LSSVM), and Long Short-Term Memory (LSTM) networks. Using monitoring data from the high-fill embankment slope at Hongtuyao as the research subject, the VMLL model is employed to predict slope displacement based on small-sample data. The objective is to provide a more accurate method for early warning of slope displacement. Firstly, the original monitoring data are decomposed into trend displacement components and fluctuation displacement components using Variational Mode Decomposition (VMD). Subsequently, the trend component and the fluctuation component are predicted using Least Squares Support Vector Machine (LSSVM) and Long Short-Term Memory (LSTM) networks, respectively. Finally, the Marine Predators Algorithm (MPA) is employed to optimize the hyperparameters of the predictive models. Based on this framework, a VMLL-based slope displacement prediction model is constructed. To verify the superiority of the VMLL model, a comparative analysis was conducted against LSSVM, LSTM, and the VMD-LSSVM-LSTM models. The results demonstrate that the VMLL model achieves the highest prediction accuracy, with a Mean Absolute Percentage Error (MAPE) of 0.4022%, a Mean Absolute Error (MAE) of 0.016 mm, a Root Mean Square Error (RMSE) of 0.0206 mm, a coefficient of determination (R²) of 94.08%, and a Variance Accounted For (VAF) of 96.5%. Compared with the other three models, the VMLL model reduces the MAPE, MAE, and RMSE by 30.41–67.62%, 30.38–67.40%, and 27.32–71.00%, respectively. Meanwhile, it improves the R² and VAF by 5.95–217.51% and 0.38–11.48%, respectively. These improvements clearly demonstrate that the VMLL model outperforms the other models, indicating its significant advantage over single prediction models. Furthermore, three additional datasets were used to evaluate the model’s performance. The average values of the performance evaluation metrics for these datasets were 0.6207% (MAPE), 0.0238 mm(MAE), 0.0273 mm(RMSE), 90.33%(R²), and 96.81%(VAF), respectively. These results demonstrate the high accuracy and strong robustness of the proposed model in predicting both horizontal displacement and vertical settlement of slopes, providing a reliable methodological framework for slope stability assessment and landslide disaster early warning.

Advances in industrial digital technologies have led to an increasing volume of data generated from industrial bioprocesses, which can be utilised within data-driven models (DDM). However, data volume and variability complications make developing models that captures the underlying biological nature of the bioprocesses challenging. In this study, a framework for developing data-driven models of bioprocesses is proposed and evaluated by modelling an industrial bioprocess, which treats industrial or agrifood wastewaters whilst simultaneously generating bioenergy. Six models were developed to predict the reduction in chemical oxygen demand from the wastewater by the bioprocess and statistically evaluated using both testing data (randomly partitioned data from the model development) and unseen data (new data not used during the model development). The statistical error metrics employed were the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The stacked neural network model was best able to model the bioprocess, having the highest accuracy on the testing data (R2: 0.98; RMSE: 1.29; MAE: 2.27; MAPE: 4.08) and the unseen data (R2: 0.82; RMSE: 2.57; MAE: 1.75; MAPE: 3.68). Data visualisation is used to observe (or confirm) whether new data points are within the model boundaries, helping to increase confidence in the model’s predictions on future data.

Tomatoes are among the world’s most significant vegetables, both in terms of production and consumption. Harvesting takes place in tomato production when the important quality attribute of total soluble solids content reaches its maximum possible level. Tomato total soluble solids content (TSS) is among the most crucial attribute parameters for assessing tomato quality and for tomato commercialization. Determination of total soluble solids content by conventional measurement methods is both destructive and time-consuming. Therefore, the tomato processing industry needs a rapid identification method to measure total soluble solids content (TSS). In this study, we aimed to estimate how much soluble solids there are in beef tomato fruit by Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR) methods. The models were assessed using the Coefficient of Determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) metrics. The training data set results of the MLR model established to estimate the amount of brix in tomato fruit, calculated as MAE: 0.2349, RMSE: 0.3048, R2: 0.8441, and MAPE: 5.5368, while, according to the ANN model, MAE: 0.0250, RMSE: 0.031, R2: 0.9982 and MAPE: 0.5814. According to the metric outcomes, the ANN-based model performed better in both the training and testing parts.

Forecasting the quantity of municipal solid waste generation is an essential task for sustainable solid waste management and strategy implementation. The estimation of future waste generation rates can help to motivate for analyzing gaps in existing waste management and better planning strategies. Improper management and unsafe disposal of solid waste create a threat to the environment and human health. Hence, a sound forecasting of solid waste generation is very crucial for planning and management accordingly. Artificial intelligence is an excellent and new application of soft computing which is used as a forecasting tool. The main objective of this study is to apply ANN time series model along with autoregressive technique to forecast the monthly solid waste generation in Kolkata. For the same, data related to the monthly solid waste generation was gathered from 2010 to 2017. Total data of 96 months were divided into three categories, i.e., 70%, 15%, and 15% for training, validation, and testing, respectively. The model was evaluated based on performance value of mean square error, root mean square error, and regression coefficient. The ANN structure of 1-19-1 was considered as optimized model for solid waste forecasting because it has the lowest mean square error and the highest regression coefficient. The applied time series model forecasts that Kolkata will generate about 5205 MT/day municipal solid waste in 2030 which will add more than 1000 MT/day waste with the existing rate of generation. The present study helps in estimating and allocating essential resources that need in future for sound solid waste management and preparing alternative strategies to reach the sustainable goals.