1. Introduction: Knowing the quantity of generated solid waste play a very significant role in solid waste management programs in a region. Due to lack of measured data as well as unavoidable errors in measurements, assessment of volume of generated solid waste is always challenging. Also, field measurement and continues monitoring of the volume of solid waste is usually costly, difficult and time-consuming. Accurate prediction of solid waste generation can be regarded as a key factor in future solid waste management system planning. Conventional forecasting methods in solid waste generation forecasting frequently use the demographic and socioeconomic factors in a per capita basis. In most cases, insufficient funds, the limited measuring equipment, lack of appropriate management systems and due to the lack of recorded data for the volume of generated solid waste cause many problems in integrated solid waste systems management (Dyson and Chang, 2005). In this study, three computational intelligence techniques including M5P model trees, support vector machines (SVM) and multi-layer perceptron (MLP) artificial neural network are utilized to predict solid waste generation in Hormozgan Province, Iran. After a sensitivity analysis, four more influential factors including elevation, population, urban development index (measures the level of development in cities based on infrastructure, the municipality established year, the metropolitan area, population, city product and income, health and education) and the frequency of garbage collection were considered in developing models. The performance of proposed models in solid waste generation forecasting are assessed via different error evaluation indices and finally the results are compared...

Forecasting of municipal waste generation is a critical challenge for decision making and planning, because proper planning and operation of a solid waste management system is intensively affected by municipal solid waste (MSW) streams analysis and accurate predictions of solid waste quantities generated. Due to dynamic and complexity of solid waste management system, models by artificial intelligence can be a useful solution of this problem. In this paper, a novel method of Forecasting MSW generation has been proposed.Here, support vector machine (SVM) as an intelligence tool combined with partial least square (PLS) as a feature selection tool was used to weekly prediction of MSW generated in Tehran, Iran. Weekly MSW generated in the period of 2008 to 2011 was used as input data for model learning. Moreover, Monte Carlo method was used to analyze uncertainty of the model results. Model performance evaluated and compared by statistical indices of Relative Mean Errors, Root Mean Squared Errors, Mean Absolute Relative Error and coefficient of determination. Comparison of SVM and PLS-SVM model showed PLS-SVM is superior to SVM model in predictive ability and calculation time saving. Also, results demonstrate which PLS could successfully identify the complex nonlinearity and correlations among input variables and minimize them. The uncertainty analysis also verified that the PLS-SVM model had more robustness than SVM and had a lower sensitivity to change of input variables.

Introduction: Solid waste reduction is a key and fundamental factor in creating a sustainable society. Tehran Municipality has embarked on a series of positive measures in recent years in different areas of waste management such as source separation, mechanized waste collection, and constructing compost factories. However these measures have not only brought about any reduction in solid waste reduction but have also resulted in their increase. In this article, first we will describe the current situation of waste management in Tehran. Then since having an understanding of the type and amount of the generated solid waste is important in defining strategies and programs aiming at reduction of waste generation, we manage to have evaluation of the current situation of municipal waste generation in 22 regions of Tehran.Methods: The study was a descriptive cross-sectional one conducted from 2010 to 2014. Relevant officials of the waste recovery in 22 regions of Tehran were approached in order to collect data about municipal solid waste generation through interviewing, filling out questionnaires, conducting field visits from Aradkooh Disposal and Processing Complex and collecting information on disposal and destiny of wastes. Then the data were compiled and analyzed.Results: Total solid waste generation in Tehran from 2010 to 2014 amounted to respectively 3389662, 3399344, 3449338 and 3245157 Metric Tons, categorized into three groups of municipal, companies and townships and hospital wastes. Most of the generated waste produced in Tehran was that of households and commercial (known as municipal waste) from 22 Regions of Tehran. Based on the surveys conducted, per capita solid waste generation of 11 regions of Tehran ranged from 550 to 1000 grams and in other 11 ones from 1000 to 1521 grams per capita per day. The lowest and highest waste generation rate belonged respectively to region 13 with 556 grams and region 12 with 1521 grams per capita per day in 2011.Conclusion: Comparing per capita generation of municipal solid waste in different municipal regions in Tehran with maximum acceptable capacity of waste generation indicates the deviation of waste generation of all Tehran regions from the standard acceptable amount. Therefore, not only is it necessary to plan and take strategic measures to reduce Tehran waste generation but also these programs and measures should be specific to each region considering its specifications and solid waste quality and quantity.

Background and purpose: Physical analysis of solid waste is the first step in waste management. In Iran, no data is available about rural solid wastes in the country. The aim of this study was to determine qualitative and quantitative analysis of rural solid wastes in Iran.Materials and methods: In this cross-sectional study, the data for national rural solid waste in 2012 was obtained from Iranian State Municipalities and Village Assistance Offices organization. Then the generation, per capita and physical composition of solid waste in rural areas in Iran were compared.Excel and SPSS V.17 were applied to analyze the data.Results: The average solid waste generation per capita was 444 g per day and total wastes in Iran's rural areas was estimated at around 3.5 million tons per year. The majority of country’s rural solid wastes consisted of organic materials (52.53%) and plastics (16%) were the most valuable dry solid wastes.Conclusion: By applying composting method, not only the fertilizing capacity of the waste is used but also leachate, offensive odor and toxic gas generation would decrease. Recycling of dry solid waste in rural areas would reduce their cost of collection and disposal and also increases the profit from selling recycled materials.

Background and Objective: The first step in design of municipal waste management systems is complete understanding of waste generation quantity. Forecasting waste generation is one of the most complex engineering problems due to the effect of various and out of control parameters on waste generation. Therefore, it is obvious that it is necessary to develop approaches to a model such complex events. The objective of this study is forecasting waste generation quantity using intelligent models as well as their comparisons and uncertainty analysis. Method: In this study, Mashhad city was selected as a case study and waste generation time series of waste generation in 1380 to 1390 were used for weekly prediction. Intelligent models including artificial neural network, support vector machine, adaptive neuro-fuzzy inference system as well as Knearest neighbors were used for modelling. After optimizing the models’ parameters, models’ accuracy were compared by statistical indices. Finally, result uncertainty of the models was done by Mont Carlo technique. Findings: Results showed that coefficient of determination (R2) of artificial neural network adaptive neuro-fuzzy inference system, support vector machine, and K-nearest neighbor models were 0.67, 0.69, 0.72 and 0.64 respectively. Uncertainty analysis was also justified the results and demonstrates that support vector machine model had the lowest uncertainty among other models and the lowest sensitivity to input variables. Conclusion: Intelligent models were successfully able to forecast waste quantity and among the studied models, support vector machine was the best predictive model. Moreover, support vector machine produced the results with the lowest uncertainty the other models.

The objective of this study was to develop a forecast model to determine the rate of generation of municipal solid waste in the municipalities of the Cuenca del Cañ ó n del Sumidero, Chiapas, Mexico. Multiple linear regression was used with social and demographic explanatory variables. The compiled database consisted of 9 variables with 118 specific data per variable, which were analyzed using a multicollinearity test to select the most important ones. Initially, different regression models were generated, but only 2 of them were considered useful, because they used few predictors that were statistically significant. The most important variables to predict the rate of waste generation in the study area were the population of each municipality, the migration and the population density. Although other variables, such as daily per capita income and average schooling are very important, they do not seem to have an effect on the response variable in this study. The model with the highest parsimony resulted in an adjusted coefficient of 0. 975, an average absolute percentage error of 7. 70, an average absolute deviation of 0. 16 and an average root square error of 0. 19, showing a high influence on the phenomenon studied and a good predictive capacity.

Background and Objective: Knowledge about the quantity of municipal solid waste (MSW) generation plays a key role in formulating policies of waste management. So far, different methods have been applied to estimate the quantity of waste generation. In this study, eight specific forms of mathematical functions were evaluated to predict waste generation by the regression analysis method based on population. Materials and Methods: The significance test of each model and the existence necessity of predictor parameters were performed using the F-and t-statistic, respectively. The statistical indicators of determination coefficient ( R2 ), adjusted determination coefficient ( 2 Adjusted R ), root mean square error (RMSE), mean bias error (MBE) and mean percentage error (MPE) were used for model’ s goodness of fit. The predicted determination coefficient ( 2 Pr edicted R ) was calculated to assess the predictive ability of models by method of Leave-one-out cross validation. Results: The results showed that polynomial models of second order and more are not significant (at 0. 01 level) despite good accuracy and are not suitable for long-term prediction. Linear, power and exponential models are best with R2 equal to 0. 942, 0. 932 and 0. 936 and 2 Pr edicted R equal to 0. 904, 0. 893 and 0. 898 respectively. However, the uncertainty was greater in the exponential model. Conclusion: The status of waste generation was investigated in four scenarios based on growth rate of population (increasing, fixing and decreasing births) at Tehran metropolis in 2021-2051. In all scenarios, annual generation and per capita of waste are increased to 2051. The daily waste generation will increase to 12317 ton in 2051.