Related Experiment Video
Updated: Aug 17, 2026
12:44
Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
Forecasting municipal solid waste generation in a fast-growing urban region with system dynamics modeling
1Department of Environmental Engineering, Texas A&M University-Kingsville, MSC 213, Kingsville, TX 78363, USA.
Waste Management (New York, N.Y.)
|July 13, 2005
Summary
Accurate solid waste generation prediction is vital for municipal planning. System dynamics modeling offers a novel approach to forecast waste trends in fast-growing urban areas using limited data.
Area of Science:
- Environmental Science
- Urban Planning
- Systems Engineering
Background:
- Accurate solid waste generation prediction is crucial for effective municipal solid waste management planning and design.
- Fast-growing urban regions face challenges in predicting waste generation trends due to limited historical data and capacity constraints.
- Incomplete records hinder long-term system planning and short-term expansion programs for waste management.
Purpose of the Study:
- To develop and apply a new analytical approach for predicting solid waste generation with reasonable accuracy, especially with limited data samples.
- To assess the impact of solid waste generation on sustainable urban development.
- To evaluate the practical implementation of the proposed approach through a case study in a rapidly developing urban area.
Main Methods:
- The study introduces system dynamics modeling as a novel approach for solid waste generation prediction.
- A case study was conducted in San Antonio, Texas, a rapidly growing region influenced by the North American Free Trade Agreement (NAFTA).
- Five different solid waste generation models were analyzed using the Stella simulation tool.
Main Results:
- System dynamics modeling effectively predicts solid waste generation trends in fast-growing urban areas, even with limited data.
- The approach demonstrates the ability to incorporate various causative models and manage uncertainties.
- Traditional statistical methods, such as least-squares regression, are less effective in handling these complex scenarios.
Conclusions:
- System dynamics modeling provides a robust framework for forecasting solid waste generation in dynamic urban environments.
- This method enhances the ability to plan and manage municipal solid waste systems more effectively, supporting sustainable development.
- The findings suggest a valuable alternative to traditional statistical methods for waste management prediction.
Related Concept Videos
01:25Modeling with Differential Equations
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
01:26Exponential Equations for Modeling Growth
Exponential models are essential for describing rapid, multiplicative changes in natural systems, such as population growth. When a population doubles at regular intervals, the process can be modeled using a suitable base. For instance, a bacterial culture that doubles every three hours follows the model n(t)=n0⋅2t/3, where n(t) is the population at the time t.A more general model uses the natural base e, especially for continuous growth. This takes the form n(t)=n0⋅ert, where r is the relative...
01:27Growth Models with Integration: Problem Solving
In population modeling, integration provides a systematic way to determine accumulated quantities from known rates of change. One such application arises in ecology, where the total weight of a fish population in a body of water is referred to as its biomass. When the rate of growth of this biomass is known as a function of time, calculus can be used to determine the total biomass at a future date.Growth Rate and Biomass FunctionLet the growth rate of the fish population be represented by a...
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
01:29Exponential Equations with Logarithms: Problem Solving
In ecological studies, exponential models are often used to predict how populations grow over time under favorable conditions. These models assume that the growth rate is proportional to the current population, leading to continuous and compounding increases.The model expresses the population as a function of time, combining the initial population with a growth factor raised to an exponent involving the growth rate and time. To estimate how long it takes for a population to reach a specific...
01:21Design Example: Creating a Hydraulic Model of a Dam Spillway
Scaled hydraulic models of dam spillways provide a practical way to replicate and study the intricate flow dynamics of these structures. Often built to a 1:15 ratio, these models allow for observing critical water behavior, such as velocity distribution, flow patterns, and energy dissipation.