TY - JOUR
T1 - Forecasting solid waste generation
T2 - A Fourier series approach
AU - Asante-Darko, Disraeli
AU - Adabor, Emmanuel Sarkodie
AU - Amponsah, Samuel Kwame
N1 - Publisher Copyright:
Copyright © 2017 Inderscience Enterprises Ltd.
PY - 2017
Y1 - 2017
N2 - Successful planning of a solid waste management system depends on the accuracy of prediction of solid waste generation. With a continual economic development and increase in the living standards, the demand for goods and services is increasing at an unprecedented rate, resulting in a commensurate increase in per capita waste generation. In order to facilitate informed decision making for an effective solid waste management, we propose a Fourier Series Model to forecast solid waste generation in Kumasi, Ghana. A monthly waste data from 2007 to 2014 was obtained from the solid waste department of the Kumasi Metropolitan Assembly, Ghana. This was used to formulate the Fourier series model for forecasting solid waste. This novel application incorporates the characteristics of the data making them it appropriate for forecasting solid waste. MAPE and RMSE comparison of our proposed model with existing method for forecasting solid waste shows that our method competes favourably well.
AB - Successful planning of a solid waste management system depends on the accuracy of prediction of solid waste generation. With a continual economic development and increase in the living standards, the demand for goods and services is increasing at an unprecedented rate, resulting in a commensurate increase in per capita waste generation. In order to facilitate informed decision making for an effective solid waste management, we propose a Fourier Series Model to forecast solid waste generation in Kumasi, Ghana. A monthly waste data from 2007 to 2014 was obtained from the solid waste department of the Kumasi Metropolitan Assembly, Ghana. This was used to formulate the Fourier series model for forecasting solid waste. This novel application incorporates the characteristics of the data making them it appropriate for forecasting solid waste. MAPE and RMSE comparison of our proposed model with existing method for forecasting solid waste shows that our method competes favourably well.
KW - Forecasting
KW - Fourier series
KW - MAPE
KW - Mean absolute percentage error
KW - RMSE
KW - Root mean squared error
KW - Solid waste
KW - Waste management
UR - http://www.scopus.com/inward/record.url?scp=85021183799&partnerID=8YFLogxK
U2 - 10.1504/IJEWM.2017.084640
DO - 10.1504/IJEWM.2017.084640
M3 - Article
AN - SCOPUS:85021183799
SN - 1478-9876
VL - 19
SP - 318
EP - 337
JO - International Journal of Environment and Waste Management
JF - International Journal of Environment and Waste Management
IS - 4
ER -