Modeling Sustainability Indicators Using Artificial Neural Networks: a Pm10 Case Study
نویسندگان
چکیده
This paper describes the development of artificial neural network models for the prediction of annual PM10 emissions and concentrations, by using available sustainability and economical/ industrial parameters as inputs. A genetic algorithm and correlation analysis were used as methods for the selection of inputs for the PM10 emissions and the model of the PM10 concentrations, respectively. The model performance was analyzed using multiple statistical indicators and it was demonstrated that the ANN model provides considerably better results than the compared principal component regression model. The wide availability of the input parameters used in the created ANN models could overcome the lack of sustainability indicators in many countries.
منابع مشابه
Prediction of monthly rainfall using artificial neural network mixture approach, Case Study: Torbat-e Heydariyeh
Rainfall is one of the most important elements of water cycle used in evaluating climate conditions of each region. Long-term forecast of rainfall for arid and semi-arid regions is very important for managing and planning of water resources. To forecast appropriately, accurate data regarding humidity, temperature, pressure, wind speed etc. is required.This article is analytical and its database...
متن کاملModeling environmental indicators for land leveling, using Artificial Neural Networks and Adaptive Neuron-Fuzzy Inference System
Land leveling is one of the most important steps in soil preparation and cultivation. Although land leveling with machines requires considerable amount of energy, it delivers a suitable surface slope with minimal soil deterioration as well as damage to plants and other organisms in the soil. Notwithstanding, in recent years researchers have tried to reduce fossil fuel consumption and its delete...
متن کاملModeling environmental indicators for land leveling, using Artificial Neural Networks and Adaptive Neuron-Fuzzy Inference System
Land leveling is one of the most important steps in soil preparation and cultivation. Although land leveling with machines requires considerable amount of energy, it delivers a suitable surface slope with minimal soil deterioration as well as damage to plants and other organisms in the soil. Notwithstanding, in recent years researchers have tried to reduce fossil fuel consumption and its delete...
متن کاملModeling of streamflow- suspended sediment load relationship by adaptive neuro-fuzzy and artificial neural network approaches (Case study: Dalaki River, Iran)
Modeling of stream flow–suspended sediment relationship is one of the most studied topics in hydrology due to itsessential application to water resources management. Recently, artificial intelligence has gained much popularity owing toits application in calibrating the nonlinear relationships inherent in the stream flow–suspended sediment relationship. Thisstudy made us of adaptive neuro-fuzzy ...
متن کاملFlood Forecasting Using Artificial Neural Networks: an Application of Multi-Model Data Fusion technique
Floods are among the natural disasters that cause human hardship and economic loss. Establishing a viable flood forecasting and warning system for communities at risk can mitigate these adverse effects. However, establishing an accurate flood forecasting system is still challenging due to the lack of knowledge about the effective variables in forecasting. The present study has indicated that th...
متن کامل