
This study investigates the dynamics of environmental and economic indicators by examining time series data to understand their impact on sustainable development and environmental management. This study analyzes the data obtained from different sectors by determining the trends of the main variables such as Agriculture, Forestry and Fishing (AFF), Construction (C), Electricity, Gas, Steam and Air Conditioning Supply (EGSACS), Manufacturing (MAN), Mining (MIN), Other Services Industries (OSI), Total Households (TH), Total Industry and Households (TIH), Transportation and Storage (TS), Water supply; sewerage, waste management and remediation activities (WSSWMRA) and uses Artificial Neural Networks for data analysis. The results show that; it can be said that the artificial neural network produces results very close to the real values.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
