Thermal power plants pollution assessment based on deep neural networks, remote sensing, and GIS: A real case study in Iran

To investigate the impact of the Bandar Abbas thermal power plant on the waters of the Persian Gulf coast, a combination of satellite images and ground data was utilized to determine the Sea Surface Temperature (SST) as a thermal index, Total Organic Carbon (TOC) and Chemical Oxygen Demand (COD) as biological indices. Additionally, measurements of SO2, O3, NO2, CO2, CO, and CH4 values in the atmosphere were taken to determine the plant's impact on air pollution. Temperature values of the water for different months were pre-dicted using Long Short-Term Memory (LSTM), Support Vector Regression (SVR), and Cascade neural networks. The results indicate that the waters near thermal power plants exhibit the highest temperatures in July and September, with temperatures reaching approximately 50 degrees C. Furthermore, the SST values were found to be strongly correlated with ecological indices. The Multiple Linear Regression (MLR) analysis revealed a strong correlation between the temperature and TOC, COD, and O2 in water (R2TOC = 0.98), R2O2 = -0.89, R2COD = 0.87 and O3, NO3, CO2, and CO in the air (R2O3 = 0.99, R2NO3 = 0.97, R2CO2= 0.95, R2CO = 0.96). Finally, the results demonstrate that the LSTM method exhibited high accuracy in predicting the water temperature (R2 = 0.98).

Automated summary

A combination of satellite images and ground data was used to investigate the impact of the Bandar Abbas thermal power plant on the waters of the Persian Gulf coast. Sea Surface Temperature (SST), Total Organic Carbon (TOC), and Chemical Oxygen Demand (COD) were determined as thermal and biological indices. Measurements of atmospheric pollutants were also taken. The study found a strong correlation between water temperature and ecological indices, with the LSTM method showing high accuracy in predicting water temperature.