REMOTE SENSING IMAGES FOR WATER QUALITY MONITORING BASED ON DEEP LEARNING MODEL

For the growing number of ecological issues that adversely affect natural systems, accurate assessment and monitoring of ecological functioning was essential as a prerequisite for a better response. To obtain a reliable performance of environmental control and the following evaluation, this study built an integrated innovative framework with 3 layers that combine CMT, DL and RST. To acquire better precise and enhanced monitoring results, the FMRCNN a type of DL modifies and efficiently utilizes the big data platform offered by RST and CMT. Our case study at PIL showed that this proposed system has an efficient performance than the existing system. The time series were created for each feature in PIL water bodies using all available data, covering the years 2017 to 2022. Next, we find unusual data points on time series using the EDA anomaly detection approach. The EDA approach successfully recognises abnormal events in WQ based on other studies. Furthermore, the relationship between non-optically active variables such as pH, DO, BGA, Chl, Turbidity and fDOM is very strong, according to our findings. The proposed methodology improves the performance measures reliable and effective than the existing system to monitor indoor WQ at the PIL.