Forecasting and meta-features estimation of wastewater and climate change impacts in coastal region using manifold learning

South Asia's coastlines are the most densely inhabited and economically active ecosystems have already begun to shift due to climate change. Over the past century, climate change has contributed to a gradual and considerable rise in sea level, which has eroded shorelines and increased storm-related coastal flooding. The differences in estuary water quality over time, both seasonally and annually, have been efficiently controlled by changes in stream flow. Assessment requires digitized analytical platforms to lower the risk of catastrophes associated with climate change in coastal towns. To predict future changes in an area's vulnerability and waste planning de-cisions, a prospective investigation requires qualitative and quantitative scenarios. The paper concentrates on the development of a forecasting platform to evaluate the climate change and waste water impacts on the south coastal region of India. Due to the enhancement of Digitization, a multi-model ensemble combined with manifold learning is implemented on the multi-case models influencing the uncertainty probability rate of 23% and can be ignored with desired precaution on the coastal environmental. Because Manifold Learning Analysis results cannot be utilized directly in wastewater management studies because of their inherent biases, a statistical bias correction and meta-feature estimation have been implemented. Within the climate-hydrology modeling chain, the results demonstrate a wide range of expected changes in water resources in some places. Experimental statistics reveal that the forecasted rate of 91.45% will be the better choice to reduce the uncertainty of climatic change and wastewater management.