Monitoring and predicting spatio-temporal dynamics of river bankline movements: a case study for land use risk management in the lower Ganga River, India.

Changing the river course in the alluvial plain region is a common phenomenon that may have disastrous consequences. The risk of river bank erosion has increased dramatically during the last few decades. As a result, assessing the river bankline alteration is necessary.

Optimisation and interpretation of machine and deep learning models for improved water quality management in Lake Loktak.

Loktak Lake, one of the largest freshwater lakes in Manipur, India, is critical for the eco-hydrology and economy of the region, but faces deteriorating water quality due to urbanisation, anthropogenic activities, and domestic sewage. Addressing the urgent need for effective pollution management, this study aims to assess the lake's water quality status using the water quality index (WQI) and develop advanced machine learning (ML) tools for WQI assessment and ML model interpretation to improve pollution management decision making. The WQI was assessed using entropy-based weighting arithmetic and three ML models - Gradient Boosting Machine (GBM), Random Forest (RF) and Deep Neural Network (DNN) - were optimised using a grid search algorithm in the H2O Application Programming Interface (API).