Intercomparison of deep learning models in predicting streamflow patterns: insight from CMIP6.

This research was carried out to predict daily streamflow for the Swat River Basin, Pakistan through four deep learning (DL) models: Feed Forward Artificial Neural Networks (FFANN), Seasonal Artificial Neural Networks (SANN), Time Lag Artificial Neural Networks (TLANN) and Long Short-Term Memory (LSTM) under two Shared Socioeconomic Pathways (SSPs) 585 and 245. Taylor Diagram, Random Forest, and Gradient Boosting techniques were used to select the best combination of General Circulation Models (GCMs) for Multi-Model Ensemble (MME) computation. MME was computed via the Random Forest technique for Maximum Temperature (T), Minimum Temperature (T), and precipitation for the aforementioned three techniques.

Prediction of sustainable management of sediment in rivers and reservoirs.

Water structure construction has been the subject of heated discussion for more than a decade. The following concepts include integrated management of water basins or continued sustainable development of water basins. Although there are not a lot of underdeveloped water basins, there have been 150 years of dam development.