Stream salinity prediction in data-scarce regions: Application of transfer learning and uncertainty quantification.

Scarcity of stream salinity data poses a challenge to understanding salinity dynamics and its implications for water supply management in water-scarce salt-prone regions around the world. This paper introduces a framework for generating continuous daily stream salinity estimates using instance-based transfer learning (TL) and assessing the reliability of the synthetic salinity data through uncertainty quantification via prediction intervals (PIs). The framework was developed using two temporally distinct specific conductance (SC) datasets from the Upper Red River Basin (URRB) located in southwestern Oklahoma and Texas Panhandle, United States.

Assessing long-term climate change impact on spatiotemporal changes of groundwater level using autoregressive-based and ensemble machine learning models.

To evaluate the long-term climate change impacts on groundwater fluctuations of the Ardabil plain, Iran, a groundwater level (GWL) modeling was proposed in this study. Accordingly, the outputs of Global Climate Models (GCMs) under the sixth report of Coupled Model Intercomparison Project (CMIP6) and future scenario of the Shared Socioeconomic Pathway 5-8.5 (SSP5-8.