A hybrid ensemble learning merging approach for enhancing the super drought computation over Lake Victoria Basin.

This study introduces a novel Hybrid Ensemble Machine-Learning (HEML) algorithm to merge long-term satellite-based reanalysis precipitation products (SRPPs), enabling the estimation of super drought events in the Lake Victoria Basin (LVB) during the period of 1984 to 2019. This study considers three widely used Machine learning (ML) models, including RF (Random Forest), GBM (Gradient Boosting Machine), and KNN (k-nearest Neighbors), for the emerging HEML approach. The three SRPPs, including CHIRPS (Climate Hazards Group Infra-Red Precipitation with Station), ERA5-Land, and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Climate Data Record), were used to merge for developing new precipitation estimates from HEML model.

Environmental hazard assessment and metal contamination in coastal sediments.

In order to reduce contamination levels from diverse sources, it is important to understand the factors affecting the natural ecosystems that are impacted by coastal and marine pollution. In this study, we used GIS and remote sensing techniques to investigate and evaluate the distribution of heavy metals (Fe, Mn, Zn, Cr, Pb, Co, and Cu) in surface sediments along Tamil Nadu's East Coast (from Besant Nagar to Sathurangapattinam). The CF and Igeo of metals indicate that sediments contain no evidence of Fe, Mn, or Zn metal pollution in the sediments, with only mild contamination from Co, Cu, and Pb.