ABSTRACT
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. Additionally, classification and regression models were employed as base learners in developing this algorithm. The newly developed HEML datasets were compared with other ML and SRPP products for super-drought monitoring. The Standardized precipitation evapotranspiration index (SPEI) was used to estimate super drought characteristics, including Drought frequency (DF), Drought Duration (DD), and Drought Intensity (DI) from machine learning and SRPPs products in LVB and compared with RG observation. The results revealed that the HEML algorithm shows excellent performance (CC = 0.93) compared to the single ML merging method and SRPPs against observation. Furthermore, the HEML merging product adeptly captures the spatiotemporal patterns of super drought characteristics during both training (1984-2009) and testing (2010-2019) periods. This research offers crucial insights for near-real-time drought monitoring, water resource management, and informed policy decisions.
ABSTRACT
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. In contrast, the sediment samples were found to be significantly contaminated with Cr. Heavy metal contamination occurs in the following order, according to our research: Cr > Pb > Cu > Co > Mn > Zn > Fe. Except for sites 8, 10, 11, and 13, where PLI>1 implies that there is no pollution in this area, the PLI values show that most of the locations are contaminated. The ecological risk index (ERI) values for five metals in the study areas are as follows: Cr > Pb > Cu > Mn > Zn. The sediment samples fall into the low-risk and highly polluted to dangerous sediment categories for SPI, according to the Risk index (RI). Based on the Mean Effect Range-Median Quotient (M-ERM-Q), Cu, Pb, Zn, and Cr metals in the research region have a 9-21% probability of being harmful. Statistical approaches show that the majority of heavy metals in sediments are of natural origin. The spatial distribution of heavy metals in surface sediments provides the conceptual framework for practical strategies to protect coastal areas. Many shreds of evidence indicate that anthropogenic inputs from the surrounding land area are primarily responsible for the deposition of these heavy metals in the coastal zone.