There has been an unsettling rise in the river contamination due to the climate change and
anthropogenic activities. Last decades' research has immensely focussed on river basin …

The goal of this paper was to conduct a systematic literature analysis on the application of
different types of artificial intelligence models in surface water quality monitoring. The …

Remotely sensed hyperspectral data can support more effective water quality monitoring.
Nevertheless, the variability and complexity of urban river water make it hard to retrieve …

Numerous conventional methods are available for analyzing various water quality
parameters to determine the water quality index. However, ongoing surveillance is …

The feasibility of artificial intelligence (AI) as a predictive model for thorough efficacy
analysis on environmental pollution applied on mangrove forests are discussed. Mangrove …

The smarter world needs more efforts to purposeful manage and usage of technologies,
science, artificial intelligence, and artificial neural networks, as their product. One of the main …

Updating the physicochemical properties of surface water is important to water quality
researchers and decision-makers. Routine surface water quality monitoring methods are …

Traditional surface water quality assessment is costly, labor intensive, and time consuming;
however, remote sensing has the potential to assess surface water quality because of its …

The changing climate and economic development have exerted significant pressure on the
water quality of global lakes and reservoirs (hereinafter referred to as lakes). Existing …

Estimation of surface water quality parameters (SWQPs) from satellite imagery is a cost-
effective method. Remote sensing algorithms are developed to map the relationship …