AI Medical Compendium Journal:
Environmental monitoring and assessment

Showing 1 to 10 of 175 articles

Enhancing particulate matter prediction in Delhi: insights from statistical and machine learning models.

Environmental monitoring and assessment
This study advances our approach to modeling particulate matter levels-specifically, PM and PM-in Delhi's dynamic urban environment through an extensive evaluation of traditional time series models (ARIMAX, SARIMAX) and machine learning models (RF, S...

The integrated fuzzy AHP and fuzzy logic techniques for mapping and prioritizing groundwater potential zone based on water quality.

Environmental monitoring and assessment
Groundwater, which is utilized to supply water demand in various sectors such as domestic water consumption, agriculture, and industry, could be achieved by delineating a groundwater potential zone. Although mapping groundwater potential zones has be...

Geospatial artificial intelligence for detection and mapping of small water bodies in satellite imagery.

Environmental monitoring and assessment
Remote sensing (RS) data is extensively used in the observation and management of surface water and the detection of water bodies for studying ecological and hydrological processes. Small waterbodies are often neglected because of their tiny presence...

Surveillance of SARS-CoV-2 RNA in wastewater treatment plants in Türkiye, Istanbul: a long-term study and statistical analysis.

Environmental monitoring and assessment
Wastewater-based epidemiology (WBE) is a powerful method that allows community surveillance to identify diseases/pandemic dynamics in a city, especially in metropolitan areas with high overpopulation. This study investigated the detection and quantif...

Fate and speciation of NO in an arid climatic region: factors assessment.

Environmental monitoring and assessment
NO and NO continuously recycle in the lower atmosphere through a complex series of reactions involving NO, VOCs, NO, and O. Therefore, the NO/NO ratio can be utilized in dispersion models as an important substitute to understand the fate of NO and NO...

Modeling climate change impacts and predicting future vulnerability in the Mount Kenya forest ecosystem using remote sensing and machine learning.

Environmental monitoring and assessment
The Mount Kenya forest ecosystem (MKFE), a crucial biodiversity hotspot and one of Kenya's key water towers, is increasingly threatened by climate change, putting its ecological integrity and vital ecosystem services at risk. Understanding the intera...

Bayesian-optimized recursive machine learning for predicting human-induced changes in suspended sediment transport.

Environmental monitoring and assessment
The suspended sediment load (SSL) of a river is a key indicator of water resource management, river morphology, and ecosystem health. This study analyzes historical changes in SSL and evaluates machine learning (ML) models for SSL prediction in the G...

Estimating soil cadmium concentration using multi-source UAV imagery and machine learning techniques.

Environmental monitoring and assessment
Urbanization and industrialization have led to widespread soil heavy metals contamination, posing significant risks to ecosystems and human health. Conventional methods for mapping heavy metal distribution, which rely on soil sampling followed by che...

Water quality parameters retrieval and nutrient status evaluation based on machine learning methods and Sentinel- 2 imagery: a case study of the Hongjiannao Lake.

Environmental monitoring and assessment
A timely and accurate understanding of lake water quality is significant for maintaining ecological balance, ensuring water resource security, and promoting regional sustainable development. However, due to the varying numerical ranges and characteri...

Variability analysis of soil organic carbon content across land use types and its digital mapping using machine learning and deep learning algorithms.

Environmental monitoring and assessment
Soil organic carbon (SOC) plays a crucial role in carbon cycle management and soil fertility. Understanding the spatial variations in SOC content is vital for supporting sustainable soil resource management. In this study, we analyzed the variability...