AIMC Topic: Environmental Monitoring

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Predictive model for progressive salinization in a coastal aquifer using artificial intelligence and hydrogeochemical techniques: a case study of the Nile Delta aquifer, Egypt.

Environmental science and pollution research international
To monitor groundwater salinization due to seawater intrusion (SWI) in the aquifer of the eastern Nile Delta, Egypt, we developed a predictive regression model based on an innovative approach using SWI indicators and artificial intelligence (AI) meth...

Predicting spatiotemporally-resolved mean air temperature over Sweden from satellite data using an ensemble model.

Environmental research
Mapping of air temperature (Ta) at high spatiotemporal resolution is critical to reducing exposure assessment errors in epidemiological studies on the health effects of air temperature. In this study, we applied a three-stage ensemble model to estima...

Deep learning-based remote sensing estimation of water transparency in shallow lakes by combining Landsat 8 and Sentinel 2 images.

Environmental science and pollution research international
Water transparency is a key indicator of water quality as it reflects the turbidity and eutrophication in lakes and reservoirs. To carry out remote sensing monitoring of water transparency rapidly and intelligently, deep learning technology was used ...

Feature extraction and machine learning techniques for identifying historic urban environmental hazards: New methods to locate lost fossil fuel infrastructure in US cities.

PloS one
U.S. cities contain unknown numbers of undocumented "manufactured gas" sites, legacies of an industry that dominated energy production during the late-19th and early-20th centuries. While many of these unidentified sites likely contain significant le...

Cyanobacteria cell prediction using interpretable deep learning model with observed, numerical, and sensing data assemblage.

Water research
Massive cyanobacterial blooms in river water causes adverse impacts on aquatic ecosystems and water quality. Complex and diverse data sources are available to investigate the cyanobacteria phenomena, including in situ data, synthetic measurements, an...

Improving streamflow simulation by combining hydrological process-driven and artificial intelligence-based models.

Environmental science and pollution research international
Accurate and timely monitoring of streamflow and its variation is crucial for water resources management in watersheds. This study aimed at evaluating the performance of two process-driven conceptual rainfall-runoff models (HBV: Hydrologiska ByrÄns V...

Prediction of soil-bearing capacity on forest roads by statistical approaches.

Environmental monitoring and assessment
The soil-bearing capacity is one of the important criteria in dimensioning the superstructure. In Turkey, predictability of California Bearing Ratio values, which may be used in the planning and dimensioning of forest roads, of which about 26% lacks ...

Novel approach for predicting groundwater storage loss using machine learning.

Journal of environmental management
Comprehensive national estimates of groundwater storage loss (GSL) are needed for better management of natural resources. This is especially important for data scarce regions with high pressure on groundwater resources. In Iran, almost all major grou...

Global soil moisture data derived through machine learning trained with in-situ measurements.

Scientific data
While soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term data...

Towards automatic airborne pollen monitoring: From commercial devices to operational by mitigating class-imbalance in a deep learning approach.

The Science of the total environment
Allergic diseases have been the epidemic of the century among chronic diseases. Particularly for pollen allergies, and in the context of climate change, as airborne pollen seasons have been shifting earlier and abundances have been becoming higher, p...