AIMC Topic: Environmental Monitoring

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Development of a recurrent spatiotemporal deep-learning method coupled with data fusion for correction of hourly ozone forecasts.

Environmental pollution (Barking, Essex : 1987)
Ambient ozone (O) predictions can be very challenging mainly due to the highly nonlinear photochemistry among its precursors, and meteorological conditions and regional transport can further complicate the O formation processes. The emission-based ch...

Predicting spatial variations in annual average outdoor ultrafine particle concentrations in Montreal and Toronto, Canada: Integrating land use regression and deep learning models.

Environment international
BACKGROUND: Concentrations of outdoor ultrafine particles (UFP; <0.1 µm) and black carbon (BC) can vary greatly within cities and long-term exposures to these pollutants have been associated with a variety of adverse health outcomes.

Environmental vulnerability evolution in the Brazilian Amazon.

Anais da Academia Brasileira de Ciencias
Decision making and environmental policies are mainly based on propensity level to impact in the area. The propensity level can be determined through artificial intelligence techniques included in geotechnological universe. Thus, this study aimed to ...

HRFSVM: identification of fish disease using hybrid Random Forest and Support Vector Machine.

Environmental monitoring and assessment
Aquaculture fish diseases pose a serious threat to the security of food supplies. Fish species vary widely, and because they resemble one another so much, it is challenging to distinguish between them based solely on appearance. To stop the spread of...

Machine learning approaches for data-driven process monitoring of biological wastewater treatment plant: A review of research works on benchmark simulation model No. 1(BSM1).

Environmental monitoring and assessment
In the past decade, machine learning techniques have seen wide industrial applications for design of data-based process monitoring systems with an aim to improve industrial productivity. An efficient process monitoring system for wastewater treatment...

Modeling dissolved oxygen concentration using machine learning techniques with dimensionality reduction approach.

Environmental monitoring and assessment
Oxygen is crucial to keep the life cycle balance in any aspect. Aquatic life is highly influenced by the levels of dissolved oxygen (DO). This calls for not just constant monitoring of the DO in aquatic systems, but to generate an accurate prediction...

Unsupervised Learning-Based WSN Clustering for Efficient Environmental Pollution Monitoring.

Sensors (Basel, Switzerland)
Wireless Sensor Networks (WSNs) have been adopted in various environmental pollution monitoring applications. As an important environmental field, water quality monitoring is a vital process to ensure the sustainable, important feeding of and as a li...

Spatiotemporal change and prediction of land use in Manasi region based on deep learning.

Environmental science and pollution research international
The Manasi region is located in an arid and semi-arid region with fragile ecology and scarce resources. The land use change prediction is important for the management and optimization of land resources. We utilized Sankey diagram, dynamic degree of l...

Application of deep learning approaches to predict monthly stream flows.

Environmental monitoring and assessment
Accurate and reliable flow estimations are of great importance for hydroelectric power generation, flood and drought risk management, and the effective use of water resources. This research carries out a comprehensive study on the application of gate...

Prediction and sensitivity analysis of chlorophyll a based on a support vector machine regression algorithm.

Environmental monitoring and assessment
Outbreaks of planktonic algae seriously affect the water quality of rivers and are difficult to control. Based on the analysis of the temporal and spatial variation characteristics of environmental factors, this study uses a support vector machine re...