AIMC Topic: Environmental Monitoring

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The Node Deployment of Intelligent Sensor Networks Based on the Spatial Difference of Farmland Soil.

Sensors (Basel, Switzerland)
Considering that agricultural production is characterized by vast areas, scattered fields and long crop growth cycles, intelligent wireless sensor networks (WSNs) are suitable for monitoring crop growth information. Cost and coverage are the most key...

Characterizing air quality data from complex network perspective.

Environmental science and pollution research international
Air quality depends mainly on changes in emission of pollutants and their precursors. Understanding its characteristics is the key to predicting and controlling air quality. In this study, complex networks were built to analyze topological characteri...

Development of sediment load estimation models by using artificial neural networking techniques.

Environmental monitoring and assessment
This study aims at the development of an artificial neural network-based model for the estimation of weekly sediment load at a catchment located in northern part of Pakistan. The adopted methodology has been based upon antecedent sediment conditions,...

Prediction of blast-induced air overpressure: a hybrid AI-based predictive model.

Environmental monitoring and assessment
Blast operations in the vicinity of residential areas usually produce significant environmental problems which may cause severe damage to the nearby areas. Blast-induced air overpressure (AOp) is one of the most important environmental impacts of bla...

Suspect screening of large numbers of emerging contaminants in environmental waters using artificial neural networks for chromatographic retention time prediction and high resolution mass spectrometry data analysis.

The Science of the total environment
The recent development of broad-scope high resolution mass spectrometry (HRMS) screening methods has resulted in a much improved capability for new compound identification in environmental samples. However, positive identifications at the ng/L concen...

Forecasting PM10 in Algiers: efficacy of multilayer perceptron networks.

Environmental science and pollution research international
Air quality forecasting system has acquired high importance in atmospheric pollution due to its negative impacts on the environment and human health. The artificial neural network is one of the most common soft computing methods that can be pragmatic...

Applying the Back-Propagation Neural Network model and fuzzy classification to evaluate the trophic status of a reservoir system.

Environmental monitoring and assessment
The trophic state index, and in particular, the Carlson Trophic State Index (CTSI), is critical for evaluating reservoir water quality. Despite its common use in evaluating static water quality, the reliability of the CTSI may decrease when water tur...

Tactile soft-sparse mean fluid-flow imaging with a robotic whisker array.

Bioinspiration & biomimetics
An array of whiskers is critical to many mammals to survive in their environment. However, current engineered systems generally employ vision, radar or sonar to explore the surroundings, not having sufficiently benefited from tactile perception. Insp...

ANN modelling of sediment concentration in the dynamic glacial environment of Gangotri in Himalaya.

Environmental monitoring and assessment
The present study explores for the first time the possibility of modelling sediment concentration with artificial neural networks (ANNs) at Gangotri, the source of Bhagirathi River in the Himalaya. Discharge, rainfall and temperature have been consid...

Development of wavelet-ANN models to predict water quality parameters in Hilo Bay, Pacific Ocean.

Marine pollution bulletin
The main objective of this study is to apply artificial neural network (ANN) and wavelet-neural network (WNN) models for predicting a variety of ocean water quality parameters. In this regard, several water quality parameters in Hilo Bay, Pacific Oce...