AIMC Topic: Environmental Monitoring

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Modelling the reference crop evapotranspiration in the Beas-Sutlej basin (India): an artificial neural network approach based on different combinations of meteorological data.

Environmental monitoring and assessment
Accurate prediction of the reference evapotranspiration (ET) is vital for estimating the crop water requirements precisely. In this study, we developed multi-layer perceptron artificial neural network (MLP-ANN) models considering different combinatio...

Pixel-level image classification for detecting beach litter using a deep learning approach.

Marine pollution bulletin
Mitigating and preventing beach litter from entering the ocean is urgently required. Monitoring beach litter solely through human effort is cumbersome, with respect to both time and cost. To address this problem, an artificial intelligence technique ...

A deep learning method for cyanobacterial harmful algae blooms prediction in Taihu Lake, China.

Harmful algae
Cyanobacterial Harmful Algae Blooms (CyanoHABs) in the eutrophic lakes have become a global environmental and ecological problem. In this study, a CNN-LSTM integrated model for predicting the CyanoHABs area was proposed and applied to the prediction ...

High Accuracy Real-Time Multi-Gas Identification by a Batch-Uniform Gas Sensor Array and Deep Learning Algorithm.

ACS sensors
Semiconductor metal oxide (SMO) gas sensors are attracting great attention as next-generation environmental monitoring sensors. However, there are limitations to the actual application of SMO gas sensors due to their low selectivity. Although the ele...

Temporal Prediction of Paralytic Shellfish Toxins in the Mussel Using a LSTM Neural Network Model from Environmental Data.

Toxins
Paralytic shellfish toxins (PSTs) are produced mainly by (formerly ). Since 2000, the National Institute of Fisheries Science (NIFS) has been providing information on PST outbreaks in Korean coastal waters at one- or two-week intervals. However, a d...

MARIDA: A benchmark for Marine Debris detection from Sentinel-2 remote sensing data.

PloS one
Currently, a significant amount of research is focused on detecting Marine Debris and assessing its spectral behaviour via remote sensing, ultimately aiming at new operational monitoring solutions. Here, we introduce a Marine Debris Archive (MARIDA),...

Deciphering urban traffic impacts on air quality by deep learning and emission inventory.

Journal of environmental sciences (China)
Air pollution is a major obstacle to future sustainability, and traffic pollution has become a large drag on the sustainable developments of future metropolises. Here, combined with the large volume of real-time monitoring data, we propose a deep lea...

TrendProbe: Time profile analysis of emerging contaminants by LC-HRMS non-target screening and deep learning convolutional neural network.

Journal of hazardous materials
Peak prioritization is one of the key steps in non-target screening of environmental samples to direct the identification efforts to relevant and important features. Occurrence of chemicals is sometimes a function of time and their presence in consec...

Applicability of recurrent neural networks to retrieve missing runoff records: challenges and opportunities in Turkey.

Environmental monitoring and assessment
Acquiring river flow records is the primary precondition for providing optimal water resource management practices and preserving the ecohydrological balance. In Turkey, some river gauging stations go intermittently out of service due to some technic...

A study of uncertainties in groundwater vulnerability modelling using Bayesian model averaging (BMA).

Journal of environmental management
Bayesian Model Averaging (BMA) is used to study inherent uncertainties using the Basic DRASTIC Framework (BDF) for assessing the groundwater vulnerability in a study area related to Lake Urmia. BMA is naturally an Inclusive Multiple Modelling (IMM) s...