AIMC Topic: Rivers

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Improving multiple model ensemble predictions of daily precipitation and temperature through machine learning techniques.

Scientific reports Mar 18, 2022
Multi-Model Ensembles (MMEs) are used for improving the performance of GCM simulations. This study evaluates the performance of MMEs of precipitation, maximum temperature and minimum temperature over a tropical river basin in India developed by vario...
Machine Learning Temperature Rivers Linear Models Support Vector Machine
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Simultaneous feature engineering and interpretation: Forecasting harmful algal blooms using a deep learning approach.

Water research Mar 12, 2022
Routine monitoring for harmful algal blooms (HABs) is generally undertaken at low temporal frequency (e.g., weekly to monthly) that is unsuitable for capturing highly dynamic variations in cyanobacteria abundance. Therefore, we developed a model inco...
Harmful Algal Bloom Rivers Water Quality Deep Learning Cyanobacteria
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Predicting streamflow in Peninsular Malaysia using support vector machine and deep learning algorithms.

Scientific reports Mar 10, 2022
Floods and droughts are environmental phenomena that occur in Peninsular Malaysia due to extreme values of streamflow (SF). Due to this, the study of SF prediction is highly significant for the purpose of municipal and environmental damage mitigation...
Algorithms Malaysia Rivers Support Vector Machine Deep Learning
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Application of artificial intelligence based on synchrosqueezed wavelet transform and improved deep extreme learning machine in water quality prediction.

Environmental science and pollution research international Jan 24, 2022
Water quality prediction is the basis for the prevention and control of water pollution. In this paper, to address the problem of low prediction accuracy of existing empirical models due to the non-smoothness and nonlinearity of water quality series,...
Rivers Wavelet Analysis Artificial Intelligence Algorithms Water Quality
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Development of new computational machine learning models for longitudinal dispersion coefficient determination: case study of natural streams, United States.

Environmental science and pollution research international Jan 21, 2022
Natural streams longitudinal dispersion coefficient (Kx) is an essential indicator for pollutants transport and its determination is very important. Kx is influenced by several parameters, including river hydraulic geometry, sediment properties, and ...
Rivers Machine Learning Water Pollution United States
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Analysis and prediction of water quality using deep learning and auto deep learning techniques.

The Science of the total environment Jan 20, 2022
Natural water sources like ponds, lakes and rivers are facing a great threat because of activities like discharge of untreated industrial effluents, sewage water, wastes, etc. It is mandatory to examine the water quality to ensure that only safe wate...
Artificial Intelligence Water Quality Deep Learning Rivers
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Ensemble streamflow forecasting based on variational mode decomposition and long short term memory.

Scientific reports Jan 11, 2022
Reliable and accurate streamflow forecasting plays a vital role in the optimal management of water resources. To improve the stability and accuracy of streamflow forecasting, a hybrid decomposition-ensemble model named VMD-LSTM-GBRT, which is sensiti...
Models, Theoretical Support Vector Machine China Rivers Neural Networks, Computer Forecasting Algorithms
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TrendProbe: Time profile analysis of emerging contaminants by LC-HRMS non-target screening and deep learning convolutional neural network.

Journal of hazardous materials Jan 4, 2022
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...
Water Pollutants, Chemical Rivers Environmental Monitoring Deep Learning Neural Networks, Computer
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Runoff forecasting model based on variational mode decomposition and artificial neural networks.

Mathematical biosciences and engineering : MBE Dec 13, 2021
Accurate runoff forecasting plays a vital role in water resource management. Therefore, various forecasting models have been proposed in the literature. Among them, the decomposition-based models have proved their superiority in runoff series forecas...
China Data Collection Forecasting Rivers Neural Networks, Computer
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Deep learning models to predict flood events in fast-flowing watersheds.

The Science of the total environment Nov 23, 2021
This study aims to explore the reliability of flood warning forecasts based on deep learning models, in particular Long-Short Term Memory (LSTM) architecture. We also wish to verify the applicability of flood event predictions for a river with flood ...
Rivers Deep Learning Cities Reproducibility of Results Floods
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