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Rivers

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Deep learning based regression for optically inactive inland water quality parameter estimation using airborne hyperspectral imagery.

Environmental pollution (Barking, Essex : 1987)
Airborne hyperspectral remote sensing has the characteristics of high spatial and spectral resolutions, and provides an opportunity for accurate and efficient inland water qauality monitoring. Many studies have focused on evaluating and quantifying t...

Application of ANN and SVM for prediction nutrients in rivers.

Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering
This paper presents the results of predicting nutrients in rivers on national level by the use of two artificial intelligence methodologies. Artificial neural network (ANN) and support vector machine (SVM) were used to predict annual concentration of...

A water quality prediction model based on variational mode decomposition and the least squares support vector machine optimized by the sparrow search algorithm (VMD-SSA-LSSVM) of the Yangtze River, China.

Environmental monitoring and assessment
Accurate and reliable water quality forecasting is of great significance for water resource optimization and management. This study focuses on the prediction of water quality parameters such as the dissolved oxygen (DO) in a river system. The accurac...

Water quality assessment of a river using deep learning Bi-LSTM methodology: forecasting and validation.

Environmental science and pollution research international
Water is a prime necessity for the survival and sustenance of all living beings. Over the past few years, the water quality of rivers is adversely affected due to harmful wastes and pollutants. This ever-increasing water pollution is a big matter of ...

An interpretable machine learning method for supporting ecosystem management: Application to species distribution models of freshwater macroinvertebrates.

Journal of environmental management
Species distribution models (SDMs), in which species occurrences are related to a suite of environmental variables, have been used as a decision-making tool in ecosystem management. Complex machine learning (ML) algorithms that lack interpretability ...

Using a deep convolutional network to predict the longitudinal dispersion coefficient.

Journal of contaminant hydrology
Given the interest in accurately predicting the Longitudinal Dispersion Coefficient (D) within the fields of hydraulic and water quality modeling, a wide range of methods have been used to estimate this parameter. In order to improve the accuracy of ...

From Hydrometeorology to River Water Quality: Can a Deep Learning Model Predict Dissolved Oxygen at the Continental Scale?

Environmental science & technology
Dissolved oxygen (DO) reflects river metabolic pulses and is an essential water quality measure. Our capabilities of forecasting DO however remain elusive. Water quality data, specifically DO data here, often have large gaps and sparse areal and temp...

Identification Framework of Contaminant Spill in Rivers Using Machine Learning with Breakthrough Curve Analysis.

International journal of environmental research and public health
To minimize the damage from contaminant accidents in rivers, early identification of the contaminant source is crucial. Thus, in this study, a framework combining Machine Learning (ML) and the Transient Storage zone Model (TSM) was developed to predi...

An efficient method for building a database of diatom populations for drowning site inference using a deep learning algorithm.

International journal of legal medicine
Seasonal or monthly databases of the diatom populations in specific bodies of water are needed to infer the drowning site of a drowned body. However, existing diatom testing methods are laborious, time-consuming, and costly and usually require specif...

Iterative classifier optimizer-based pace regression and random forest hybrid models for suspended sediment load prediction.

Environmental science and pollution research international
Suspended sediment load is a substantial portion of the total sediment load in rivers and plays a vital role in determination of the service life of the downstream dam. To this end, estimation models are needed to compute suspended sediment load in r...