QUAL2Kw – A framework for modeling water quality in streams and rivers using a genetic algorithm for calibration

2006 ◽  
Vol 21 (3) ◽  
pp. 419-425 ◽  
Author(s):  
Gregory J. Pelletier ◽  
Steven C. Chapra ◽  
Hua Tao
Keyword(s):  
Genetic Algorithm ◽  
Water Quality ◽  
Streams And Rivers

scholarly journals Nutrient Limitation and Uptake Rates in Streams and Rivers of the Greater Yellowstone Ecosystem

2013 ◽  
Vol 36 ◽  
pp. 153-159
Author(s):  
Jennifer Tank ◽  
Alexander Reisinger
Keyword(s):  
Water Quality ◽  
Nutrient Limitation ◽  
Water Column ◽  
Nutrient Dynamics ◽  
Nutrient Retention ◽  
Light Limitation ◽  
Small Stream ◽  
Streams And Rivers ◽  
Greater Yellowstone ◽  
Uptake Studies

Nutrient pollution of aquatic ecosystems is a growing concern as the influence of human activities continues to increase on the landscape. Headwater streams have long been shown to process nutrients via the biofilm community growing on the bottom of streams. The growth and activity of these biofilms is often limited by the availability of nitrogen (N), phosphorus (P), or co-limited by both N and P. Although small stream nutrient dynamics are relatively well understood, comparatively little is known about larger, non-wadeable rivers. Biofilms on the river bottom are likely still nutrient limited, but there becomes an increased potential for light limitation as rivers increase in depth. In addition to biofilms on the bottom of rivers, free-living microbial communities suspended in the water column also occur in rivers and process nutrients - a component of nutrient processing largely ignored in streams. In summer 2013 we worked in streams and rivers of the Greater Yellowstone Area (GYA) to establish the nutrient limitation status of minimally-impacted rivers, as well as the role of the water column in processing nutrients as streams increase in size. For both the nutrient limitation and water column uptake studies, we are using the GYA sites in addition to systems from other regions of the US to establish what controls the various aspects of nutrient dynamics in rivers. Our results from the GYA, in addition to Midwest and Southwest US rivers, will provide water quality managers with new strategies for improving water quality downstream, and clarify mechanisms controlling nutrient retention in rivers.

Hybrid Non-Dominated Sorting Genetic Algorithm

2018 ◽  
pp. 264-286 ◽  
Author(s):  
Sankhadeep Chatterjee ◽  
Sarbartha Sarkar ◽  
Nilanjan Dey ◽  
Amira S. Ashour ◽  
Soumya Sen
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Genetic Algorithm ◽  
Water Quality ◽  
Quality Prediction ◽  
Learning Methods ◽  
Nsga Ii ◽  
Water Quality Prediction ◽  
Machine Learning Methods ◽  
Precautionary Measures

Water pollution due to industrial and domestic reasons is highly affecting the water quality. In undeveloped and developed countries, it has become a major reason behind a number of water borne diseases. Poor public health is putting an extra economic liability in order to deploy precautionary measures against these diseases. Recent research works have been directed toward more sustainable solutions to this problem. It has been revealed that good quality of water supply can not only improve the public health, it also accelerates economic growth of a geographical location as well. Water quality prediction using machine learning methods is still at its primitive stage. Besides, most of the studies did not follow any national or international standard for water quality prediction. In the current work, both the problems have been addressed. First, advanced machine learning methods, namely Artificial Neural Networks (ANNs) supported by a well-known multi-objective optimization algorithm called the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) has been used to classify the water samples into two different classes. Secondly, Indian national standard for water quality (IS 10500:2012) has been utilized for this classification task. The hybrid NN-NSGA-II model is compared with another two well-known meta-heuristic supported ANN classifiers, namely ANN trained by Genetic Algorithm (NN-GA) and by Particle Swarm Optimization (NN-PSO). Apart from that, the support vector machine (SVM) has also been included in the comparative study. Besides analysing the performance based on several performance measuring methods, the statistical significance of the results obtained by NN-NSGA-II has been judged by performing Wilcoxon rank sum test with 5% confidence level. Results have indicated the ingenuity of the proposed NN-NSGA-II model over the other classifiers under current study.

scholarly journals Water quality in low‐elevation streams and rivers of New Zealand: Recent state and trends in contrasting land‐cover classes

2004 ◽  
Vol 38 (2) ◽  
pp. 347-366 ◽  
Author(s):  
Scott T. Larned ◽  
Mike R. Scarsbrook ◽  
Ton H. Snelder ◽  
Ned J. Norton ◽  
Barry J. F. Biggs
Keyword(s):  
Water Quality ◽  
New Zealand ◽  
Land Cover ◽  
Streams And Rivers

scholarly journals Calibration of water quality model for distribution networks using genetic algorithm, particle swarm optimization, and hybrid methods

MethodsX ◽  
2019 ◽  
Vol 6 ◽  
pp. 540-548 ◽  
Author(s):  
Roya Peirovi Minaee ◽  
Mojtaba Afsharnia ◽  
Alireza Moghaddam ◽  
Ali Asghar Ebrahimi ◽  
Mohsen Askarishahi ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Water Quality ◽  
Particle Swarm Optimization ◽  
Hybrid Methods ◽  
Particle Swarm ◽  
Distribution Networks ◽  
Water Quality Model ◽  
Quality Model ◽  
Swarm Optimization
Sign in / Sign up

Export Citation Format

Share Document