Comparison of the Performance of Decision Tree (DT) Algorithms and ELM model in the Prediction of Water Quality of the Upper Green River watershed

Proposition O was created to help the City of Los Angeles comply with the Total Maximum Daily Load (TMDL) requirements under the Clean Water Act. In this study, the effectiveness of the Proposition O projects in Los Angeles River watershed was examined to show whether it achieves the goal of meeting water quality standards. Our analysis shows the most effective single project will remove at most 2% of pollutant loads from Los Angeles River Watershed and will not achieve TMDL compliance, although several projects can make important contributions to achieve compliance. The ranking results show that the projects that treat the runoff from the largest drainage area have the greatest impact on the water quality of Los Angeles river.

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Study and Analysis of the Water Quality of the Blackstone River Watershed

10.23860/diss-alam-shahriar-1996 ◽

1996 ◽

Author(s):

◽

Shahriar Alam

Keyword(s):

Water Quality ◽

River Watershed ◽

Blackstone River

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Microbiological water quality of the Mfoundi River watershed at Yaoundé, Cameroon, as inferred from indicator bacteria of fecal contamination

Environmental Monitoring and Assessment ◽

10.1007/s10661-005-9172-7 ◽

2006 ◽

Vol 122 (1-3) ◽

pp. 171-183 ◽

Cited By ~ 19

Author(s):

E. Djuikom ◽

T. Njine ◽

M. Nola ◽

V. Sikati ◽

L-B. Jugnia

Keyword(s):

Water Quality ◽

Fecal Contamination ◽

Indicator Bacteria ◽

River Watershed ◽

Microbiological Water Quality

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Impact of Yangtze River Water Transfer on the Water Quality of the Lixia River Watershed, China

PLoS ONE ◽

10.1371/journal.pone.0119720 ◽

2015 ◽

Vol 10 (4) ◽

pp. e0119720 ◽

Cited By ~ 7

Author(s):

Xiaoxue Ma ◽

Lachun Wang ◽

Hao Wu ◽

Na Li ◽

Lei Ma ◽

...

Keyword(s):

Water Quality ◽

River Water ◽

Yangtze River ◽

Water Transfer ◽

River Watershed

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Classification and Prediction of Fecal Coliform in Stream Waters Using Decision Trees (DTs) for Upper Green River Watershed, Kentucky, USA

Water ◽

10.3390/w13192790 ◽

2021 ◽

Vol 13 (19) ◽

pp. 2790

Author(s):

Abdul Hannan ◽

Jagadeesh Anmala

Keyword(s):

Water Quality ◽

Random Forest ◽

Decision Tree ◽

Stream Water ◽

Fecal Coliforms ◽

Stream Water Quality ◽

Green River ◽

Input Variables ◽

Stream Waters

The classification of stream waters using parameters such as fecal coliforms into the classes of body contact and recreation, fishing and boating, domestic utilization, and danger itself is a significant practical problem of water quality prediction worldwide. Various statistical and causal approaches are used routinely to solve the problem from a causal modeling perspective. However, a transparent process in the form of Decision Trees is used to shed more light on the structure of input variables such as climate and land use in predicting the stream water quality in the current paper. The Decision Tree algorithms such as classification and regression tree (CART), iterative dichotomiser (ID3), random forest (RF), and ensemble methods such as bagging and boosting are applied to predict and classify the unknown stream water quality behavior from the input variables. The variants of bagging and boosting have also been looked at for more effective modeling results. Although the Random Forest, Gradient Boosting, and Extremely Randomized Tree models have been found to yield consistent classification results, DTs with Adaptive Boosting and Bagging gave the best testing accuracies out of all the attempted modeling approaches for the classification of Fecal Coliforms in the Upper Green River watershed, Kentucky, USA. Separately, a discussion of the Decision Support System (DSS) that uses Decision Tree Classifier (DTC) is provided.

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APLICACIÓN DE LA LÓGICA DIFUSA PARA DETERMINAR LA CALIDAD DE CUERPOS DE AGUA DE LA CUENCA DEL RÍO RÍMAC

Revista TECNIA ◽

10.21754/tecnia.v27i1.128 ◽

2018 ◽

Vol 27 (1) ◽

pp. 73

Author(s):

A. Aguirre ◽

E. Palomino ◽

G. Salazar

Keyword(s):

Water Quality ◽

Environmental Management ◽

Clustering Analysis ◽

Monitoring Point ◽

River Watershed ◽

Grey Clustering ◽

Toma De Decisiones ◽

Del Rio ◽

Bodies Of Water

El presente trabajo tiene como finalidad determinar la calidad de cinco cuerpos de agua de la Cuenca del Río Rímac los cuales pertenecen a la Categoría 1 A2: " Poblacional y Recreacional" Aguas que pueden ser potabilizadas con tratamiento convencional, mediante el análisis de datos que la Autoridad Nacional del Agua ANA proporcionó en su informe de Técnico de Monitoreo de la Calidad del Agua en la Cuenca del Río Rímac en el año 2013, con la finalidad de brindar una herramienta que cuantifique la calidad de los cuerpos de agua y facilite la toma de decisiones en la gestión ambiental de estos mediante la identificación de aquel que sea más propenso a la pérdida de su calidad como Categoría 1 A2. La aplicación de la metodología de Análisis de Grey Clustering y el Índice de la calidad de agua de Prati, permitieron realizar el análisis de forma objetiva, el criterio para la selección de los cuerpos de agua fue aquellos que cuyos puntos de monitoreo se encontraban más próximos al Río Rímac (aguas abajo) debido a su representatividad ya que ahí convergen todos los posible contaminantes [1] que pudiesen arrastrar desde aguas arriba. Finalmente se determinó que los cinco cuerpos de agua en estudio no se encontraban contaminados, lo cual les da las características de pertenecer a la Categoría 1 A2? ya que estos no representa un riesgo para la salud de las personas y puede ser potabilizada mediante un tratamiento convencional. Palabras clave.- lógica difusa, análisis de Grey Clustering, calidad de agua, gestión ambiental em> ABSTRACT The purpose of the present work is to determine the quality of five bodies of water within the Rimac river watershed, by analyzing the data provided by the Autoridad Nacional del Agua (ANA ‐ the National Water Authority) in its report on Technical Monitoring of Water Quality in the Rimac River Watershed (2013). These five bodies of water have been assigned to Category 1 A2 ("Domestic and Recreational Use") indicating that their water could be made potable through conventional treatment. The results can be used as a tool to evaluate the quality of bodies of water and facilitate their environmental management, by identifying those would be more prone to lose their qualification as Category 1 A2. Use of the Grey Clustering Analysis methodology and the Prati water quality index allowed us to carry out the analysis in an objective manner. The criteria for selecting the bodies of water was the proximity of their monitoring point to the Rimac river (downstream), because of their importance as a point of convergence of all the contaminants [1] that could be brought from upstream. It was found that the five bodies of water in consideration were not contaminated, so that they do belong to Category 1 A2. This means that they do not represent any risk for human health and can be made potable through conventional water treatment. Keywords.- fuzzy logic, Grey Clustering Analysis, water quality, environmental management.

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Surface Water Quality Assessment, Prediction & Modelling of River Daya in Odisha

10.21203/rs.3.rs-1181043/v1 ◽

2022 ◽

Author(s):

Pramod Jena ◽

*Sayed Modinur Rahaman ◽

Pradeep Kumar DasMohapatra ◽

Durga Prasad Barik ◽

Dikshya Surabhi Patra

Keyword(s):

Water Quality ◽

Surface Water ◽

Decision Tree ◽

Surface Water Quality ◽

Water Quality Assessment ◽

Total Coliform ◽

Geographic Region ◽

Total Hardness ◽

Good Tool

Abstract A decision tree -based approach is projected to predict surface water quality and is a good tool to assess quality and guarantee property safe use of water for drinking. The most objective of this study is to assess the surface water quality of the Daya watercourse to work out the quality of drinking functions. Samples were collected from designated locations throughout totally different seasons (winter, summer, rainy) over a amount of five years (2016, 2017, 2018, 2019, and 2020). Total dissolved solids, pH, alkalinity, chloride, nitrate, total hardness, calcium, magnesium, iron, fluoride, were all tested as well as total coliform, fecal coliform, and E. coli. The main goal is to use decision tree regression to construct a model to assess and predict water quality changes in the Daya geographic region of Odisha, India, and compare it to statistical methods.

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