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

Water ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 2790
Author(s):  
Abdul Hannan ◽  
Jagadeesh Anmala

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.

2019 ◽  
Vol 19 (6) ◽  
pp. 1831-1840 ◽  
Author(s):  
Jagadeesh Anmala ◽  
Turuganti Venkateshwarlu

Abstract The measurement and statistical modeling of water quality data are essential to developing a region-based stream-wise database that would be of great use to the EPA's needs. Such a database would also be useful in bio-assessment and in the modeling of processes that are related to riparian vegetation surrounding a water body such as a stream network. With the help of easily measurable data, it would be easier to come up with database-intensive numerical and computer models that explain the stream water quality distribution and biological integrity and predict stream water quality patterns. Statistical assessments of nutrients, stream water metallic and non-metallic pollutants, organic matter, and biological species data are needed to accurately describe the pollutant effects, to quantify health hazards, and in the modeling of water quality and its risk assessment. The study details the results of statistical nonlinear regression and artificial neural network models for Upper Green River watershed, Kentucky, USA. The neural network models predicted the stream water quality parameters with more accuracy than the nonlinear regression models in both training and testing phases. For example, neural network models of pH, conductivity, salinity, total dissolved solids, and dissolved oxygen gave an R2 coefficient close to 1.0 in the testing phase, while the nonlinear regression models resulted in less than 0.6. For other parameters also, neural networks showed better generalization compared with nonlinear regression models.


1997 ◽  
Vol 1 (1) ◽  
pp. 185-196 ◽  
Author(s):  
C. Neal ◽  
T. Hill ◽  
S. Alexander ◽  
B. Reynolds ◽  
S. Hill ◽  
...  

Abstract. The patterns of variation in water quality for an acidic stream draining plantation forest overlying acidic and acid sensitive gley soils with shale and slate bedrock changed following the introduction of a 45 m deep borchole near to the stream. During drilling, air flushing of debris from the borehole cleared fracture routes for groundwater penetration to the stream via the stream bed. Consequently, there were and there remain marked increases in pH, alkalinity and calcium concentrations in the stream water. The extent of this water quality improvement varies according to flow. Under extreme highfiow conditions, most of the stream water is supplied from near surface soil water sources and acidic stream waters (pH about 4.2) result. Under baseflow conditions, the stream water pH is about 7.0 upstream and about 7.5 downstream of the borehole. Under intermediate flow conditions, the improvement in pH is most marked and values increase from around 5 to around 6.3. For acid sensitive 'hard rock' areas such as those studied here, the bedrock has frequently been assumed to be both impermeable and low in base cations. This study illustrates that this view may be incorrect, and that groundwater may provide an important modifier of streamwater quality, at least for slate and shale dominated hard rock areas. Indeed, the work demonstrates clearly the potential for water quality remediation through groundwater manipulation.


2013 ◽  
Vol 295-298 ◽  
pp. 2148-2151
Author(s):  
Fang Liu ◽  
Jian Zhu ◽  
Hai Bo Luo ◽  
Yuan Sheng Liu

In order to evaluate the impacts of N and P mobility from agricultural soils on surface water quality, the dissolved N and P concentrations in the runoff, the drainage and stream waters nearby agricultural lands were investigated at the karst hilly regions in central Guizhou Province. The results shown that the concentrations of NO3−, NH4+ and PO43− in the runoff from upland soils were 9.8~22.1 mg L−1, 0.429~0.818 mg L−1 and 0.025~0.052 mg L−1, respectively, and higher concentrations of NO3− (14.5~25.3 mg L−1) in the drainage waters from paddy soils. In karst areas, the concentrations of NO3− in the stream waters nearby agricultural lands was 14.9~28.5 mg L−1, as indicated by high concentration of NO3− compared with the Grade III of Surface Water Quality Standard of China, suggesting a eutrophication problem for surface water nearby agricultural lands with intensive cultivation.


Data Series ◽  
10.3133/ds37 ◽  
1996 ◽  
Author(s):  
Richard B. Alexander ◽  
J.R. Slack ◽  
A.S. Ludtke ◽  
K.K. Fitzgerald ◽  
T.L. Schertz ◽  
...  

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