GIS and Artificial Neural Network–Based Water Quality Model for a Stream Network in the Upper Green River Basin, Kentucky, USA

Anmala, Jagadeesh; Meier, Ouida W.; Meier, Albert J.; Grubbs, Scott A.

doi:10.1061/(asce)ee.1943-7870.0000801

Cited by 24 publications

(21 citation statements)

References 22 publications

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“…These results agree with the findings of the present study ( Table 3 ). Several other studies are consistent with the present study and conclude, based on similar findings, that the ANN model can easily classify and predict water quality with the justifiable output [ 19 , 20 , 50 , 55 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 ].…”

Section: Discussionsupporting

confidence: 92%

The Use of Artificial Neural Networks to Predict the Physicochemical Characteristics of Water Quality in Three District Municipalities, Eastern Cape Province, South Africa

Setshedi

Mutingwende

Ngqwala

2021

IJERPH

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Reliable prediction of water quality changes is a prerequisite for early water pollution control and is vital in environmental monitoring, ecosystem sustainability, and human health. This study uses Artificial Neural Network (ANN) technique to develop the best model fits to predict water quality parameters by employing multilayer perceptron (MLP) neural network and the radial basis function (RBF) neural network, using data collected from three district municipalities. Two input combination models, MLP-4-5-4 and MLP-4-9-4, were trained, verified, and tested for their predictive performance ability, and their physicochemical prediction accuracy was compared by using each model’s observed data with the predicted data. The MLP-4-5-4 model showed a better understanding of the data sets and water quality predictive ability giving an MSE of 39.06589 and a correlation coefficient (R2) of the observed and the predicted water quality of 0.989383 compared to the MLP-4-9-4 model (R2 = 0.993532, MSE = 39.03087). These results apply to natural water resources management in South Africa and similar catchment systems. The MLP-4-5-4 system can be scaled up for future water quality prediction of the Waste Water Treatment Plants (WWTPs), groundwater, and surface water while raising awareness among the public and industry on future water quality.

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Section: Discussionsupporting

confidence: 92%

The Use of Artificial Neural Networks to Predict the Physicochemical Characteristics of Water Quality in Three District Municipalities, Eastern Cape Province, South Africa

Setshedi

Mutingwende

Ngqwala

2021

IJERPH

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“…The various parts of the overland planes of the watershed contribute as surface water flows, which enters tributaries first and then the main stem of Green River before reaching the watershed outlet tip. Based on watershed characteristics and time of concentration studies, the two-day cumulative and interpolated precipitation values are most suitable drivers of fecal coliform concentrations than other precipitation measures at all the sampling sites [29]. The positive correlation of microbial indicators such as fecal coliform bacteria and precipitation/rainfall/wet weather conditions is in agreement with the other studies such as that of [40][41][42][43][44] for rivers and bays and [45,46] for lakes.…”

Section: Overviewsupporting

confidence: 81%

“…The land use factors developed using GIS analysis [29] essentially indicate the percentage contribution of each land use to the total catchment area or watershed area. The three dominant land use factors are ULUF (urban land use factor), FLUF (forest land use factor), and ALUF (agricultural land use factor).…”

Section: Gis Landuse Analysismentioning

confidence: 99%

“…The output parameter predictions can also be further generalized or recasted using the regression formulae obtained of DTR and the output parameter bounds. The current Decision Support System is an improvised version of the DSS discussed in [29]. The Artificial Neural Network (ANN) model is replaced by DTC to suit the current problem of classification stream waters into four classes.…”

Section: Decision Tree Classifier Based Decision Support System (Dtcdss)mentioning

confidence: 99%

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

Hannan

Anmala

2021

Water

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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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“…To date, a number of ANN models have been developed and introduced allowing solving large complex problems especially in environmental concerns [ Eissa et al ., ; Anmala et al ., ; Ashtiani et al ., ; Coad et al ., ; Benzer and Benzer , ; Wang et al ., ]. The differences between various ANN classes may reside, for instance, in the model topology, the training algorithm and the transfer function used.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Fully nonlinear statistical and machine‐learning approaches for hydrological frequency estimation at ungauged sites

Ouali

Chebana

Ouarda³

2017

J Adv Model Earth Syst

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The high complexity of hydrological systems has long been recognized. Despite the increasing number of statistical techniques that aim to estimate hydrological quantiles at ungauged sites, few approaches were designed to account for the possible nonlinear connections between hydrological variables and catchments characteristics. Recently, a number of nonlinear machine‐learning tools have received attention in regional frequency analysis (RFA) applications especially for estimation purposes. In this paper, the aim is to study nonlinearity‐related aspects in the RFA of hydrological variables using statistical and machine‐learning approaches. To this end, a variety of combinations of linear and nonlinear approaches are considered in the main RFA steps (delineation and estimation). Artificial neural networks (ANNs) and generalized additive models (GAMs) are combined to a nonlinear ANN‐based canonical correlation analysis (NLCCA) procedure to ensure an appropriate nonlinear modeling of the complex processes involved. A comparison is carried out between classical linear combinations (CCAs combined with linear regression (LR) model), semilinear combinations (e.g., NLCCA with LR) and fully nonlinear combinations (e.g., NLCCA with GAM). The considered models are applied to three different data sets located in North America. Results indicate that fully nonlinear models (in both RFA steps) are the most appropriate since they provide best performances and a more realistic description of the physical processes involved, even though they are relatively more complex than linear ones. On the other hand, semilinear models which consider nonlinearity either in the delineation or estimation steps showed little improvement over linear models. The linear approaches provided the lowest performances.

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GIS and Artificial Neural Network–Based Water Quality Model for a Stream Network in the Upper Green River Basin, Kentucky, USA

Cited by 24 publications

References 22 publications

The Use of Artificial Neural Networks to Predict the Physicochemical Characteristics of Water Quality in Three District Municipalities, Eastern Cape Province, South Africa

The Use of Artificial Neural Networks to Predict the Physicochemical Characteristics of Water Quality in Three District Municipalities, Eastern Cape Province, South Africa

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

Fully nonlinear statistical and machine‐learning approaches for hydrological frequency estimation at ungauged sites

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