Measuring treatment effectiveness of urban wetland using hybrid water quality — Artificial neural network (ANN) model

Singh, Gurmeet; Kandasamy, Jaya; Shon, Ho Kyong; Cho, J.

doi:10.5004/dwt.2011.2712

Cited by 11 publications

(3 citation statements)

References 3 publications

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“…Machine learning models have shown encouraging performances in a range of water resources applications, such as rainfallrunoff modelling (Minns and Hall, 1996;Khu et al, 2001;Babovic and Keijzer, 2002;Chiang et al, 2004), streamflow forecasting (Nourani et al, 2009;Meshgi et al, 2014Meshgi et al, , 2015Humphrey et al, 2016;Karimi et al, 2016), estimation of missing data (Elshorbagy et al, 2002), error correction (Sun et al, 2012), water quality modelling (Savic and Khu, 2005;Singh et al, 2011;García-Alba et al, 2019), sediment transport modelling (Babovic and Abbott, 1997;Afan et al, 2014;Safari and Mehr, 2018), reservoir management (Giuliani et al, 2015), prediction of climate variables (Dahamsheh and Aksoy, 2013;Ferreira et al, 2019), because of their potential to apprehend the noise complexity, non-linearity, non-stationarity and dynamism of data (Yaseen et al, 2015). Certainly, if we are only interested in better forecasting results then, the machine learning models might be the preferred choice over the conceptual or process-based models due to their better predictive capability.…”

Section: Machine Learning In Water Resourcesmentioning

confidence: 99%

“…CC BY 4.0 License. streamflow estimation (Nourani et al, 2009;Humphrey et al, 2016), water quality modelling (Singh et al, 2011;García-Alba et al, 2019), groundwater modelling (Nayak et al, 2006;Gholami et al, 2015), data assimilation Vojinovic et al, 2003), estimation of climate variables (Dahamsheh and Aksoy, 2013;Ferreira et al, 2019), flood and drought forecasting (Chang et al, 2014;Dehghani et al, 2014) and sediment transport modelling (Afan et al, 2014). Most of the above applications use supervised learning ANN models, such as Feed Forward Back Propagation (FFBP), Radial Basis Function Neural Network (RBFNN), and Generalized Regression Neural Network (GRNN).…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

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Hydrologically Informed Machine Learning for Rainfall-Runoff Modelling: Towards Distributed Modelling

Herath¹,

Chadalawada²,

Babovic³

2020

Preprint

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Abstract. Despite showing a great success of applications in many commercial fields, machine learning and data science models in general, show a limited use in scientific fields including hydrology. The approach is often criticized for lack of interpretability and physical consistency. This has led to the emergence of new paradigms, such as Theory Guided Data Science (TGDS) and physics informed machine learning. The motivation behind such approaches is to improve the physical meaningfulness of machine learning models by blending existing scientific knowledge with learning algorithms. Following the same principles, in our prior work (Chadalawada et al., 2020), a new model induction framework was founded on Genetic Programming (GP) namely Machine Learning Rainfall-Runoff Model Induction Toolkit (ML-RR-MI). ML-RR-MI is cable of developing fully-fledged lumped conceptual rainfall-runoff models for a watershed of interest using the building blocks of two flexible rainfall-runoff modelling frameworks (FUSE and SUPERFLEX). In this study, we extend ML-RR-MI towards inducing semi-distributed rainfall-runoff models. This effort is motivated by the desire to address the decreasing meaningfulness of lumped models which tend to particularly deteriorate within large catchments where the spatial heterogeneity of forcing variables and watershed properties are significant. Henceforth, our machine learning approach for rainfall-runoff modelling titled Machine Induction Knowledge-Augmented System Hydrologique Asiatique (MIKA-SHA) captures spatial variabilities and automatically induces rainfall-runoff models for the catchment of interest without any subjectivity in model selection. Currently, MIKA-SHA learns models utilizing the model building components of FUSE and SUPERFLEX. However, the proposed framework can be coupled with any internally coherent collection of building blocks. MIKA-SHA’s model induction capabilities have been tested on the Red Creek catchment near Vestry, Mississippi, United States. The resulted model architectures through MIKA-SHA are compatible with previously reported research findings and fieldwork insights of the watershed and are readily interpretable by hydrologists.

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Section: Machine Learning In Water Resourcesmentioning

confidence: 99%

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Hydrologically Informed Machine Learning for Rainfall-Runoff Modelling: Towards Distributed Modelling

Herath¹,

Chadalawada²,

Babovic³

2020

Preprint

View full text Add to dashboard Cite

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“…ANNs are digitized models of human brain computer programs designed to simulate the way in which human brain processes information [36]. Generally, development of an ANN model consists of the following steps: data collection, analysis and pre-processing of the data, creation, and configuration of the network, training, and validation of the network and finally simulations and predictions with the validated network [37]. The structure of ANN is comprised of an input layer, an output layer and one or more hidden layers as represented in Fig.…”

Section: Annmentioning

confidence: 99%

Soft computing techniques in prediction Cr(VI) removal efficiency of polymer inclusion membranes

Yaqub

Eren

Eyüpoğlu

2019

Environmental Engineering Research

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In this study soft computing techniques including, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were investigated for the prediction of Cr(VI) transport efficiency by novel Polymer Inclusion Membranes (PIMs). Transport experiments carried out by varying parameters such as time, film thickness, carrier type, carier rate, plasticizer type, and plasticizer rate. The predictive performance of ANN and ANFIS model was evaluated by using statistical performance criteria such as Root Mean Standard Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R 2). Moreover, Sensitivity Analysis (SA) was carried out to investigate the effect of each input on PIMs Cr(VI) removal efficiency. The proposed ANN model presented reliable and valid results, followed by ANFIS model results. RMSE and MAE values were 0.00556, 0.00163 for ANN and 0.00924, 0.00493 for ANFIS model in the prediction of Cr(VI) removal efficiency on testing data sets. The R 2 values were 0.973 and 0.867 on testing data sets by ANN and ANFIS, respectively. Results show that the ANN-based prediction model performed better than ANFIS. SA demonstrated that time; film thickness; carrier type and plasticizer type are major operating parameters having 33.61%, 26.85%, 21.07% and 8.917% contribution, respectively.

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Environmental monitoring and assessment of the water bodies of a pre-construction urban wetland

Zuo

Wan

Zhou

et al. 2014

Environ Monit Assess

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It is planned that the Dayanghan Wetland in China will be transformed into a national park but little is known about its current water quality and pollution status. Thus, we monitored the physical and chemical characteristics of the Dayanghan Wetland, which showed that the water quality was generally good. However, the chemical oxygen demand was more than double the reference value, which may be attributable to previous tillage for vegetable crops and other farmlands. In addition, nickel and chromium caused low-level pollution in the water bodies of the Dayanghan Wetland. The mean trophic level index and nutrient quality index were 39.1 and 2.69, respectively. Both indices suggest that the water bodies of the Dayanghan Wetland are in a mesotrophic state and that no eutrophication has occurred. The study would provide a precise report on the status of environmental quality of the water bodies of a typical pre-construction wetland for the administration and decision of the local government and the planning agent.

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Measuring treatment effectiveness of urban wetland using hybrid water quality — Artificial neural network (ANN) model

Cited by 11 publications

References 3 publications

Hydrologically Informed Machine Learning for Rainfall-Runoff Modelling: Towards Distributed Modelling

Hydrologically Informed Machine Learning for Rainfall-Runoff Modelling: Towards Distributed Modelling

Soft computing techniques in prediction Cr(VI) removal efficiency of polymer inclusion membranes

Environmental monitoring and assessment of the water bodies of a pre-construction urban wetland

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