Artificial Neural Network (ANN) For Evaluating Permeability Decline in Permeable Reactive Barrier (PRB)

Santisukkasaem, Umarat; Olawuyi, Fehintola; Oye, Peter; Das, Diganta Bhusan

doi:10.1007/s40710-015-0076-4

Cited by 26 publications

(11 citation statements)

References 23 publications

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“…The African Great Rift Valley accommodates the world's most severe fluoride belt [29,[119][120][121][122]. Here, fluoride mitigation by means of alum (Nalgonda technique) has been used, with limited success since the 1980s [121].…”

Section: Testing Fe 0 Filters For Fluoride Mitigationmentioning

confidence: 99%

Metallic Iron (Fe0) Provide Possible Solution to Universal Safe Drinking Water Provision

Noubactep¹

2018

J Water Technol Treat Methods

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The use of metallic iron (Fe 0 ) for environmental remediation is applied industrially with a great degree of empiricism. Rules of thumb seem to be the only guide followed by the greater part of the Fe 0 remediation community. The present communication demonstrates the validity of such a harsh statement and hopes that the research community will now follow the 10-year-old path concealing Fe 0 remediation and mainstream science. A promising application is safe drinking water provision on a decentralized manner.

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Section: Testing Fe 0 Filters For Fluoride Mitigationmentioning

confidence: 99%

Metallic Iron (Fe0) Provide Possible Solution to Universal Safe Drinking Water Provision

Noubactep¹

2018

J Water Technol Treat Methods

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“…Typical applications include the following, among many others: predicting the dispersion coefficient (D) in a river ecosystem (Antonopoulos et al 2015); modelling the permeability losses in permeable reactive barriers (Santisukkasaem et al 2015); estimating the reference evapotranspiration (ET 0 ) in India (Adamala et al 2015); calculating the dynamic coefficient in porous media ; predicting Indian monsoon rainfall (Azad et al 2015); modeling of arsenic (III) removal (Mandal et al 2015); predicting effluent biochemical oxygen demand (BOD) in a wastewater treatment plant (Heddam et al 2016); modeling Secchi disk depth (SD) in river (Heddam 2016a); and predicting phycocyanin (PC) pigment concentration in river (Heddam 2016b). Unsurprisingly, regarding the high capabilities of ANNs in developing environmental models, they have rapidly gained much popularity.…”

Section: Multilayer Perceptron Neural Network (Mlpnn)mentioning

confidence: 99%

Use of Optimally Pruned Extreme Learning Machine (OP-ELM) in Forecasting Dissolved Oxygen Concentration (DO) Several Hours in Advance: a Case Study from the Klamath River, Oregon, USA

Heddam

2016

Environ. Process.

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This study presents a new method called optimally pruned extreme learning machine (OP-ELM) for forecasting dissolved oxygen concentration (DO) several hours in advance. The forecast time horizon ranges from 24-h ahead (one day) to 168-h ahead (seven days). The proposed OP-ELM model is compared to the standard multilayer perceptron neural network (MLPNN) with respect to their capabilities of forecasting DO in the Klamath River at Miller Island Boat Ramp, Oregon, USA. To demonstrate the forecasting capability of OP-ELM and MLPNN models, we used a long-term data set of hourly DO data for a ten-year period, from 1 January 2004 to 31 December 2013, collected by the United States Geological Survey (USGS Stations No: 420,853,121,505,500 [Top] and 420,853,121,505,501 [Bottom]). For developing the models, we split the data set into a training subset (from 2004 to 2010) that corresponded to 70 %, and a validation (from 2011 to 2013) that corresponded to 30 % of the total data set. We investigated the performance and accuracy of the proposed two models for three different horizons, i.e., short-term, medium-term and long-term forecasting; a total of six different models (FM1 to FM6), having the same data sets as inputs, were developed for short-term (24 h to 48 h), medium-term (72 h to 96 h) and long-term (120 h to 168 h) horizons. Input variables used in the six models were the six antecedent DO concentrations at (t-5), (t-4), (t-3), (t-2), (t-1) and (t). The performance of the OP-ELM and MLPNN models in training and validation sets were compared with the observed data. To get more accurate evaluation of the results of the two models, the following seven statistical performance indices were used: the coefficient of correlation (R), the Willmott index of agreement (d), the Nash-Sutcliffe efficiency (NSE), the root mean squared error (RMSE), the mean absolute error (MAE), the bias error (Bias), and the mean absolute percentage error (MAPE). The study reveals that OP-ELM and MLPN provided good results and they were successful in forecasting DO at a high level of accuracy. The reliability of forecasting decreased Environ. Process. with increasing the step ahead. The measures of model performance fell within the acceptable ranges for the two stations. Regarding the fact that researches on medium and long-term forecasting are relatively limited, the present work aims to build and provide a good early warning system capable of preventing DO depletion and the associated problems of anoxia and hypoxia in river. Furthermore, the proposed forecasting models, when implemented appropriately, could be reliably used in detecting future change in DO concentration in rivers.

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“…The qualitative evaluation method, including the Delphi method [ 31 ] and expert meeting method [ 32 ], and the quantitative evaluation method including the system engineering method [ 33 ], statistical analysis method [ 34 , 35 ], and the operational research method [ 36 ] etc., are the two main comprehensive evaluation methods [ 37 ]. As the appearance of mutual integration from different knowledge areas, new methods including system modeling and simulation method [ 38 ], information theory method [ 39 ], grey theory method [ 40 , 41 ], intelligent method [ 42 , 43 , 44 ], rough set method [ 45 ], matter element analysis method [ 46 ], and a novel MADM approach [ 47 ] for comprehensive evaluation problems emerged. Different evaluation methods suit for different research objects.…”

Section: Introductionmentioning

confidence: 99%

Multi-Index Evaluation for Flood Disaster from Sustainable Perspective: A Case Study of Xinjiang in China

Dou

Xue

Zhao

et al. 2018

IJERPH

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The floods have undermined the sustainable construction of cities because of their sudden and destruction. To reduce the losses caused by floods, it is necessary to make a reasonable evaluation for historical floods and provide scientific guidance for future precaution. Previous research mainly used subjective/objective weights or barely made static analysis without considering the uncertainty and ambiguity of floods. Therefore, this study proposed a variable fuzzy recognition model, based on combined weights, to evaluate floods, including the determination of index weights and the choice of evaluation model. To make the index weights reflect both subjective experience and objective data, the combined weights were proposed and calculated based on the principle of minimum identification information. Then, the relative membership degree matrix and evaluation results can be worked out by the variable fuzzy recognition model. Conclusions indicated that the combined weights were more convincing than simply subjective or objective weights. Moreover, the variable fuzzy recognition model, by changing model parameters, got stable evaluation results of the sample data. Therefore, the model can improve the credibility of evaluation and the conclusions can provide reasonable suggestions for management departments.

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Artificial Neural Network (ANN) For Evaluating Permeability Decline in Permeable Reactive Barrier (PRB)

Cited by 26 publications

References 23 publications

Metallic Iron (Fe0) Provide Possible Solution to Universal Safe Drinking Water Provision

Metallic Iron (Fe0) Provide Possible Solution to Universal Safe Drinking Water Provision

Use of Optimally Pruned Extreme Learning Machine (OP-ELM) in Forecasting Dissolved Oxygen Concentration (DO) Several Hours in Advance: a Case Study from the Klamath River, Oregon, USA

Multi-Index Evaluation for Flood Disaster from Sustainable Perspective: A Case Study of Xinjiang in China

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