scholarly journals Uncertainty Quantification of Granular Computing-neural Network Model for Prediction of Pollutant Longitudinal Dispersion Coefficient in Aquatic Streams

2021 ◽  
Author(s):  
Behzad Ghiasi ◽  
Sun Yuanbin ◽  
Roohollah Noori ◽  
Hossein Sheikhian ◽  
Amin Zeynolabedin ◽  
...  
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Granular Computing ◽  
Bootstrap Method ◽  
Longitudinal Dispersion ◽  
Spatiotemporal Variability ◽  
Ann Model ◽  
Pollution Transport ◽  
Geometric Conditions ◽  
Natural Streams

Abstract Discharge of pollution loads into natural water systems remains a global challenge that threatens water/food supply as well as endangers ecosystem services. Natural rehabilitation of the polluted streams is mainly influenced by the rate of longitudinal dispersion (Dx), a key parameter with large temporal and spatial fluctuates that characterizes pollution transport. The large uncertainty in estimation of Dx in streams limits evaluation of water quality in natural streams and design of water quality enhancement strategies. This study develops a sophisticated model coupled with granular computing and neural network models (GrC-ANN) to provide robust prediction of Dx and its uncertainty for different flow-geometric conditions with high spatiotemporal variability. Uncertainty analysis of Dx GrC-ANN model was based on the alteration of training data fed to tune the model. Modified bootstrap method was employed to generate different training patterns through resampling from a 503 global database of tracer experiments in streams. Comparison between the Dx values estimated by GrC-ANN to those determined from tracer measurements show the appropriateness and robustness of the proposed method in determining the rate of longitudinal dispersion. GrC-ANN model with the narrowest bandwidth of estimated uncertainty (bandwidth-factor =0.56) that brackets the most percentage of true Dx data (i.e., 100%) is the best model to compute Dx in streams. Given considerable inherent uncertainty reported in other Dx models, the Dx GrC-ANN model is suggested as a proper tool for further studies of pollutant mixing in turbulent flow systems such as streams.

scholarly journals Granular computing–neural network model for prediction of longitudinal dispersion coefficients in rivers

2019 ◽  
Vol 80 (10) ◽  
pp. 1880-1892 ◽  
Author(s):  
Behzad Ghiasi ◽  
Hossein Sheikhian ◽  
Amin Zeynolabedin ◽  
Mohammad Hossein Niksokhan
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Granular Computing ◽  
Fuzzy Inference ◽  
Longitudinal Dispersion ◽  
Superior Performance ◽  
Ann Model ◽  
Longitudinal Dispersion Coefficient ◽  
Inference System ◽  
Advection Dispersion

Abstract Successful application of one-dimensional advection–dispersion models in rivers depends on the accuracy of the longitudinal dispersion coefficient (LDC). In this regards, this study aims to introduce an appropriate approach to estimate LDC in natural rivers that is based on a hybrid method of granular computing (GRC) and an artificial neural network (ANN) model (GRC-ANN). Also, adaptive neuro-fuzzy inference system (ANFIS) and ANN models were developed to investigate the accuracy of three credible artificial intelligence (AI) models and the performance of these models in different LDC values. By comparing with empirical models developed in other studies, the results revealed the superior performance of GRC-ANN for LDC estimation. The sensitivity analysis of the three intelligent models developed in this study was done to determine the sensitivity of each model to its input parameters, especially the most important ones. The sensitivity analysis results showed that the W/H parameter (W: channel width; H: flow depth) has the most significant impact on the output of all three models in this research.

Water Quality Forecast Based on BP-Artificial Neural Network Model in Qiantang River

2014 ◽  
Vol 668-669 ◽  
pp. 994-998
Author(s):  
Jin Ting Ding ◽  
Jie He
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Ph Value ◽  
Back Propagation ◽  
Ammonia Nitrogen ◽  
Quality Parameters ◽  
Ann Model ◽  
Qiantang River ◽  
Artificial Neural

This study aims at providing a back propagation-artificial neural network (BP-ANN) model on forecasting the water quality change trend of Qiantang River basin. To achieve this goal, a three-layer (one input layer, one hidden layer, and one output layer) BP-ANN with the LM regularization training algorithm was used. Water quality variables such as pH value, dissolved oxygen, permanganate index and ammonia-nitrogen was selected as the input data to obtain the output of the neural network. The ANN structure with 17 hidden neurons obtained the best selection. The comparison between the original measured and forecast values of the ANN model shows that the relative errors, with a few exceptions, were lower than 9%. The results indicated that the BP neural network can be satisfactorily applied to forecast precise water quality parameters and is suitable for pre-alarm of water quality trend.

scholarly journals Predicting Runoff Chloride Concentrations in Suburban Watersheds Using an Artificial Neural Network (ANN)

Hydrology ◽  
2020 ◽  
Vol 7 (4) ◽  
pp. 80
Author(s):  
Khurshid Jahan ◽  
Soni M. Pradhanang
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Sodium Chloride ◽  
Stormwater Runoff ◽  
Quality Data ◽  
Ann Model ◽  
Water Quality Data ◽  
Artificial Neural Network Ann ◽  
Chloride Concentrations

Road salts in stormwater runoff, from both urban and suburban areas, are of concern to many. Chloride-based deicers [i.e., sodium chloride (NaCl), magnesium chloride (MgCl2), and calcium chloride (CaCl2)], dissolve in runoff, travel downstream in the aqueous phase, percolate into soils, and leach into groundwater. In this study, data obtained from stormwater runoff events were used to predict chloride concentrations and seasonal impacts at different sites within a suburban watershed. Water quality data for 42 rainfall events (2016–2019) greater than 12.7 mm (0.5 inches) were used. An artificial neural network (ANN) model was developed, using measured rainfall volume, turbidity, total suspended solids (TSS), dissolved organic carbon (DOC), sodium, chloride, and total nitrate concentrations. Water quality data were trained using the Levenberg-Marquardt back-propagation algorithm. The model was then applied to six different sites. The new ANN model proved accurate in predicting values. This study illustrates that road salt and deicers are the prime cause of high chloride concentrations in runoff during winter and spring, threatening the aquatic environment.

scholarly journals Prediction of Odor Concentration Emitted from Wastewater Treatment Plant Using an Artificial Neural Network (ANN)

Atmosphere ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 784
Author(s):  
Jeong-Hee Kang ◽  
JiHyeon Song ◽  
Sung Soo Yoo ◽  
Bong-Jae Lee ◽  
Hyon Wook Ji
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Wastewater Treatment ◽  
Prediction Accuracy ◽  
Treatment Plant ◽  
Ann Model ◽  
Odor Concentration ◽  
Odor Emission ◽  
Artificial Neural Network Ann

The odor emitted from a wastewater treatment plant (WWTP) is an important environmental problem. An estimation of odor emission rate is difficult to detect and quantify. To address this, various approaches including the development of emission factors and measurement using a closed chamber have been employed. However, the evaluation of odor emission involves huge manpower, time, and cost. An artificial neural network (ANN) is recognized as an efficient method to find correlations between nonlinear data and prediction of future data based on these correlations. Due to its usefulness, ANN is used to solve complicated problems in various disciplines of sciences and engineering. In this study, a method to predict the odor concentration in a WWTP using ANN was developed. The odor concentration emitted from a WWTP was predicted by the ANN based on water quality data such as biological oxygen demand, dissolved oxygen, and pH. The water quality and odor concentration data from the WWTP were measured seasonally in spring, summer, and autumn and these were used as input variations to the ANN model. The odor predicted by the ANN model was compared with the measured data and the prediction accuracy was estimated. Suggestions for improving prediction accuracy are presented.

scholarly journals The 3D Neural Network for Improving Radar-Rainfall Estimation in Monsoon Climate

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 634
Author(s):  
Nurulhani Roslan ◽  
Mohd Nadzri Md Reba ◽  
Syarawi M. H. Sharoni ◽  
Mohammad Shawkat Hossain
Keyword(s):  
Neural Network ◽  
Nearest Neighbor ◽  
Monsoon Rainfall ◽  
Three Dimensional ◽  
Spatiotemporal Variability ◽  
Ann Model ◽  
Radar Rainfall ◽  
Statistical Indicator ◽  
Artificial Neural Network Ann ◽  
Reflectivity Data

The reflectivity (Z)—rain rate (R) model has not been tested on single polarization radar for estimating monsoon rainfall in Southeast Asia, despite its widespread use for estimating heterogeneous rainfall. The artificial neural network (ANN) regression has been applied to the radar reflectivity data to estimate monsoon rainfall using parametric Z-R models. The 10-min reflectivity data recorded in Kota Bahru radar station (in Malaysia) and hourly rain record in nearby 58 gauge stations during 2013–2015 were used. The three-dimensional nearest neighbor interpolation with altitude correction was applied for pixel matching. The non-linear Levenberg Marquardt (LM) regression, integrated with ANN regression minimized the spatiotemporal variability of the proposed Z-R model. Results showed an improvement in the statistical indicator, when LM and ANN overestimated (6.6%) and underestimated (4.4%), respectively, the mean total rainfall. For all rainfall categories, the ANN model has a positive efficiency ratio of >0.2.

Novel application of a back-propagation artificial neural network model formulated to predict algal bloom

1997 ◽  
Vol 36 (5) ◽  
pp. 89-97 ◽  
Author(s):  
Ken-ichi Yabunaka ◽  
Masaaki Hosomi ◽  
Akihiko Murakami
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Chlorophyll A ◽  
Algal Bloom ◽  
Back Propagation ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Ann Model ◽  
Chlorophyll A Concentration

This paper describes the novel application of an artificial neural network (ANN) model based on the back-propagation method formulated to predict algal bloom by simulating the future growth of five phytoplankton species and the chlorophyll a concentration in the second largest lake in Japan: eutrophic freshwater Lake Kasumigaura. Comparison of observed and calculated values showed that (i) seasonal variations in the biomass of Microcystis spp. were well-predicted with respect to the timing and magnitude of algal bloom, and (ii) the concentration of chlorophyll a, as an indicator of the total biomass of phytoplankton, was well predicted in general. The resultant correlations for the other species, however, showed that model learning was insufficient to effectively predict species biomass; thereby indicating that some unknown factors which are not represented by the set of water quality parameters used as model input data affect phytoplankton growth. A sensitivity analysis performed on input parameters showed that chlorophyll a concentration was mainly affected by PO4-P concentration, while cyanobacteria and diatom species were affected by NO3-N and NH4-N concentrations, respectively. These results indicate that the “algal bloom” ANN model achieved reasonable effectiveness with respect to learning the relationship between the selected water quality parameters and algal bloom.

scholarly journals Application of Artificial Neural Network for Forecasting Nitrate Concentration as a Water Quality Parameter: A Case Study of Feitsui Reservoir, Taiwan

2020 ◽  
Vol 15 (5) ◽  
pp. 647-652
Author(s):  
Sarmad Dashti Latif ◽  
Muhammad Shukri Bin Nor Azmi ◽  
Ali Najah Ahmed ◽  
Chow Ming Fai ◽  
Ahmed El-Shafie
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Quality Parameters ◽  
Quality Parameter ◽  
Water Quality Parameters ◽  
Water Quality Parameter ◽  
Ann Model ◽  
Artificial Neural ◽  
Feitsui Reservoir

Water resources play a vital role in various economies such as agriculture, forestry, cattle farming, hydropower generation, fisheries, industrial activity, and other creative activities, as well as the need for drinking water. Monitoring the water quality parameters in rivers is becoming increasingly relevant as freshwater is increasingly being used. In this study, the artificial neural network (ANN) model was developed and applied to predict nitrate (NO3) as a water quality parameter (WQP) in the Feitsui reservoir, Taiwan. For the input of the model, five water quality parameters were monitored and used namely, ammonium (NH3), nitrogen dioxide (NO2), dissolved oxygen (DO), nitrate (NO3) and phosphate (PO4) as input parameters. As a statistical measurement, the correlation coefficient (R) is used to evaluate the performance of the model. The result shows that ANN is an accurate model for predicting nitrate as a water quality parameter in the Feitsui reservoir. The regression value for the training, testing, validation, and overall are 0.92, 0.93, 0.99, and 0.94, respectively.

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