scholarly journals Prediction of Water Quality Variation Affected by Tributary Inputs in large Rivers Using ANN Model

10.29007/tvb3 ◽  
2018 ◽  
Author(s):  
Il Won Seo ◽  
Se Hun Yun
Keyword(s):  
Water Quality ◽  
Input Data ◽  
Model Simulation ◽  
Large River ◽  
Quality Data ◽  
Model Parameters ◽  
Ann Model ◽  
Water Quality Data ◽  
Water Quality Variation ◽  
Quality Variation

In this study, an enhanced ANN model was developed to analyze the water quality variation at the river confluence by incorporating the resilient propagation algorithm to increase the model accuracy. An ensemble modeling with stratified sampling method was also developed in order to reduce the influence of the input data and model parameters on the prediction of river water quality. The water quality parameters such as pH, electric conductivity (EC), DO and chlorophyll-a, were predicted using proposed ANN model in the large river which is affected by pollutant inputs from the tributary river. The results of model simulation showed that the pollutant input from the tributary affected the water quality of the mainstream. The model prediction using water quality data of the tributary river as the input data in addition to the mainstream data produced better results than the simulation using mainstream data only, especially for EC and DO, R2 value was improved by 30.9% and 20.6%, respectively.

scholarly journals OPTMIZATION OF BIO-OPTICAL MODEL PARAMETERS FOR TURBID LAKE WATER QUALITY ESTIMATION USING LANDSAT 8 AND WASI-2D

2020 ◽  
Vol XLII-3/W11 ◽  
pp. 67-72
Author(s):  
A. Manuel ◽  
A. C. Blanco ◽  
A. M. Tamondong ◽  
R. Jalbuena ◽  
O. Cabrera ◽  
...  
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
The Philippines ◽  
Quality Data ◽  
Model Parameters ◽  
Landsat 8 ◽  
Water Quality Data ◽  
Chlorophyll A Concentration ◽  
In Situ Data

Abstract. Laguna Lake, the Philippines’ largest freshwater lake, has always been historically, economically, and ecologically significant to the people living near it. However, as it lies at the center of urban development in Metro Manila, it suffers from water quality degradation. Water quality sampling by current field methods is not enough to assess the spatial and temporal variations of water quality in the lake. Regular water quality monitoring is advised, and remote sensing addresses the need for a synchronized and frequent observation and provides an efficient way to obtain bio-optical water quality parameters. Optimization of bio-optical models is done as local parameters change regionally and seasonally, thus requiring calibration. Field spectral measurements and in-situ water quality data taken during simultaneous satellite overpass were used to calibrate the bio-optical modelling tool WASI-2D to get estimates of chlorophyll-a concentration from the corresponding Landsat-8 images. The initial output values for chlorophyll-a concentration, which ranges from 10–40 μg/L, has an RMSE of up to 10 μg/L when compared with in situ data. Further refinements in the initial and constant parameters of the model resulted in an improved chlorophyll-a concentration retrieval from the Landsat-8 images. The outputs provided a chlorophyll-a concentration range from 5–12 μg/L, well within the usual range of measured values in the lake, with an RMSE of 2.28 μg/L compared to in situ data.

Forecasting Model Based on SAGA and LS-SVM for the Water Quality of Poyang Lake

2011 ◽  
Vol 347-353 ◽  
pp. 781-785
Author(s):  
Qun Cao ◽  
Bing Xiang Liu ◽  
Xiang Chen
Keyword(s):  
Water Quality ◽  
Poyang Lake ◽  
Quality Data ◽  
Support Vector ◽  
Forecasting Model ◽  
Model Parameters ◽  
Water Quality Data ◽  
Model Based ◽  
Simulated Annealing Genetic Algorithm

According to the nonlinearity and uncertainty of the water quality data samples, a forecasting model based on Simulated Annealing Genetic Algorithm(SAGA)and least squares support vector machines(LS-SVM) is proposed. Through adaptively optimizing the model parameters of LS-SVM by SAGA, we can apply the model to forecast water quality of Poyang Lake. The experimental results indicate that compared to the typical LS-SVM,the model is very practical and with higher precision.

scholarly journals On the value of water quality data and informative flow states in karst modelling

2017 ◽  
Vol 21 (12) ◽  
pp. 5971-5985 ◽  
Author(s):  
Andreas Hartmann ◽  
Juan Antonio Barberá ◽  
Bartolomé Andreo
Keyword(s):  
Water Quality ◽  
Water Resource ◽  
Quality Data ◽  
Model Parameters ◽  
Flow State ◽  
Saturated Flow ◽  
Water Quality Data ◽  
Model Parameter ◽  
Time Period ◽  
Flow States

Abstract. If properly applied, karst hydrological models are a valuable tool for karst water resource management. If they are able to reproduce the relevant flow and storage processes of a karst system, they can be used for prediction of water resource availability when climate or land use are expected to change. A common challenge to apply karst simulation models is the limited availability of observations to identify their model parameters. In this study, we quantify the value of information when water quality data (NO3− and SO42−) is used in addition to discharge observations to estimate the parameters of a process-based karst simulation model at a test site in southern Spain. We use a three-step procedure to (1) confine an initial sample of 500 000 model parameter sets by discharge and water quality observations, (2) identify alterations of model parameter distributions through the confinement, and (3) quantify the strength of the confinement for the model parameters. We repeat this procedure for flow states, for which the system discharge is controlled by the unsaturated zone, the saturated zone, and the entire time period including times when the spring is influenced by a nearby river. Our results indicate that NO3− provides the most information to identify the model parameters controlling soil and epikarst dynamics during the unsaturated flow state. During the saturated flow state, SO42− and discharge observations provide the best information to identify the model parameters related to groundwater processes. We found reduced parameter identifiability when the entire time period is used as the river influence disturbs parameter estimation. We finally show that most reliable simulations are obtained when a combination of discharge and water quality date is used for the combined unsaturated and saturated flow states.

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 SPATIAL AND TEMPORAL ASSESSMENT OF WATER QUALITY DATA USING MULTIVARIATE STATISTICAL TECHNIQUES

2016 ◽  
Vol 38 (2) ◽  
pp. 577
Author(s):  
Nícolas Reinaldo Finkler ◽  
Taison Anderson Bortolin ◽  
Jardel Cocconi ◽  
Ludmilson Abritta Mendes ◽  
Vania Elisabete Schneider
Keyword(s):  
Water Quality ◽  
Anthropogenic Activities ◽  
Principal Component ◽  
Quality Data ◽  
Spatial And Temporal Variation ◽  
Multivariate Statistical ◽  
Water Quality Data ◽  
And Cluster Analysis ◽  
Quality Variation ◽  
Human Density

The natural factors and anthropogenic activities that contribute to spatial and temporal variation in superficial waters in Caxias do Sul’s urban hydrographic basins were determined applying multivariate analysis of data. The techniques used in this study were Principal Component Analysis and Cluster Analysis. The monitoring was executed in 12 sampling stations, during January, 2009 to January, 2010 with monthly periodicity in total of 13 campaigns. Between chemical, biological and physical, 20 parameters were analyzed. The results state that with the use of ACP, a data variance of 70.94% was observed. Therefore, it testifies that major pollutants that contribute to a water quality variation in the county are classified as domestic and industrial pollutants, mainly from galvanic industry. Moreover, two clusters were found which differentiated regarding their location and distance from areas with a high human density, corroborating on identifying of impact due to human activities in urban rivers.

scholarly journals A Fusion Water Quality Soft-Sensing Method Based on WASP Model and Its Application in Water Eutrophication Evaluation

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Xiaoyi Wang ◽  
Jie Jia ◽  
Tingli Su ◽  
Zhiyao Zhao ◽  
Jiping Xu ◽  
...  
Keyword(s):  
Water Quality ◽  
Unscented Kalman Filter ◽  
Water Environment ◽  
Quality Analysis ◽  
Quality Data ◽  
Model Parameters ◽  
Water Quality Data ◽  
Soft Sensing ◽  
Water Eutrophication ◽  
Quality Indexes

Water environment protection is of great significance for both economic development and improvement of people’s livelihood, where modeling of water environment evolution is indispensable in water quality analysis. However, many water quality indexes related to water quality model cannot be measured online, and some model parameters always vary among different water areas. Thus, this paper proposes a water quality soft-sensing method based on the water quality mechanism model to simulate evolution of water quality indexes online, where unscented Kalman filter is utilized to estimate model parameters. Furthermore, a modified fuzzy comprehensive evaluation method is presented to evaluate the level of water eutrophication condition. Finally, the water quality data collected from Taihu Lake and Beihai Lake are used to validate the effectiveness and generality of the proposed method. The results show that the proposed soft-sensing method is able to describe the variation of related water quality indexes, with better accuracy compared to nonlinear least squares based method and traditional trial-and-error based method. On this basis, the water eutrophication condition can be also accurately evaluated.

A parameter identifiability and estimation study in Yesilirmak River

2009 ◽  
Vol 59 (3) ◽  
pp. 515-521 ◽  
Author(s):  
R. Berber ◽  
M. Yuceer ◽  
E. Karadurmus
Keyword(s):  
Water Quality ◽  
River Water ◽  
Effective Means ◽  
River Water Quality ◽  
Quality Data ◽  
Model Parameters ◽  
Quality Model ◽  
Water Quality Data ◽  
Parameter Identifiability ◽  
Pollutant Concentrations

Water quality models have relatively large number of parameters, which need to be estimated against observed data through a non-trivial task that is associated with substantial difficulties. This work involves a systematic model calibration and validation study for river water quality. The model considered was composed of dynamic mass balances for eleven pollution constituents, stemming from QUAL2E water quality model by considering a river segment as a series of continuous stirred-tank reactors (CSTRs). Parameter identifiability was analyzed from the perspective of sensitivity measure and collinearity index, which indicated that 8 parameters would fall within the identifiability range. The model parameters were then estimated by an integration based optimization algorithm coupled with sequential quadratic programming. Dynamic field data consisting of major pollutant concentrations were collected from sampling stations along Yesilirmak River around the city of Amasya in Turkey, and compared with model predictions. The calibrated model responses were in good agreement with the observed river water quality data, and this indicated that the suggested procedure provided an effective means for reliable estimation of model parameters and dynamic simulation for river streams.

scholarly journals On the value of water quality data and informative flow states in karst modelling

2017 ◽  
Author(s):  
Andreas Hartmann ◽  
Juan Antonio Barberá ◽  
Bartolomé Andreo
Keyword(s):  
Water Quality ◽  
Water Resources ◽  
Quality Data ◽  
Model Parameters ◽  
Flow State ◽  
Saturated Flow ◽  
Water Quality Data ◽  
Model Parameter ◽  
Time Period ◽  
Flow States

Abstract. If properly applied, karst hydrological models are a valuable tool for karst water resources management. If they are able to reproduce the relevant flow and storage processes of a karst system, they can be used for prediction of water resources availability when climate or land use are expected to change. A common challenge to apply karst simulation models is the limited availability of observations to identify their model parameters. In this study, we quantify the value of information when water quality data (NO3− and SO4−2) is used in addition to discharge observations to estimate the parameters of a process-based karst simulation model at a test site in Southern Spain. We use a three-step procedure to (1) confine an initial sample of 500 000 model parameter sets by discharge and water quality observations, (2) identify alterations of model parameter distributions through the confinement, and (3) quantify the strength of the confinement for the model parameters. We repeat this procedure for flow states, at which the system discharge is controlled by the unsaturated zone, the saturated zone, and the entire time period including times when the spring is influenced by a nearby river. Our results indicate that NO3− provides most information to identify the model parameters controlling soil and epikarst dynamics during the unsaturated flow state. During the saturated flow state, SO4−2 and discharge observations provide the best information to identify the model parameters related to groundwater processes. We found reduced parameter identifiability when the entire time period is used as the river influence disturbs parameter estimation. We finally show that most reliable simulations are obtained when a combination of discharge and water quality date is used for the combined unsaturated and saturated flow states.

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