Estimation of urban runoff and water quality using remote sensing and artificial intelligence

2003 ◽  
Vol 47 (7-8) ◽  
pp. 319-325 ◽  
Author(s):  
S.R. Ha ◽  
S.Y. Park ◽  
D.H. Park
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Remote Sensing ◽  
Water Quality ◽  
Urban Runoff ◽  
Quality Data ◽  
Peak Time ◽  
Unit Load ◽  
Runoff Simulation ◽  
Water Quality Data

Water quality and quantity of runoff are strongly dependent on the landuse and landcover (LULC) criteria. In this study, we developed a more improved parameter estimation procedure for the environmental model using remote sensing (RS) and artificial intelligence (AI) techniques. Landsat TM multi-band (7bands) and Korea Multi-Purpose Satellite (KOMPSAT) panchromatic data were selected for input data processing. We employed two kinds of artificial intelligence techniques, RBF-NN (radial-basis-function neural network) and ANN (artificial neural network), to classify LULC of the study area. A bootstrap resampling method, a statistical technique, was employed to generate the confidence intervals and distribution of the unit load. SWMM was used to simulate the urban runoff and water quality and applied to the study watershed. The condition of urban flow and non-point contaminations was simulated with rainfall-runoff and measured water quality data. The estimated total runoff, peak time, and pollutant generation varied considerably according to the classification accuracy and percentile unit load applied. The proposed procedure would efficiently be applied to water quality and runoff simulation in a rapidly changing urban area.

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.

Assessing the potential of remote sensing-derived water quality data to explain variations in fish assemblages and to support fish status assessments in large lakes

Hydrobiologia ◽  
2016 ◽  
Vol 780 (1) ◽  
pp. 71-84 ◽  
Author(s):  
Alfred Sandström ◽  
Petra Philipson ◽  
Anders Asp ◽  
Thomas Axenrot ◽  
Anders Kinnerbäck ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Fish Assemblages ◽  
Quality Data ◽  
Large Lakes ◽  
Water Quality Data

scholarly journals A prediction model of aquaculture water quality based on multiscale decomposition

2021 ◽  
Vol 18 (6) ◽  
pp. 7561-7579
Author(s):  
Huanhai Yang ◽  
◽  
Shue Liu ◽  
◽  
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Prediction Model ◽  
Short Term Memory ◽  
Normal Growth ◽  
Original Data ◽  
Quality Data ◽  
Water Quality Data ◽  
Water Quality Prediction ◽  
Aquaculture Water

<abstract><p>In the field of intensive aquaculture, the deterioration of water quality is one of the main factors restricting the normal growth of aquatic products. Predicting water quality in real time constitutes the theoretical basis for the evaluation, planning and intelligent regulation of the aquaculture environment. Based on the design principles of decomposition, recombination and integration, this paper constructs a multiscale aquaculture water quality prediction model. First, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method is used to decompose the different water quality variables at different time scales step by step to generate a series of intrinsic mode function (IMF) components with the same characteristic scale. Then, the sample entropy of each IMF component is calculated, the components with similar sample entropies are combined, and the original data are recombined into several subsequences through the above operations. In this paper, a prediction model based on a long short-term memory (LSTM) neural network is constructed to predict each recombination subsequence, and the Adam optimization algorithm is used to continuously update the weight of neural network to train and optimize the prediction performance. Finally, the predicted value of each subsequence is superimposed to predict the original water quality data. The dissolved oxygen and pH data of an aquaculture base were collected for prediction experiments, the results of which show that the proposed model has a high prediction accuracy and strong generalization performance.</p></abstract>

scholarly journals Remote sensing-based algorithms for water quality monitoring in Olushandja Dam, north-Central Namibia

2020 ◽  
Author(s):  
Taimi S. Kapalanga ◽  
Zvikomborero Hoko ◽  
Webster Gumindoga ◽  
Loyd Chikwiramakomo
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Linear Relationship ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Monitoring ◽  
Quality Data ◽  
Water Quality Data ◽  
Measured Water

Abstract Frequent and continuous water quality monitoring of Olushandja Dam in Namibia is needed to inform timely decision making. This study was carried out from November 2014 to June 2015 with Landsat 8 reflectance values and field measured water quality data that were used to develop regression analysis-based retrieval algorithms. Water quality parameters considered included turbidity, total suspended solids (TSS), nitrates, ammonia, total nitrogen (TN), total phosphorus (TP) and total algae counts. Results show that turbidity levels exceeded the recommended limits for raw water for potable water treatment while TN and TP values are within acceptable values. Turbidity, TN, and TP and total algae count showed a medium to strong positive linear relationship between Landsat predicted and measured water quality data while TSS showed a weak linear relationship. The regression coefficients between predicted and measured values were: turbidity (R2 = 0.767); TN (R2 = 0.798,); TP (R2 = 0.907); TSS (R2 = 0.284,) and total algae count (R2 = 0.851). Prediction algorithms are generally best fit to derive water quality parameters. Remote sensing is recommended for frequent and continuous monitoring of Olushandja Dam as it has the ability to provide rapid information on the spatio-temporal variability of surface water quality.

Prediction of Chlorophyll and Phosphorus in Lake Ontario by Ensemble of Neural Network Models

2020 ◽  
Author(s):  
Youyue Sun ◽  
Yu Li ◽  
Jinhui Jeanne Huang ◽  
Edward McBean
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Goodness Of Fit ◽  
Lake Ontario ◽  
Control Measures ◽  
Quality Data ◽  
Group Method ◽  
Generalized Regression Neural Network ◽  
Water Quality Control ◽  
Water Quality Data

&lt;p&gt;Chlorophyll-a (CHLA) and total phosphorous (TP) are key indicators for water quality and eutrophication in lakes. It would be a great help to water management if CHLA and TP could be predicted with certain leading time to ensure water quality control measures could be implemented. Since eutrophication is the results of a complex bio-chemical-physical processes involving in pH, temperature, dissolved oxygen (DO) and many other water quality parameters, the discover of their internal correlations and relationships may help in the predication of CHLA and TP. In this study, a long term (20 years) water quality data including CHLA, TP, total nitrogen (TN), turbidity (TB), sulphate, pH, and DO collected in Lake Ontario by the Environment and Climate Change Canada agency were obtained. These data were analyzed by using a group of Neural Network (NN) models and ensemble strategies were evaluated in this study. One particular ensemble of the following NN models, namely, back propagation, Kohonen, probabilistic neural network (PNN), generalized regression neural network (GRNN), or group method of data handling (GMDH) were selected which has higher goodness of fit and shows robustness in model validation. Comparing with one single NN model, the ensemble model could provide more accurate predictions of CHLA and TP concentration in Lake Ontario and the predication of CHLA and TP would be helpful in lake management, eco-restoration and public health risk assessment.&lt;/p&gt;

scholarly journals Prediction of dissolved oxygen in urban rivers at the Three Gorges Reservoir, China: extreme learning machines (ELM) versus artificial neural network (ANN)

2019 ◽  
Vol 55 (1) ◽  
pp. 106-118 ◽  
Author(s):  
Senlin Zhu ◽  
Salim Heddam
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Dissolved Oxygen ◽  
Three Gorges Reservoir ◽  
Model Performance ◽  
Quality Data ◽  
Urban Rivers ◽  
Three Gorges ◽  
Water Quality Data ◽  
The Three Gorges

Abstract In the present study, two non-linear mathematical modelling approaches, namely, extreme learning machine (ELM) and multilayer perceptron neural network (MLPNN) were developed to predict daily dissolved oxygen (DO) concentrations. Water quality data from four urban rivers in the backwater zone of the Three Gorges Reservoir, China were used. The water quality data selected consisted of daily observed water temperature, pH, permanganate index, ammonia nitrogen, electrical conductivity, chemical oxygen demand, total nitrogen, total phosphorus and DO. The accuracy of the ELM model was compared with the standard MLPNN using several error statistics such as root mean squared error, mean absolute error, the coefficient of correlation and the Willmott index of agreement. Results showed that the ELM and MLPNN models perform well for the Wubu River, acceptably for the Yipin River and moderately for the Huaxi River, while poor model performance was obtained at the Tributary of Huaxi River. Model performance is negatively correlated with pollution level in each river. The MLPNN model slightly outperforms the ELM model in DO prediction. Overall, it can be concluded that MLPNN and ELM models can be applied for DO prediction in low-impacted rivers, while they may not be appropriate for DO modelling for highly polluted rivers. This article has been made Open Access thanks to the kind support of CAWQ/ACQE (https://www.cawq.ca).

Updating the U.S. Nationwide Urban runoff quality data base

1999 ◽  
Vol 39 (12) ◽  
pp. 9-16 ◽  
Author(s):  
James T. Smullen ◽  
Amy L. Shallcross ◽  
Kelly A. Cave
Keyword(s):  
Water Quality ◽  
Data Base ◽  
Oxygen Demand ◽  
Urban Runoff ◽  
The United States ◽  
Geographic Region ◽  
Storm Water ◽  
Quality Data ◽  
Water Quality Data ◽  
Urban Stormwater Quality

Urban stormwater quality data collected over the past 20 years for several large government-sponsored sampling programs in the United States were assembled and analyzed to develop new nationwide estimators and statistics for urban storm water quality. We believe that this is the first attempt to assemble and analyze these major storm water quality data sets for this purpose. In this paper, the first public report of our work to-date, we present the results of the data acquisition, data base assembly, quality assurance, computation of new stormwater event mean concentrations and associated statistics, and comparisons with the original U.S. Environmental Protection Agency's Nationwide Urban Runoff Program (NURP) results. The differences between the pooled means and those estimated from our analysis of the NURP data range from a 79% lower estimate for Copper to a 36% higher estimate for Biochemical Oxygen Demand. It is concluded that the variations between the NURP results and those developed here from the pooling of the three national data bases are important and that future work may provide a basis for differentiating Event Mean Concentrations among urban land uses, geographic region and seasons.

scholarly journals CoastColour Round Robin datasets: a database to evaluate the performance of algorithms for the retrieval of water quality parameters in coastal waters

2015 ◽  
Vol 8 (1) ◽  
pp. 173-258 ◽  
Author(s):  
B. Nechad ◽  
K. Ruddick ◽  
T. Schroeder ◽  
K. Oubelkheir ◽  
D. Blondeau-Patissier ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Coastal Waters ◽  
Quality Parameters ◽  
Round Robin ◽  
Water Quality Parameters ◽  
Quality Data ◽  
Ocean Colour ◽  
Water Quality Data

Abstract. The use of in situ measurements is essential in the validation and evaluation of the algorithms that provide coastal water quality data products from ocean colour satellite remote sensing. Over the past decade, various types of ocean colour algorithms have been developed to deal with the optical complexity of coastal waters. Yet there is a lack of a comprehensive inter-comparison due to the availability of quality checked in situ databases. The CoastColour project Round Robin (CCRR) project funded by the European Space Agency (ESA) was designed to bring together a variety of reference datasets and to use these to test algorithms and assess their accuracy for retrieving water quality parameters. This information was then developed to help end-users of remote sensing products to select the most accurate algorithms for their coastal region. To facilitate this, an inter-comparison of the performance of algorithms for the retrieval of in-water properties over coastal waters was carried out. The comparison used three types of datasets on which ocean colour algorithms were tested. The description and comparison of the three datasets are the focus of this paper, and include the Medium Resolution Imaging Spectrometer (MERIS) Level 2 match-ups, in situ reflectance measurements and data generated by a radiative transfer model (HydroLight). These datasets are available from doi.pangaea.de/10.1594/PANGAEA.841950. The datasets mainly consisted of 6484 marine reflectance associated with various geometrical (sensor viewing and solar angles) and sky conditions and water constituents: Total Suspended Matter (TSM) and Chlorophyll a (CHL) concentrations, and the absorption of Coloured Dissolved Organic Matter (CDOM). Inherent optical properties were also provided in the simulated datasets (5000 simulations) and from 3054 match-up locations. The distributions of reflectance at selected MERIS bands and band ratios, CHL and TSM as a function of reflectance, from the three datasets are compared. Match-up and in situ sites where deviations occur are identified. The distribution of the three reflectance datasets are also compared to the simulated and in situ reflectances used previously by the International Ocean Colour Coordinating Group (IOCCG, 2006) for algorithm testing, showing a clear extension of the CCRR data which covers more turbid waters.

scholarly journals Artificial Neural Network Model to Prediction of Eutrophication and Microcystis Aeruginosa Bloom

2020 ◽  
Vol 4 (2) ◽  
pp. 129-135 ◽  
Author(s):  
Pawalee Srisuksomwong ◽  
Jeeraporn Pekkoh
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Chlorophyll A ◽  
Microcystis Aeruginosa ◽  
Quality Data ◽  
Water Quality Data ◽  
Severe Problem ◽  
Temperature Conductivity ◽  
Artificial Neural

Maekuang reservoir is one of the water resources which provides water supply, livestock, and recreational in Chiangmai city, Thailand. The water quality and Microcystis aeruginosa are a severe problem in many reservoirs. M. aeruginosa is the most widespread toxic cyanobacteria in Thailand. Difficulty prediction for planning protects Maekuang reservoirs, the artificial Neural Network (ANN) model is a powerful tool that can be used to machine learning and prediction by observation data. ANN is able to learn from previous data and has been used to predict the value in the future. ANN consists of three layers as input, hidden, and output layer. Water quality data is collected biweekly at Maekuang reservoir (1999-2000). Input data for training, including nutrients (ammonium, nitrate, and phosphorus), Secchi depth, BOD, temperature, conductivity, pH, and output data for testing as Chlorophyll a and M. aeruginosa cells. The model was evaluated using four performances, namely; mean squared error (MSE), root mean square error (RMSE), sum of square error (SSE), and percentage error. It was found that the model prediction agreed with experimental data. C01-C08 scenarios focused on M. aeruginosa bloom prediction, and ANN tested for prediction of Chlorophyll a bloom shown on M01-M09 scenarios. The findings showed, this model has been validated for prediction of Chlorophyll a and shows strong agreement for nitrate, Log cell, and Chlorophyll a. Results indicate that the ANN can be predicted eutrophication indicators during the summer season, and ANN has efficient for providing the new data set and predict the behavior of M. aeruginosa bloom process.

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