scholarly journals Evaluating a microbial water quality prediction model for beach management under the revised EU Bathing Water Directive

2016 ◽  
Vol 167 ◽  
pp. 49-58 ◽  
Author(s):  
Zeinab Bedri ◽  
Aisling Corkery ◽  
John J. O'Sullivan ◽  
Louise A. Deering ◽  
Katalin Demeter ◽  
...  
Keyword(s):  
Water Quality ◽  
Prediction Model ◽  
Quality Prediction ◽  
Beach Management ◽  
Microbial Water Quality ◽  
Water Quality Prediction ◽  
Bathing Water Directive ◽  
Bathing Water

Water quality prediction model in open limestone systems

2020 ◽  
pp. 279-282
Author(s):  
Louis McDonald ◽  
Qingyun Sun ◽  
Jeffrey Skousen ◽  
Paul Ziemkiewicz
Keyword(s):  
Water Quality ◽  
Prediction Model ◽  
Quality Prediction ◽  
Water Quality Prediction

An assessment of the impact of the proposed EU bathing water directive on Irish coastal bathing area compliance

2005 ◽  
Vol 51 (3-4) ◽  
pp. 225-230 ◽  
Author(s):  
R. Chawla ◽  
K. Real ◽  
B. Masterson
Keyword(s):  
Water Quality ◽  
Quality Standards ◽  
Water Quality Standards ◽  
Microsoft Excel ◽  
Microbial Water Quality ◽  
Bathing Water Directive ◽  
Bathing Water ◽  
Good Classification ◽  
The Impact

An assessment of the impact of the new microbial water quality standards of the proposed EU Bathing Water Directive on the classification of designated Irish coastal bathing areas is presented. The new standards are applied retrospectively to the microbial water quality results for the bathing seasons of 1999, 2000 and 2001, and the outcome is compared with that recorded under the present Bathing Water Directive. A Microsoft EXCEL application was developed to generate the retrospective bathing area classifications according to the proposed Directive (Excellent, Good, Poor). It was found that the number of Irish coastal bathing areas not attaining ‘Excellent’ classification (as would be required at present for the Blue Flag award) was trebled; the number attaining ‘Good’ classification was increased by about 50%, and the number attracting ‘Poor’ classification (equivalent to ‘Fail’ under the present Directive) was increased nine-fold. Some of the shortcomings of the proposed Directive and suggestions for its revision are discussed.

A Study on Water Quality Prediction Model over Midterm Considering Correlation between Water Quality and Hyrologic Factors

2015 ◽  
Vol 15 (6) ◽  
pp. 459-470
Author(s):  
Mieun Kim ◽  
Jaemoon Kim ◽  
Sungjae Ye ◽  
Jaebeom Park ◽  
Miyeon Yun ◽  
...  
Keyword(s):  
Water Quality ◽  
Prediction Model ◽  
Quality Prediction ◽  
Water Quality Prediction

Water Quality Prediction Model Based on GRU hybrid network

2019 ◽  
Author(s):  
Jichang TU ◽  
Xueqin YANG ◽  
Chaobo CHEN ◽  
Song GAO ◽  
Jingcheng WANG ◽  
...  
Keyword(s):  
Water Quality ◽  
Prediction Model ◽  
Hybrid Network ◽  
Quality Prediction ◽  
Water Quality Prediction ◽  
Model Based

Water Quality Autoregression Prediction Model Based on Markov-Switching Theory Using Crow Search Algorithm

2020 ◽  
Author(s):  
Chunlei Liu ◽  
Chengzhong Pan ◽  
Yawen Chang ◽  
Mingjie Luo
Keyword(s):  
Water Pollution ◽  
Water Quality ◽  
Time Series ◽  
Prediction Model ◽  
Search Algorithm ◽  
Markov Switching ◽  
Information Criterion ◽  
Quality Prediction ◽  
Water Quality Prediction ◽  
Fifth Order

<p>Water quality prediction is an important technical means for preventing and controlling water pollution and is crucial in the formulation of reasonable water pollution prevention and control measures. The time series structure of natural water quality is complex and heteroscedastic, so it is difficult for the traditional prediction model to reflect the actual situation well. Hence, Markov-switching (MS) theory is applied to a water quality autoregression (AR) prediction model (MSAR) in this paper. Further, MSAR is improved by introducing the crow search algorithm to obtain model parameters (CSA-MSAR). Then existing water quality time series for COD<sub>Mn</sub> was selected as the data for the CSA-MSAR model after a normality test and the Box–Cox normality transformation. The results show that the CSA-MSAR model for COD<sub>Mn</sub> with (s, p) values of (3, 5) has the best performance. The improvement degree for selection criteria compared with AR model is as follows: Akaike information criterion for MSAR is 32.020% and 31.611% for CSA-MSAR; Bayesian information criterion for MSAR is 10.632% and 13.464% for CSA-MSAR; likelihood value for MSAR is 40.016% and 40.801% for CSA-MSAR; C for MSAR is 63.559% and 64.968% for CSA-MSAR. Moreover, the results show that the average prediction precision of the first- to fifth-order prediction is raised by 89.016% for MSAR and 89.340% for CSA-MSAR compared with AR, indicating that the introduction of MS makes the CSA-MSAR and MSAR models conform to the smoothness of the mean and variance in each state. The results also indicate that the introduction of CSA into the maximum likelihood estimation to obtain the parameters raise the model prediction precision (the average prediction precision of CSA-MSAR is higher than MSAR by 5.231% excluding the fifth-order prediction) and the CSA-MSAR model is scientifically valid and reasonable for water quality prediction.</p>

scholarly journals Prediction of Surface Water Quality by Artificial Neural Network Model Using Probabilistic Weather Forecasting

Water ◽  
2021 ◽  
Vol 13 (17) ◽  
pp. 2392
Author(s):  
Woo Suk Jung ◽  
Sung Eun Kim ◽  
Young Do Kim
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Weather Forecasting ◽  
Oxygen Demand ◽  
Meteorological Station ◽  
Quality Prediction ◽  
Water Quality Prediction ◽  
Artificial Neural

We developed an artificial neural network (ANN)-based water quality prediction model and evaluated the applicability of the model using regional probability forecasts provided by the Korea Meteorological Administration as the input data of the model. The ANN-based water quality prediction model was constructed by reflecting the actual meteorological observation data and the water quality factors classified using an exploratory factor analysis (EFA) for each unit watershed in Nam River. To apply spatial refinement of meteorological factors for each unit watershed, we used the data of the Sancheong meteorological station for Namgang A and B, and the data of the Jinju meteorological station for Namgang C, D, and E. The predicted water quality variables were dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), total organic carbon (TOC), total phosphorus (T-P), and suspended solids (SS). The ANN evaluation results reveal that the Namgang E unit watershed has a higher model accuracy than the other unit watersheds. Furthermore, compared with Namgang C and D, Namgang E has a high correlation with water quality due to meteorological effects. The results of this study will help establish a water quality forecasting system based on probabilistic weather forecasting in the long term.

Multi-task learning framework for predicting water quality using non-linear machine learning technique

2021 ◽  
pp. 1-13
Author(s):  
D. Senthilkumar ◽  
D. George Washington ◽  
A.K. Reshmy ◽  
M. Noornisha
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Prediction Model ◽  
Quality Prediction ◽  
Machine Learning Technique ◽  
Water Quality Prediction ◽  
Task Learning ◽  
Learning Technique ◽  
Non Linear ◽  
Linear Machine

Predicting the quality of water is a very important issue in an ecosystem and it can be used to control the increase of water contamination. Also, water quality prediction is a prominent complex non-linear multi-target learning problem and extracting a relevant subset of features from a large number of features with multiple targets is a challenging task. Existing water quality prediction model not focused on multi-target learning process simultaneously and not identifying the non-linear relationship between the features and target variables. Therefore, this study proposes a multi-task learning method dealing with multi-target regression using non-linear machine learning technique. Finally, experiments are conducted to build a prediction model based on the proposed methods to evaluate accuracy on water quality dataset. The experimental results indicate that our method increases the overall accuracy of the experimental dataset compared with the existing methods with the reduced number of significant features.

Sign in / Sign up

Export Citation Format

Share Document