A survey on river water quality modelling using artificial intelligence models: 2000–2020

Tiyasha, Tiyasha; Tung, Tran Minh; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher

doi:10.1016/j.jhydrol.2020.124670

Cited by 378 publications

(107 citation statements)

References 294 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The models were evaluated with respect to three commonly used statistics: root mean square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency (NSE) [80][81][82]. RMSE and MAE varied from 0 to positive infinity.…”

Section: Application and Resultsmentioning

confidence: 99%

Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches

Adnan

Heddam

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

et al. 2020

Sustainability

Self Cite

View full text Add to dashboard Cite

The potential or reference evapotranspiration (ET0) is considered as one of the fundamental variables for irrigation management, agricultural planning, and modeling different hydrological pr°Cesses, and therefore, its accurate prediction is highly essential. The study validates the feasibility of new temperature based heuristic models (i.e., group method of data handling neural network (GMDHNN), multivariate adaptive regression spline (MARS), and M5 model tree (M5Tree)) for estimating monthly ET0. The outcomes of the newly developed models are compared with empirical formulations including Hargreaves-Samani (HS), calibrated HS, and Stephens-Stewart (SS) models based on mean absolute error (MAE), root mean square error (RMSE), and Nash-Sutcliffe efficiency. Monthly maximum and minimum temperatures (Tmax and Tmin) observed at two stations in Turkey are utilized as inputs for model development. In the applications, three data division scenarios are utilized and the effect of periodicity component (PC) on models’ accuracies are also examined. By importing PC into the model inputs, the RMSE accuracy of GMDHNN, MARS, and M5Tree models increased by 1.4%, 8%, and 6% in one station, respectively. The GMDHNN model with periodic input provides a superior performance to the other alternatives in both stations. The recommended model reduced the average error of MARS, M5Tree, HS, CHS, and SS models with respect to RMSE by 3.7–6.4%, 10.7–3.9%, 76–75%, 10–35%, and 0.8–17% in estimating monthly ET0, respectively. The HS model provides the worst accuracy while the calibrated version significantly improves its accuracy. The GMDHNN, MARS, M5Tree, SS, and CHS models are also compared in estimating monthly mean ET0. The GMDHNN generally gave the best accuracy while the CHS provides considerably over/under-estimations. The study indicated that the only one data splitting scenario may mislead the modeler and for better validation of the heuristic methods, more data splitting scenarios should be applied.

show abstract

Section: Application and Resultsmentioning

confidence: 99%

Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches

Adnan

Heddam

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

et al. 2020

Sustainability

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the modelling of four biological indices, a deep learning technique based on the artificial neural network was used. In our investigation, we used the multi-layer perceptron (MLP) type of network, which is commonly used in water quality modelling (Tiyasha et al 2020 ). The MLP has many advantages such as self-adaptive iterative algorithms, highly flexible function approximator, no need to know the mathematical structure of the relationships studied and prior knowledge of them, and the possibility of using in both linear and nonlinear relationships.…”

Section: Methodsmentioning

confidence: 99%

“…It must, however, be stressed that all of the disadvantages of big datasets also require the use of adequate analytical tools, such as random forests, genetic algorithms and deep learning methods, which are based on artificial neural networks (Benedini and Tsakiris 2013;Secchi 2018;Shi 2018;Sun and Scanlon 2019). The deep learning technics have the potential to be applied to diverse research of any aquatic organisms (Iqbal et al 2019;Joutsijoki et al 2014;Tiyasha et al 2020) as well as water quality issues (Alizadeh et al 2018;Kargar et al 2020;Li et al 2015;Zhu et al 2019). Models based on artificial neural networks are recommended to solve complex and nonlinear relationships in ecological study (Park and Lek 2016) and often provided more efficient results compared with the classical modelling techniques (Heddam 2016;Wu et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Modelling of ecological status of Polish lakes using deep learning techniques

Gebler

Kolada

Pasztaleniec

et al. 2020

Environ Sci Pollut Res

View full text Add to dashboard Cite

Since 2000, after the Water Framework Directive came into force, aquatic ecosystems’ bioassessment has acquired immense practical importance for water management. Currently, due to extensive scientific research and monitoring, we have gathered comprehensive hydrobiological databases. The amount of available data increases with each subsequent year of monitoring, and the efficient analysis of these data requires the use of proper mathematical tools. Our study challenges the comparison of the modelling potential between four indices for the ecological status assessment of lakes based on three groups of aquatic organisms, i.e. phytoplankton, phytobenthos and macrophytes. One of the deep learning techniques, artificial neural networks, has been used to predict values of four biological indices based on the limited set of the physicochemical parameters of water. All analyses were conducted separately for lakes with various stratification regimes as they function differently. The best modelling quality in terms of high values of coefficients of determination and low values of the normalised root mean square error was obtained for chlorophyll a followed by phytoplankton multimetric. A lower degree of fit was obtained in the networks for macrophyte index, and the poorest model quality was obtained for phytobenthos index. For all indices, modelling quality for non-stratified lakes was higher than this for stratified lakes, giving a higher percentage of variance explained by the networks and lower values of errors. Sensitivity analysis showed that among physicochemical parameters, water transparency (Secchi disk reading) exhibits the strongest relationship with the ecological status of lakes derived by phytoplankton and macrophytes. At the same time, all input variables indicated a negligible impact on phytobenthos index. In this way, different explanations of the relationship between biological and trophic variables were revealed.

show abstract

“…xii. The evaluation criteria were determined based on the root mean square error (RMSE) and the coefficient of efficiency (CE) [50].…”

Section: E Forecast Experimentsmentioning

confidence: 99%

The Capacity of the Hybridizing Wavelet Transformation Approach With Data-Driven Models for Modeling Monthly-Scale Streamflow

et al. 2020

Self Cite

View full text Add to dashboard Cite

Hybrid models that combine wavelet transformation (WT) as a pre-processing tool with datadriven models (DDMs) as modeling approaches have been widely investigated for forecasting streamflow. The WT approach has been applied to original time series for decomposing processes prior to the application of DDM modeling. This procedure has been applied to eliminate redundant patterns or information that lead to a dramatic increase in the model performance. In this study, three experiments were implemented, including stand-alone data-driven modeling, hind cast decomposing using WT divided and entered into the extreme learning machine (ELM), and the extreme gradient boosting (XGB) model to forecast streamflow data. The WT method was applied in two forms: discrete and continuous (DWT and CWT). In this paper, a new hybrid model is proposed based on an integrative prediction model where XGB is used as an input selection tool for the importance attributes of the prediction matrix that are then supplied to the ELM model as a predictive model. The monthly streamflow, upstream flow, rainfall, temperature, and potential evapotranspiration of a basin named in 1805 and located in the south east of Turkey, are used for development of the model. The modeling results show that applying the WT method improved the performance in the hindcast experiment based on the CWT form with minimum root mean square error (RMSE = 4.910 m 3 /s). On the contrary, WT deteriorated the performance of the forecasting and the stand-alone models exhibited a better performance. WT increased the performance of the hindcast experiment due to the inclusion of future information caused by convolution of the time series. However, the forecast experiment experienced deterioration due to the border effect at the end of the time series. Hence, WT was found not to be a useful pre-processing technique in forecasting the streamflow.

show abstract

A survey on river water quality modelling using artificial intelligence models: 2000–2020

Cited by 378 publications

References 294 publications

Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches

Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches

Modelling of ecological status of Polish lakes using deep learning techniques

The Capacity of the Hybridizing Wavelet Transformation Approach With Data-Driven Models for Modeling Monthly-Scale Streamflow

Contact Info

Product

Resources

About