A coupled model tree–genetic algorithm scheme for flow and water quality predictions in watersheds

Preis, Ami; Ostfeld, Avi

doi:10.1016/j.jhydrol.2007.11.013

Cited by 49 publications

(28 citation statements)

References 42 publications

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“…Data-driven modeling has been used in areas such as rainfall-runoff and groundwater modeling (Fallah-Mehdipour et al 2014;Tokar and Markus 2000;Solomatine and Dulal 2003;Muttil and Liong 2004;Solomatine et al 2007); flood forecasting (Chen and Yu 2007;Chiang et al 2007); streamflow prediction, and river management (Preis and Ostfeld 2008;Marsili-Libellia et al 2013;Mouton et al 2009;Asefa et al 2006). Water quality constituents have also been predicted using data-driven models in many studies (Preis and Ostfeld 2008;Solomatine et al 2007;Ghavidel and Montaseri 2014;Burchard-Levine et al 2014).…”

Section: Introductionmentioning

confidence: 99%

“…Water quality constituents have also been predicted using data-driven models in many studies (Preis and Ostfeld 2008;Solomatine et al 2007;Ghavidel and Montaseri 2014;Burchard-Levine et al 2014). They are effective in building knowledge-driven simulations, that are capable of extracting different system states when the nature of complex relationships is unknown, or when the available models are inadequate (Solomatine et al 2007).…”

Section: Introductionmentioning

confidence: 99%

“…They are effective in building knowledge-driven simulations, that are capable of extracting different system states when the nature of complex relationships is unknown, or when the available models are inadequate (Solomatine et al 2007). It is always useful to have modeling alternatives and to be able validate the simulation results of physically based models with data-driven ones, or vice versa (Preis and Ostfeld 2008;Solomatine and Dulal 2003).…”

Section: Introductionmentioning

confidence: 99%

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Case Study of the Chicago River Watershed: Physical Modeling vs Data-driven Modeling of an Urban Watershed

Mahdi

Elhadi

Pagilla

2017

JWMM

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We developed a water quality model for the highly urbanized Chicago River watershed based on hydrologic simulation using BASINS/HSPF. Appropriate consideration was given to the effective impervious area (EIA). The 5 y water quality simulation resulted in finding total nitrates loadings at both point and nonpoint sources. However, it is always useful to have modeling alternatives to validate the simulation results of a physically based model with a data-driven one. Data-driven modeling has gained a lot of attention in recent decades in both hydrology and water resources research. While physically based models require the description of system inputs, physical laws and boundary and initial conditions, a data-driven model simply extracts knowledge from a large amount of data with only a limited number of assumptions about the physical behaviour of the system. For this case study, both data-driven and physical models were considered to simulate total nitrates. Comparing the performance of the two modeling approaches, the data-driven models show better performance. RMSE for regression models showed an increase in prediction performance of up to 10.7 %. Data-driven models require fewer inputs and can be deployed anywhere in the watershed, while physical models require extensive data inputs and can only be applied to the specific watershed outlets selected in the simulation.These arguments suggest the complementary use of both physical and data-driven models. The physical model can be a planning tool whenever significant physical change takes place in the watershed. The data-driven model can be an operating tool that can be periodically used to inspect the watershed water quality parameters, especially if TMDL and WQS are established for the watershed.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Case Study of the Chicago River Watershed: Physical Modeling vs Data-driven Modeling of an Urban Watershed

Mahdi

Elhadi

Pagilla

2017

JWMM

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“…Pollution models were developed in this stage to help government control the pollution sources [8,9]. Also, during this stage, new methods were used in some models to simulate specific scenarios, such as fuzzy inference systems [10], genetic algorithm [11], neural network [12] and support vector machine [13]. Although being important developments, these models are not discussed in this review.…”

Section: Introductionmentioning

confidence: 99%

A review of hydrological/water-quality models

Gao¹,

Li²

2014

Front. Agr. Sci. Eng.

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Water quality models are important in predicting the changes in surface water quality for environmental management. A range of water quality models are wildly used, but every model has its advantages and limitations for specific situations. The aim of this review is to provide a guide to researcher for selecting a suitable water quality model. Eight well known water quality models were selected for this review: SWAT, WASP, QUALs, MIKE 11, HSPF, CE-QUAL-W2, ELCOM-CAEDYM and EFDC. Each model is described according to its intended use, development, simulation elements, basic principles and applicability (e.g., for rivers, lakes, and reservoirs and estuaries). Currently, the most important trends for future model development are: (1) combination models-individual models cannot completely solve the complex situations so combined models are needed to obtain the most appropriate results, (2) application of artificial intelligence and mechanistic models combined with nonmechanistic models will provide more accurate results because of the realistic parameters derived from nonmechanistic models, and (3) integration with remote sensing, geographical information and global position systems (3S) -3S can solve problems requiring large amounts of data.

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“…2) Artificial intelligence (AI) technique is an efficient alternative tool for complex and nonlinear system modeling in many disciplines. [3][4][5][6][7] Specialists and scholars have applied AI to the area of rolling and many new theories and algorithms have been widely used. The development of intelligent control provides new thinking and ways for flatness control technology.…”

Section: Introductionmentioning

confidence: 99%

Flatness Intelligent Control Based on T-S Cloud Inference Neural Network

et al. 2014

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The accuracy of traditional flatness control methods are limited and it is difficult to establish a precise mathematical model of the rolling mill. In addition, the flatness control system is complex and multivariate. General model approaches can not satisfy the high precision demand of rolling process. In this paper, T-S cloud inference neural network and its stability are proposed. It is constructed by cloud model and T-S fuzzy neural network. The stability of T-S cloud inference neural network is analyzed by Lyapunov method in details. Based on the new network, flatness recognition model and flatness predictive model are established. And they are applied for 900HC reversible cold rolling mill. The flatness control system is designed and a simple controller is developed. Initial parameters of the controller are firstly determined through offline training based on measured data, and then they are optimized online automatically. Genetic Algorithm (GA) is used as the optimizing method which is compared with particle swarm optimization (PSO). The simulation results demonstrate that the flatness control system is effective and has a better precision and robustness.

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A coupled model tree–genetic algorithm scheme for flow and water quality predictions in watersheds

Cited by 49 publications

References 42 publications

Case Study of the Chicago River Watershed: Physical Modeling vs Data-driven Modeling of an Urban Watershed

Case Study of the Chicago River Watershed: Physical Modeling vs Data-driven Modeling of an Urban Watershed

A review of hydrological/water-quality models

Flatness Intelligent Control Based on T-S Cloud Inference Neural Network

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