Modeling Water-Quality Parameters Using Genetic Algorithm–Least Squares Support Vector Regression and Genetic Programming

Bozorg‐Haddad, Omid; Soleimani, Shima; Loáiciga, Hugo A.

doi:10.1061/(asce)ee.1943-7870.0001217

Cited by 59 publications

(19 citation statements)

References 45 publications

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“…These inequality constraints are transformed into equality constraints in LSSVR, which speeds up training process and is useful for prediction. Xiaohui and Xiaoping (2010) constructed a LS‐SVM prediction model for the mine water chaotic time series and Bozorg‐Haddad, Soleimani, and Loaiciga (2017) used the genetic algorithm (GA) and LSSVR to produce the GA–LSSVR algorithm. They all achieved improved accuracy in modeling water‐quality parameters.…”

Section: Establishment Of Data‐driven Model Based On Pso–lssvrmentioning

confidence: 99%

Data‐driven modeling for crystal size distribution parameters in cane sugar crystallization process

Meng

Tao

et al. 2021

J Food Process Engineering

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Crystal size distribution (CSD) is important in evaluating crystal quality in cane sugar crystallization process. Due to the complex non-linearity, time-delay and strong coupling in cane sugar crystallization process, it is difficult to directly modeling in the mechanism of cane sugar crystallization process to obtain CSD parameters. In order to obtain two main CSD parameters so that to achieve better control and production of cane sugar, this article constructs a data-driven model based on least squares support vector regression (LSSVR) and particle swarm optimization (PSO). Based on LSSVR, the model takes the easy to measureable variables (massecuite brix, massecuite level, massecuite temperature, steam pressure, feeding volume, and vacuum degree) as input variables, and outputs sugar CSD parameters (crystal average size, coefficient of variation of crystal size). PSO algorithm is used to optimize the key parameters of primary model to get better modeling performance. Compared with other modeling methods such as back propagation, extreme learning machine, radial basis function, and SVR, the constructed PSO-LSSVR model has obvious advantages over other models in learning speed and predictive effect, generalization ability. This model has potential to be applied to the control system of cane sugar crystallization process and get better product of sugar.

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Section: Establishment Of Data‐driven Model Based On Pso–lssvrmentioning

confidence: 99%

Data‐driven modeling for crystal size distribution parameters in cane sugar crystallization process

Meng

Tao

et al. 2021

J Food Process Engineering

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“…The maximum allowable pollutant discharge was determined for four pollution indexes, including total nitrogen, total phosphorus, ammonia and chemical oxygen demand (COD). Bozorg-Haddad et al (2017) employed two data-driven methods for modelling water-quality parameters.…”

Section: Fulfillment Of River Environmental Flow: Applying Nash Theory For Quantitative-qualitative Conflict Resolution In Reservoir Opermentioning

confidence: 99%

“…Bozorg‐Haddad et al . (2017) employed two data‐driven methods for modelling water‐quality parameters. The methods are the least‐squares support vector regression and genetic programming similar to that developed by Fallah‐Mehdipour et al .…”

Section: Introductionmentioning

confidence: 99%

Fulfillment of river environmental flow: applying Nash theory for quantitative‐qualitative conflict resolution in reservoir operation

Farhadian

Bozorg‐Haddad

Loáiciga

2020

Water & Environment J

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The construction of reservoirs for water supply alters the riverine ecology and changes water quality. It is, therefore, imperative to estimate and provide the riverine environmental flow requirement to prevent irreversible environmental damage and to maintain suitable water quality. Water releases from reservoirs constitute the means for meeting environmental flow and water quality requirements downstream from reservoirs. These requirements can be realized through the modification of the reservoir operation programmes by reallocating a percentage of the reservoir releases otherwise dedicated to quantitative objectives. This is performed in this study by considering the qualitative (i.e. pertaining to water quality) objectives alongside quantitative objectives of reservoir releases in order to achieve a general coverage for an operating programme that balances in the most rational manner the interests of riverine environmental protection and quantitative functions. The case study area is located in the river reach between the Gotvand reservoir and Shooshtar city in the Gotvand‐Karoon reservoir‐river system in Iran. The range of riverine environmental flow is calculated in the study river reach with hydrologic methods. Water quality is simulated and included reservoir operation with the QUAL‐2K simulation model. The existing quantitative and qualitative objectives for reservoir operation are determined and classified. The best reservoir releases corresponding to three scenarios are determined by the Nash theory considering all possible objectives in the reservoir‐river system. These scenarios allocate relative weights to defined objectives and releases were determined to be equal to 183.8, 141.8 and 206.8 m3/s for three scenarios.

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“…Little work has been done applying SVC to geomechanical problems, however, a case study from water quality literature is discussed here. An SVC algorithm was employed to model the electric conductivity and total dissolved solids in a river system and was compared against a more conventional/genetic programming algorithm [72]. The authors concluded that the SVC method has better accuracy for modelling water quality parameters than the genetic programming algorithm [73].…”

Section: Support Vector Clusteringmentioning

confidence: 99%

An Overview of Opportunities for Machine Learning Methods in Underground Rock Engineering Design

2019

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Machine learning methods for data processing are gaining momentum in many geoscience industries. This includes the mining industry, where machine learning is primarily being applied to autonomously driven vehicles such as haul trucks, and ore body and resource delineation. However, the development of machine learning applications in rock engineering literature is relatively recent, despite being widely used and generally accepted for decades in other risk assessment-type design areas, such as flood forecasting. Operating mines and underground infrastructure projects collect more instrumentation data than ever before, however, only a small fraction of the useful information is typically extracted for rock engineering design, and there is often insufficient time to investigate complex rock mass phenomena in detail. This paper presents a summary of current practice in rock engineering design, as well as a review of literature and methods at the intersection of machine learning and rock engineering. It identifies gaps, such as standards for architecture, input selection and performance metrics, and areas for future work. These gaps present an opportunity to define a framework for integrating machine learning into conventional rock engineering design methodologies to make them more rigorous and reliable in predicting probable underlying physical mechanics and phenomenon.

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Modeling Water-Quality Parameters Using Genetic Algorithm–Least Squares Support Vector Regression and Genetic Programming

Cited by 59 publications

References 45 publications

Data‐driven modeling for crystal size distribution parameters in cane sugar crystallization process

Data‐driven modeling for crystal size distribution parameters in cane sugar crystallization process

Fulfillment of river environmental flow: applying Nash theory for quantitative‐qualitative conflict resolution in reservoir operation

An Overview of Opportunities for Machine Learning Methods in Underground Rock Engineering Design

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