Chance-constrained multi-objective optimization of groundwater remediation design at DNAPLs-contaminated sites using a multi-algorithm genetically adaptive method

Ouyang, Qiubao; Lu, Wenxi; Hou, Zeyu; Zhang, Yu; Li, Shuai; Luo, Jiannan

doi:10.1016/j.jconhyd.2017.03.004

Cited by 37 publications

(13 citation statements)

References 31 publications

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“…SVM parameters greatly impact the prediction ability of the SVM model, therefore, reasonable selection of parameters is important to establish an SVM model. GA is an effective method to select parameters with characteristics of global optimization and computational stability (Lin et al 2013;Lewis and Randall 2017;Ouyang et al 2017). Therefore, GA is used to select SVM parameters, avoiding the subjectivity of artificially selecting parameters and improving the prediction ability of SVM.…”

Section: Methodsmentioning

confidence: 99%

Height prediction of water-flowing fracture zone with a genetic-algorithm support-vector-machine method

Hou

Wen

et al. 2020

Int J Coal Sci Technol

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Prediction of the height of a water-flowing fracture zone (WFFZ) is the foundation for evaluating water bursting conditions on roof coal. By taking the Binchang mining area as the study area and conducting an in-depth study of the influence of coal seam thickness, burial depth, working face length, and roof category on the height of a WFFZ, we proposed that the proportion of hard rock in different roof ranges should be used to characterise the influence of roof category on WFFZ height. Based on data of WFFZ height and its influence index obtained from field observations, a prediction model is established for WFFZ height using a combination of a genetic algorithm and a support-vector machine. The reliability and superiority of the prediction model were verified by a comparative study and an engineering application. The results show that the main factors affecting WFFZ height in the study area are coal seam thickness, burial depth, working face length, and roof category. Compared with multiple-linear-regression and back-propagation neural-network approaches, the height-prediction model of the WFFZ based on a genetic-algorithm support-vector-machine method has higher training and prediction accuracy and is more suitable for WFFZ prediction in the mining area.

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Section: Methodsmentioning

confidence: 99%

Height prediction of water-flowing fracture zone with a genetic-algorithm support-vector-machine method

Hou

Wen

et al. 2020

Int J Coal Sci Technol

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“…The problem can thus be formulated as determining the location and number of wells as well as the required pumping rates at these wells such that the cost is minimum and the desired quality levels are maintained. Several studies used EAs for solving the groundwater remediation problems via the simulation-optimization framework such as GA (Huang & Mayer 1997;Wang & Zheng 1997;Sun & Zheng 1999;Smalley et al 2000;Yoon & Shoemaker 2001;Zheng & Wang 2002;Babbar & Minsker 2006;Wu et al 2006;Park et al 2007;Bayer et al 2008;Seyedpour et al 2019), SA (Kobayashi et al 2008); MOGA (Erickson et al 2002;Mantoglou & Kourakos 2007;Singh et al 2008), NSGA-II (Singh & Chakrabarty 2011;Ouyang et al 2017), etc. More details of these EAs applications are also elaborated in Table 9.…”

Section: Applications In Reservoir Operation and Irrigation Systemsmentioning

confidence: 99%

Evolutionary algorithms, swarm intelligence methods, and their applications in water resources engineering: a state-of-the-art review

Reddy

Kumar

2020

H2Open Journal

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During the last three decades, the water resources engineering field has received a tremendous increase in the development and use of meta-heuristic algorithms like evolutionary algorithms (EA) and swarm intelligence (SI) algorithms for solving various kinds of optimization problems. The efficient design and operation of water resource systems is a challenging task and requires solutions through optimization. Further, real-life water resource management problems may involve several complexities like nonconvex, nonlinear and discontinuous functions, discrete variables, a large number of equality and inequality constraints, and often associated with multi-modal solutions. The objective function is not known analytically, and the conventional methods may face difficulties in finding optimal solutions. The issues lead to the development of various types of heuristic and meta-heuristic algorithms, which proved to be flexible and potential tools for solving several complex water resources problems. This paper provides a review of state-of-the-art methods and their use in planning and management of hydrological and water resources systems. It includes a brief overview of EAs (genetic algorithms, differential evolution, evolutionary strategies, etc.) and SI algorithms (particle swarm optimization, ant colony optimization, etc.), and applications in the areas of water distribution networks, water supply, and wastewater systems, reservoir operation and irrigation systems, watershed management, parameter estimation of hydrological models, urban drainage and sewer networks, and groundwater systems monitoring network design and groundwater remediation. This paper also provides insights, challenges, and need for algorithmic improvements and opportunities for future applications in the water resources field, in the face of rising problem complexities and uncertainties.

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“…Some processes can be solved by physical and numerical models, but still retain some unresolved hypotheses or a trial and error program. Thus, the MOGA is an appropriate method for solving multi-objective nonlinear optimization problems (Lin et al 2013;Lewis and Randall 2017;Ouyang et al 2017). An integrating model combining the MOGA and SVM is developed herein to forecast groundwater level and identify the optimal input combinations.…”

Section: Multi-objective Genetic Algorithm (Moga)mentioning

confidence: 99%

A Two-Stage Approach Integrating SOM- and MOGA-SVM-Based Algorithms to Forecast Spatial-temporal Groundwater Level with Meteorological Factors

Fang

Jhong

Tan

et al. 2018

Water Resour Manage

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To obtain accurate and effective forecasts of groundwater level, a two-stage approach integrating Self-Organizing Maps (SOM-), Multi-Objective Genetic Algorithm and Support Vector Machine (MOGA-SVM-based) algorithms is developed herein using the optimal input combinations of meteorological factors in a complex spatial-temporal groundwater system. In the first stage, an SOM-based clustering method is used to separate distinct and meaningful spatial groundwater zones. In the second stage, a temporal analysis model integrating MOGA with SVM is developed to identify the optimal input combinations. An actual application is conducted using the Choushui River Alluvial Fan in Taiwan as the case study; it currently has over-pumping and land subsidence problems. The MOGA-SVM model is compared with an existing model based on the SVM to demonstrate the superiority of the proposed approach. Moreover, the effective meteorological factors in different spatial zones can be determined by using the proposed approach to show the spatial characteristics, and these factors can significantly improve the forecasting performance, especially for long lead-time forecasting. In conclusion, the proposed spatial-temporal approach is applicable to a huge and complex groundwater system; it provides an alternative to the existing model for water resources management problems.Keywords Groundwater level forecast . Meteorological factor . Self-organizing map (SOM) . Support vector machine (SVM) . Multi-objective genetic algorithm (MOGA) . Choushui River alluvial fan

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Chance-constrained multi-objective optimization of groundwater remediation design at DNAPLs-contaminated sites using a multi-algorithm genetically adaptive method

Cited by 37 publications

References 31 publications

Height prediction of water-flowing fracture zone with a genetic-algorithm support-vector-machine method

Height prediction of water-flowing fracture zone with a genetic-algorithm support-vector-machine method

Evolutionary algorithms, swarm intelligence methods, and their applications in water resources engineering: a state-of-the-art review

A Two-Stage Approach Integrating SOM- and MOGA-SVM-Based Algorithms to Forecast Spatial-temporal Groundwater Level with Meteorological Factors

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