Water quality prediction: Multi objective genetic algorithm coupled artificial neural network based approach

Genetic algorithm (GA) is improved with fast non-dominated sort approach and crowded comparison operator. A new algorithm called parallel multi-objective genetic algorithm (PMGA) is developed with the support of Massage Passing Interface (MPI). Then, PMGA is combined with Artificial Neural Network (ANN) to improve the optimization efficiency. Training samples of the ANN are evaluated based on the two-dimensional Navier-Stokes equation solver of cascade. To demonstrate the feasibility of the hybrid algorithm, an optimization of a controllable diffusion cascade is performed. The optimization results show that the present method is efficient and trustiness.

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Optimization of subcooled flow boiling in a vertical pipe by using artificial neural network and multi objective genetic algorithm

Applied Thermal Engineering ◽

10.1016/j.applthermaleng.2016.09.114 ◽

2017 ◽

Vol 111 ◽

pp. 1039-1051 ◽

Cited By ~ 9

Author(s):

Hasan Alimoradi ◽

Mehrzad Shams

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Flow Boiling ◽

Vertical Pipe ◽

Subcooled Flow Boiling ◽

Multi Objective ◽

Multi Objective Genetic Algorithm ◽

Subcooled Flow ◽

Artificial Neural

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Twin Shaft-Geared Crankweb Crankshaft System with Optimization of Crankshaft Dimensions Using Integrated Artificial Neural Network-Multi Objective Genetic Algorithm

Periodica Polytechnica Transportation Engineering ◽

10.3311/pptr.10188 ◽

2017 ◽

Vol 47 (1) ◽

pp. 68-81

Author(s):

Anierudh Vishwanathan

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Journal Bearing ◽

Combustion Engine ◽

Design Parameters ◽

Data Set ◽

Multi Objective ◽

Multi Objective Genetic Algorithm ◽

Artificial Neural

This paper suggests a novel design of a multi cylinder internal combustion engine crankshaft which will convert the unnecessary/extra torque provided by the engine into speed of the vehicle. Transmission gear design has been incorporated with crankshaft design to enable the vehicle attain same speed and torque at lower R.P.M resulting in improved fuel economy provided the operating power remains same. This paper also depicts the reduction in the fuel consumption of the engine due to the proposed design of the crankshaft system. In order to accommodate the wear and tear of the crankshaft due to the gearing action, design parameters like crankpin diameter, journal bearing diameter, crankpin fillet radii and journal bearing fillet radii have been optimized for output parameters like stress which has been calculated using finite element analysis with ANSYS Mechanical APDL and minimum volume using integrated Artificial Neural Network-Multi objective genetic algorithm. The data set for the optimization process has been generated using Latin Hypercube Sampling technique.

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

Water ◽

10.3390/w13172392 ◽

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.

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