scholarly journals Responses of hydrological model equifinality, uncertainty, and performance to multi-objective parameter calibration

2018 ◽  
Vol 20 (4) ◽  
pp. 864-885 ◽  
Author(s):  
Younggu Her ◽  
Chounghyun Seong
Keyword(s):  
Model Performance ◽  
Mathematical Representation ◽  
Objective Functions ◽  
Parameter Calibration ◽  
Hydrological Simulation ◽  
Multi Objective ◽  
Adequate Number ◽  
Hydrological System ◽  
And Performance ◽  
The Impact

Abstract Multi-objective calibration can help identify parameter sets that represent a hydrological system and enable further constraining of the parameter space. Multi-objective calibration is expected to be more frequently utilized, along with the advances in optimization algorithms and computing resources. However, the impact of the number of objective functions on modeling outputs is still unclear, and the adequate number of objective functions remains an open question. We investigated the responses of model performance, equifinality, and uncertainty to the number of objective functions incorporated in a hierarchical and sequential manner in parameter calibration. The Hydrological Simulation Program – FORTRAN (HSPF) models that were prepared for bacteria total maximum daily load (TMDL) development served as a mathematical representation to simulate the hydrological processes of three watersheds located in Virginia, and the Expert System for Calibration of HSPF (HSPEXP) statistics were employed as objective functions in parameter calibration experiments. Results showed that the amount of equifinality and output uncertainty overall decreased while the model performance was maintained as the number of objective functions increased sequentially. However, there was no further significant improvement in the equifinality and uncertainty when including more than four objective functions. This study demonstrated that the introduction of an adequate number of objective functions could improve the quality of calibration without requiring additional observations.

Use of nonlinear asymmetrical shock absorbers in multi-objective optimization of the suspension system in a variety of road excitations

2016 ◽  
Vol 231 (2) ◽  
pp. 372-387 ◽  
Author(s):  
Abolfazl Seifi ◽  
Reza Hassannejad ◽  
Mohammad Ali Hamed
Keyword(s):  
Degrees Of Freedom ◽  
Ride Comfort ◽  
Low Cost ◽  
Suspension System ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Vehicle Handling ◽  
Multi Objective ◽  
Shock Absorbers ◽  
The Impact

In this study, a new method to improve ride comfort, vehicle handling, and workspace was presented in multi-objective optimization using nonlinear asymmetrical dampers. The main aim of this research was to provide suitable passive suspension based on more efficiency and the low cost of the mentioned dampers. Using the model with five degrees of freedom, suspension system parameters were optimized under sinusoidal road excitation. The main functions of the suspension system were chosen as objective functions. In order to better illustrate the impact of each objective functions on the suspension parameters, at first two-objective and finally five-objective were considered in the optimization problem. The obtained results indicated that the optimized viscous coefficients for five-objective optimization lead to 3.58% increase in ride comfort, 0.74% in vehicle handling ability, and 2.20% in workspace changes for the average of forward and rear suspension.

A New Hybrid Algorithm for Multi-Objective Robust Optimization With Interval Uncertainty

2015 ◽  
Vol 137 (2) ◽  
Author(s):  
Shuo Cheng ◽  
Jianhua Zhou ◽  
Mian Li
Keyword(s):  
Robust Optimization ◽  
Optimization Problems ◽  
Hybrid Approach ◽  
Interval Uncertainty ◽  
Engineering Optimization ◽  
Objective Functions ◽  
Robust Solutions ◽  
Multi Objective ◽  
And Performance

Uncertainty is a very critical but inevitable issue in design optimization. Compared to single-objective optimization problems, the situation becomes more difficult for multi-objective engineering optimization problems under uncertainty. Multi-objective robust optimization (MORO) approaches have been developed to find Pareto robust solutions. While the literature reports on many techniques in MORO, few papers focus on using multi-objective differential evolution (MODE) for robust optimization (RO) and performance improvement of its solutions. In this article, MODE is first modified and developed for RO problems with interval uncertainty, formulating a new MODE-RO algorithm. To improve the solutions’ quality of MODE-RO, a new hybrid (MODE-sequential quadratic programming (SQP)-RO) algorithm is proposed further, where SQP is incorporated into the procedure to enhance the local search. The proposed hybrid approach takes the advantage of MODE for its capability of handling not-well behaved robust constraint functions and SQP for its fast local convergence. Two numerical and one engineering examples, with two or three objective functions, are tested to demonstrate the applicability and performance of the proposed algorithms. The results show that MODE-RO is effective in solving MORO problems while, on the average, MODE-SQP-RO improves the quality of robust solutions obtained by MODE-RO with comparable numbers of function evaluations.

scholarly journals Impact of Catchment Discretization and Imputed Radiation on Model Response: A Case Study from Central Himalayan Catchment

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2339
Author(s):  
Bikas Chandra Bhattarai ◽  
Olga Silantyeva ◽  
Aynom T. Teweldebrhan ◽  
Sigbjørn Helset ◽  
Ola Skavhaug ◽  
...  
Keyword(s):  
Flow Condition ◽  
Hydrological Modeling ◽  
Model Performance ◽  
Low Flow ◽  
Shortwave Radiation ◽  
Snow Cover Area ◽  
Hydrological Simulation ◽  
Model Response ◽  
Simulation Results ◽  
The Impact

Distributed and semi-distributed hydrological modeling approaches commonly involve the discretization of a catchment into several modeling elements. Although some modeling studies were conducted using triangulated irregular networks (TINs) previously, little attention has been given to assess the impact of TINs as compared to the standard catchment discretization techniques. Here, we examine how different catchment discretization approaches and radiation forcings influence hydrological simulation results. Three catchment discretization methods, i.e., elevation zones (Hypsograph) (HYP), regular square grid (SqGrid), and TIN, were evaluated in a highly steep and glacierized Marsyangdi-2 river catchment, central Himalaya, Nepal. To evaluate the impact of radiation on model response, shortwave radiation was converted using two approaches: one with the measured solar radiation assuming a horizontal surface and another with a translation to slopes. The results indicate that the catchment discretization has a great impact on simulation results. Evaluation of the simulated streamflow value using Nash–Sutcliffe efficiency (NSE) and log-transformed Nash–Sutcliffe efficiency (LnNSE) shows that highest model performance was obtained when using TIN followed by HYP (during the high flow condition) and SqGrid (during the low flow condition). Similar order of precedence in relative model performance was obtained both during the calibration and validation periods. Snow simulated from the TIN-based discretized models was validated with Moderate Resolution Imaging Spectroradiometer (MODIS) snow products. Critical Success Indexes (CSI) between TIN-based discretized model snow simulation and MODIS snow were found satisfactory. Bias in catchment average snow cover area from the models with and without using imputed radiation is less than two percent, but implementation of imputed radiation into the Statkraft Hydrological Forecasting Toolbox (Shyft) gives better CSI with MODIS snow.

scholarly journals Optimization techniques on fuzzy inference systems to detect Xanthomonas campestris disease

2021 ◽  
Vol 11 (4) ◽  
pp. 3510
Author(s):  
Julio Barón Velandia ◽  
Camilo Enrique Rocha Calderón ◽  
Daniel David Leal Lara
Keyword(s):  
Xanthomonas Campestris ◽  
Fuzzy Inference ◽  
Model Performance ◽  
Optimization Techniques ◽  
Training Data ◽  
Fuzzy Inference Systems ◽  
The Status ◽  
And Performance ◽  
Inference Systems ◽  
The Impact

<span>This paper shows the outcomes for four optimization models based on fuzzy inference systems, intervened using Quasi-Newton and genetic algorithms, to early assess</span><span> bean plants’ leaves for Xanthomonas campestris<em> </em>disease. The assessment on the status of the plant (sane or ill) is defined through the intensity of the color in the RGB scale for the data-sets and images to analyze the implementation of the models. The best model performance is 99.68% when compared with the training data and a 94% effectiveness rate on the detection of Xanthomonas campestris in a bean leave image. Therefore, these results would allow farmers to take early measures to reduce the impact of the disease on the look and performance of green bean crops.</span>

scholarly journals Multi-objective formulation of MSA for phylogeny estimation

2018 ◽  
Author(s):  
Muhammad Ali Nayeem ◽  
Md. Shamsuzzoha Bayzid ◽  
Atif Hasan Rahman ◽  
Rifat Shahriyar ◽  
M. Sohel Rahman
Keyword(s):  
Phylogenetic Tree ◽  
Phylogenetic Trees ◽  
Real Life ◽  
Objective Functions ◽  
Multiple Sequence ◽  
Basic Step ◽  
Multi Objective ◽  
Phylogeny Estimation ◽  
And Function ◽  
The Impact

AbstractMultiple sequence alignment (MSA) is a basic step in many analyses in computational biology, including predicting the structure and function of proteins, orthology prediction and estimating phylogenies. The objective of MSA is to infer the homology among the sequences of chosen species. Commonly, the MSAs are inferred by optimizing a single function or objective. The alignments estimated under one criterion may be different to the alignments generated by other criteria, inferring discordant homologies and thus leading to different evolutionary histories relating the sequences. In recent past, researchers have advocated for the multi-objective formulation of MSA, to address this issue, where multiple conflicting objective functions are being optimized simultaneously to generate a set of alignments. However, no theoretical or empirical justification with respect to a real-life application has been shown for a particular multi-objective formulation. In this study, we investigate the impact of multi-objective formulation in the context of phylogenetic tree estimation. Employing multi-objective metaheuristics, we demonstrate that trees estimated on the alignments generated by multi-objective formulation are substantially better than the trees estimated by the state-of-the-art MSA tools, including PASTA, MUSCLE, CLUSTAL, MAFFT etc. We also demonstrate that highly accurate alignments with respect to popular measures like sum-of-pair (SP) score and total-column (TC) score do not necessarily lead to highly accurate phylogenetic trees. Thus in essence we ask the question whether a phylogeny-aware metric can guide us in choosing appropriate multi-objective formulations that can result in better phylogeny estimation. And we answer the question affirmatively through carefully designed extensive empirical study. As a by-product we also suggest a methodology for primary selection of a set of objective functions for a multi-objective formulation based on the association with the resulting phylogenetic tree.

scholarly journals Multi-Objective Optimisation for Power System Planning Integrating Sustainability Indicators

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2199
Author(s):  
Taimur Al Shidhani ◽  
Anastasia Ioannou ◽  
Gioia Falcone
Keyword(s):  
Power Generation ◽  
Land Use ◽  
Renewable Energy ◽  
Power System ◽  
Carbon Emissions ◽  
Fossil Fuels ◽  
Objective Functions ◽  
Multi Objective ◽  
Trade Offs ◽  
The Impact

The increase in global electricity demand, along with its impact on climate change, call for integrating sustainability aspects in the power system expansion planning. Sustainable power generation planning needs to fulfill different, often contradictory, objectives. This paper proposes a multi-objective optimisation model integrating four objective functions, including minimisation of total discounted costs, carbon emissions, land use, and social opposition. Other factors addressed in the model include renewable energy share, jobs created, mortality rates, and energy diversity, among others. Single-objective linear optimisations are initially performed to investigate the impact of each objective function on the resulting power generation mix. Minimising land use and discounted total costs favoured fossil fuels technologies, as opposed to minimising carbon emissions, which resulted in increased renewable energy shares. Minimising social opposition also favoured renewable energy shares, except for hydropower and onshore wind technologies. Accordingly, to investigate the trade-offs among the objective functions, Pareto front candidates for each pair of objective functions were generated, indicating a strong correlation between the minimisation of carbon emissions and the social opposition. Limited trade-offs were also observed between the minimisation of costs and land use. Integrating the objective functions in the multi-objective model resulted in various non-dominated solutions. This tool aims to enable decision-makers identify the trade-offs when optimising the power system under different objectives and determine the most suitable electricity generation mix.

scholarly journals Bi-Level Poisoning Attack Model and Countermeasure for Appliance Consumption Data of Smart Homes

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3887
Author(s):  
Mustain Billah ◽  
Adnan Anwar ◽  
Ziaur Rahman ◽  
Syed Md. Galib
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
Model Performance ◽  
Building Energy ◽  
Energy Prediction ◽  
Energy Management Systems ◽  
Attack Model ◽  
Building Energy Management ◽  
And Performance ◽  
The Impact

Accurate building energy prediction is useful in various applications starting from building energy automation and management to optimal storage control. However, vulnerabilities should be considered when designing building energy prediction models, as intelligent attackers can deliberately influence the model performance using sophisticated attack models. These may consequently degrade the prediction accuracy, which may affect the efficiency and performance of the building energy management systems. In this paper, we investigate the impact of bi-level poisoning attacks on regression models of energy usage obtained from household appliances. Furthermore, an effective countermeasure against the poisoning attacks on the prediction model is proposed in this paper. Attacks and defenses are evaluated on a benchmark dataset. Experimental results show that an intelligent cyber-attacker can poison the prediction model to manipulate the decision. However, our proposed solution successfully ensures defense against such poisoning attacks effectively compared to other benchmark techniques.

scholarly journals Multi-Product Multi Echelon Measurements of Perishable Supply Chain: Fuzzy Non-Linear Programming Approach

Mathematics ◽  
2021 ◽  
Vol 9 (17) ◽  
pp. 2093
Author(s):  
Sadia Samar Ali ◽  
Haripriya Barman ◽  
Rajbir Kaur ◽  
Hana Tomaskova ◽  
Sankar Kumar Roy
Keyword(s):  
Linear Programming ◽  
Supply Chain ◽  
Mixed Integer ◽  
Programming Approach ◽  
Objective Functions ◽  
Non Linear Programming ◽  
Multi Objective ◽  
Non Linear ◽  
The Cost ◽  
The Impact

The perishable milk products industry has to deal with multiple pressures such as demand forecasting, price fluctuations, lead time, order batching, and inflated orders along with difficulties of climatic and traffic conditions, storage areas and shipment in unfavorable circumstances. The Indian dairy industry faces immense wattage issue due to improper infrastructure for the cold chain storage facilities, resulting in unsatisfied customers. A study is undertaken to comprehend the supply chain framework that handles perishability issues in production and distribution. Researchers propose a multi-objective mixed-integer non-linear supply chain coordination model under uncertain environments to minimize the cost of transportation, offset wastage of products and neutralize the losses due to insufficiencies of transit and storage amenities. The proposed model is meant for managing the delivery with lesser deterioration losses for producers, warehouses, and retailers. The model considers various costs for holding, halting, discounts on purchased cost, transportation cost for truckload policy under regular and unforeseen circumstances of curfew, and identify the rate of deterioration to know the impact on the cost for all players involved in the SCM framework. To handle uncertainty of objective functions, fuzzy set concepts and the defuzzification method are imposed, and fuzzy non-linear programming algorithms are used to get the single objective function from the defuzzified multi-objective functions. Data analysis is done on Lingo 18.0 software. Rate of deterioration is highest for the warehouse, which indicates that efforts should be made to augment warehouse facilities for less spoilage to reduce losses in cost. Finally, the study ends with main findings, conclusions, limitations and future scopes.

Fuzzy multi-objective decision making approach for nuclear power plant installation

2020 ◽  
Vol 39 (5) ◽  
pp. 6339-6350
Author(s):  
Esra Çakır ◽  
Ziya Ulukan
Keyword(s):  
Linear Programming ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Energy Supply ◽  
Energy Demand ◽  
Nuclear Power ◽  
Power Plants ◽  
Total Duration ◽  
Objective Functions ◽  
Multi Objective

Due to the increase in energy demand, many countries suffer from energy poverty because of insufficient and expensive energy supply. Plans to use alternative power like nuclear power for electricity generation are being revived among developing countries. Decisions for installation of power plants need to be based on careful assessment of future energy supply and demand, economic and financial implications and requirements for technology transfer. Since the problem involves many vague parameters, a fuzzy model should be an appropriate approach for dealing with this problem. This study develops a Fuzzy Multi-Objective Linear Programming (FMOLP) model for solving the nuclear power plant installation problem in fuzzy environment. FMOLP approach is recommended for cases where the objective functions are imprecise and can only be stated within a certain threshold level. The proposed model attempts to minimize total duration time, total cost and maximize the total crash time of the installation project. By using FMOLP, the weighted additive technique can also be applied in order to transform the model into Fuzzy Multiple Weighted-Objective Linear Programming (FMWOLP) to control the objective values such that all decision makers target on each criterion can be met. The optimum solution with the achievement level for both of the models (FMOLP and FMWOLP) are compared with each other. FMWOLP results in better performance as the overall degree of satisfaction depends on the weight given to the objective functions. A numerical example demonstrates the feasibility of applying the proposed models to nuclear power plant installation problem.

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