scholarly journals A nonlinear modelling-based high-order response surface method for predicting monthly pan evaporations

2016 ◽  
Author(s):  
Behrooz Keshtegar ◽  
Ozgur Kisi
Keyword(s):  
Response Surface ◽  
Response Surface Method ◽  
High Order ◽  
Climatic Data ◽  
Pan Evaporation ◽  
Surface Method ◽  
Neuro Fuzzy ◽  
Comparison Results ◽  
Fuzzy Genetic ◽  
Better Than

Abstract. Accurate modelling of pan evaporation has a vital importance in the planning and management of water resources. In this paper, the response surface method (RSM) is extended for estimation of monthly pan evaporations using high-order response surface (HORS) function. A HORS function is proposed to improve the accurate predictions with various climatic data, which are solar radiation, air temperature, relative humidity and wind speed from two stations, Antalya and Mersin, in Mediterranean Region of Turkey. The HORS predictions were compared to artificial neural networks (ANNs), neuro-fuzzy (ANFIS) and fuzzy genetic (FG) methods in these stations. Finally, the pan evaporation of Mersin station was estimated using input data of Antalya station in terms of HORS, FG, ANNs, and ANFIS modelling. Comparison results indicated that HORS models performed slightly better than FG, ANN and ANFIS models. The HORS approach could be successfully and simply applied to estimate the monthly pan evaporations.

Adaptive anisotropic response surface method based on univariate dimension-reduction model and its high-order revision

2020 ◽  
Vol 37 (9) ◽  
pp. 3097-3125
Author(s):  
Wenliang Fan ◽  
Wentong Zhang ◽  
Min Li ◽  
Alfredo H.-S. Ang ◽  
Zhengliang Li
Keyword(s):  
Dimension Reduction ◽  
Response Surface ◽  
Response Surface Method ◽  
High Order ◽  
Practical Implementation ◽  
Content Type ◽  
Reduction Model ◽  
Surface Method ◽  
Univariate Function ◽  
Improving Accuracy

Purpose Based on univariate dimension-reduction model, this study aims to propose an adaptive anisotropic response surface method (ARSM) and its high-order revision (HARSM) to balance the accuracy and efficiency for response surface method (RSM). Design/methodology/approach First, judgment criteria for the constitution of a univariate function are derived mathematically, together with the practical implementation. Second, by combining separate polynomial approximation of each component function of univariate dimension-reduction model with its constitution analysis, the anisotropic ARSM is proposed. Third, the high-order revision for component functions is introduced to improve the accuracy of ARSM, namely, HARSM, in which the revision is also anisotropic. Finally, several examples are investigated to verify the accuracy, efficiency and convergence of the proposed methods, and the influence of parameters on the proposed methods is also performed. Findings The criteria for constitution analysis are appropriate and practical. Obtaining the undetermined coefficients for every component functions is easier than the existing RSMs. The existence of special component functions is useful to improve the efficiency of the ARSM. HARSM is helpful for improving accuracy significantly and it is more robust than ARSM and the existing quadratic polynomial RSMs and linear RSM. ARSM and HARSM can achieve appropriate balance between precision and efficiency. Originality/value The constitution of univariate function can be determined adaptively and the nonlinearity of different variables in the response surface can be treated in an anisotropic way.

scholarly journals Reliability Analysis for Bypass Seepage Stability of Complex Reinforced Earth-Rockfill Dam with High-Order Practical Stochastic Response Surface Method

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Jinwen He ◽  
Ping Zhang ◽  
Xiaona Li
Keyword(s):  
Water Level ◽  
Reliability Analysis ◽  
Response Surface ◽  
Response Surface Method ◽  
Failure Probability ◽  
High Order ◽  
Stochastic Response ◽  
Rockfill Dam ◽  
Surface Method ◽  
Reinforced Earth

Practical stochastic response surface method (SRSM) using ordinary high-order polynomials with mixed terms to approximate the true limit state function (LSF) is proposed to analyze the reliability of bypass seepage stability of earth-rockfill dam. Firstly, the orders of random variable are determined with a univariate fitting. Secondly, nonessential variables are excluded to identify possible mixed terms. Thirdly, orthogonal table is used to arrange additional samples, and stepwise regression is conducted to achieve a specific high-order response surface polynomial (RSP). Fourthly, Monte Carlo simulation (MCS) is used to calculate the failure probability, and RSP is updated by arranging several additional samplings around the design point. At last, the Bantou complex reinforced earth-rockfill dam was taken as an example. There are 6 random variables, that is, the upper water level and 5 hydraulic conductivities (HCs). The result shows that a third-order RSP can ensure good precision, and the failure probability of bypass seepage stability is 3.680×10−5 within an acceptable risk range. The HC of concrete cut-off wall and the HC of rockfill are unimportant random variables. Maximum failure probability at the bank slope has positive correlation with the HC of curtain and the upper water level, negative correlation with the HC of alluvial deposits, and less significance with the HC of filled soil. With the increase of coefficient of variation (Cov) of the HC of curtain, the bypass seepage failure probability increases dramatically. Practical SRSM adopts a nonintrusive form. The reliability analysis and the bypass seepage analysis were conducted separately; therefore, it has a high computational efficiency. Compared with the existing SRSM, the RSP of practical SRSM is simpler and the procedure of the reliability analysis is easier. This paper provides a further evidence for readily application of the high-order practical SRSM to engineering.

Optimized River Stream-Flow Forecasting Model Utilizing High-Order Response Surface Method

2016 ◽  
Vol 30 (11) ◽  
pp. 3899-3914 ◽  
Author(s):  
Behrooz Keshtegar ◽  
Mohammed Falah Allawi ◽  
Haitham Abdulmohsin Afan ◽  
Ahmed El-Shafie
Keyword(s):  
Response Surface ◽  
Response Surface Method ◽  
Stream Flow ◽  
High Order ◽  
Forecasting Model ◽  
Surface Method

A Proposal of PSO with Presumption Function of the Solution Landscape by Response Surface Method

2014 ◽  
Vol 134 (9) ◽  
pp. 1293-1298
Author(s):  
Toshiya Kaihara ◽  
Nobutada Fuji ◽  
Tomomi Nonaka ◽  
Yuma Tomoi
Keyword(s):  
Response Surface ◽  
Response Surface Method ◽  
Surface Method
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