Shuffled Complex Evolution-University Of Arizona Research Articles

Integrating remotely sensed leaf area index and leaf nitrogen accumulation with RiceGrow model based on particle swarm optimization algorithm for rice grain yield assessment

Nanjing Agricultural University, National Engineering and Technology Center for InformationAgriculture, Nanjing, Jiangsu 210095, ChinaAbstract. A regional rice (Oryza sativa) grain yield prediction technique was proposed by inte-gration of ground-based and spaceborne remote sensing (RS) data with the rice growth model(RiceGrow) through a new particle swarm optimization (PSO) algorithm. Based on an initial-ization/parameterization strategy (calibration), two agronomic indicators, leaf area index (LAI)and leaf nitrogen accumulation (LNA) remotely sensed by field spectra and satellite images,were combined to serve as an external assimilation parameter and integrated with theRiceGrow model for inversion of three model management parameters, including sowingdate, sowing rate, and nitrogen rate. Rice grain yield was then predicted by inputting these opti-mized parameters into the reinitialized model. PSO was used for the parameterization andregionalization of the integrated model and compared with the shuffled complex evolution–University of Arizona (SCE-UA) optimization algorithm. The test results showed that LAItogether with LNA as the integrated parameter performed better than each alone for cropmodel parameter initialization. PSO also performed better than SCE-UA in terms of runningefficiency and assimilation results, indicating that PSO is a reliable optimization method forassimilating RS information and the crop growth model. The integrated model also hadimproved precision for predicting rice grain yield.

홍수유출 모형 자동 보정의 벌칙함수를 이용한 기능 향상 연구

유역유출 모의 모형의 자동 보정에 주로 사용되는 진화계열의 알고리즘은 무제약 최적화 알고리즘이다. 이러한 진화계열 알고리즘에 제약조건을 반영하기 위해서는 제약조건을 다룰 수 있는 별도의 방법이 요구된다. 본 연구의 목적은 진화계열 알고리즘의 일종인 집합체 혼합진화 알고리즘에 벌칙함수를 적용하여 제약조건을 고려할 수 있도록 하는 것이다. 또한, 제약조건을 고려할 수 있는 집합체 혼합진화 알고리즘을 SWMM의 자동 보정 모듈에 적용하여 기존 자동 보정 모듈의 기능을 개선하는 것이다. 홍수유출 해석에서는 첨두유량과 관련된 지표가 중요하므로 첨두유량의 오차와 첨두유량 발생시간의 오차를 제어할 수 있는 제약조건을 구성하였다. 제약조건을 포함하여 구성된 자동 보정 모듈은 밀양댐 유역과 구로1 빗물펌프장 배수유역의 홍수유출 모의 모형에 대하여 적용되었다. 자동 보정의 결과는 제약조건의 포함 유무에 따른 결과를 비교하여제시되었다. 그 결과, 제약조건을 고려함에 따라 본래의 목적함수를 크게 위배하지 않으면서, 첨두유량과 첨두유량 발생시간의 오차가 크게 개선되었다. 또한, 검증을 통해서도 제약최적화를 통한 자동보정의 적절성이 검토되었다. 결론적으로 벌칙함수를 이용한 제약조건의 반영을 통해 자동 보정 모듈의 기능을 향상시킬 수 있었다. Evolutionary algorithms, which are frequently used in an automatic calibration of watershed runoff simulation models, are unconstrained optimization algorithms. An additional method is required to impose constraints on those algorithms. The purpose of the study is to modify the SCE-UA (shuffled complex evolution-University of Arizona) to impose constraints by a penalty function and to improve performance of the automatic calibration module of the SWMM (storm water management model) linked with the SCE-UA. As indicators related to peak flow are important in watershed runoff event simulation, error of peak flow and error of peak flow occurrence time are selected to set up constraints. The automatic calibration module including the constraints was applied to the Milyang Dam Basin and the Guro 1 Pumping Station Basin. The automatic calibration results were compared with the results calibrated by an automatic calibration without the constraints. Error of peak flow and error of peak flow occurrence time were greatly improved and the original objective function value is not highly violated in the automatic calibration including the constraints. The automatic calibration model with constraints was also verified, and the results was excellent. In conclusion, the performance of the automatic calibration module for watershed runoff event simulation was improved by application of the penalty function to impose constraints.

남한 강수량 분포 추정을 위한 PRISM 매개변수 및 수치표고모형 최적화

생태환경모델링, 수문모델링, 기후변화 영향평가 등 다양한 분야에서 정규 격자 형태의 기후자료에 대한 요구가 증가하고 있다. PRISM(Precipitation-Elevation Regressions on Independent Slopes Model)은 다양한 격자형태의 기후자료 생산방법 중 고지대의 강수량 추정에 유용한 방법이다. 그러나 국내에서는 이 모델의 매개변수 및 모델에 사용되는 수치표고모형의 공간해상도 최적화에 대한 논의가 충분하지 않았다. 이에 본 연구에서는 PRISM을 개발하였다. 그리고 SCE-UA(Shuffled Complex Evolution-University of Arizona) 기법을 이용하여 2000-2005년 1km 공간해상도의 남한 연평균 강수 격자자료를 생산하는 데 필요한 PRISM 매개변수 최적값 및 DEM의 적정 공간해상도를 추정하였다. 아울러 매개변수와 수치표고모형에 대한 PRISM의 민감도 분석을 수행하였다. 그 결과 PRISM 모델에서 관측소 최대 탐색반경(67km)과 최소반경(31km), 지형고도-강수량의 선형회귀식 산정에 필요한 최소 관측소 개수(4개), 수치표고모형의 적정 공간해상도(<TEX>$1{\times}1km$</TEX>) 등을 결정하였다. 그리고 PRISM 모의 결과가 수치표고모형의 공간해상도에 매우 민감하다는 것을 확인하였다. 본 연구결과는 PRISM 기법을 국내에 적용할 때 정확도를 향상시키는데 기여할 것으로 기대된다. The demand for a climatological dataset with a regular spaced grid is increasing in diverse fields such as ecological and hydrological modeling as well as regional climate impact studies. PRISM(Precipitation-Elevation Regressions on Independent Slopes Model) is a useful method to estimate high-altitude precipitation. However, it is not well discussed over the optimization of PRISM parameters and DEM(Digital Elevation Model) resolution in South Korea. This study developed the PRISM and then optimized parameters of the model and DEM resolution for producing a gridded annual average precipitation data of South Korea with 1km spatial resolution during the period 2000-2005. SCE-UA (Shuffled Complex Evolution-University of Arizona) method employed for the optimization. In addition, sensitivity analysis investigates the change in the model output with respect to the parameter and the DEM spatial resolution variations. The study result shows that maximum radius within which station search will be conducted is 67km. Minimum radius within which all stations are included is 31km. Minimum number of stations required for cell precipitation and elevation regression calculation is four. Optimizing DEM resolution is <TEX>$1{\times}1km$</TEX>. This study also shows that the PRISM output very sensitive to DEM spatial resolution variations. This study contributes to improving the accuracy of PRISM technique as it applies to South Korea.

집합체 혼합진화 알고리즘을 이용한 도시유역 홍수유출 모형의 자동 보정에 관한 연구

SWMM은 도시유역의 홍수유출 해석에 관한 대표적인 모형으로서 국 내외에서 활용도가 높은 반면, 다수의 불명확한 매개변수를 포함하고 있어 사용에 어려움이 있다. 본 연구에서는 SWMM에 집합체 혼합진화(SCE-UA) 알고리즘을 결합하여 자동 보정 모듈을 개발하였다. 최적화 문제는 목적함수에 따라 그 결과가 상이하게 도출될 수 있으므로 연구에서는 5개의 단일 목적함수를 적용하여 가장 적합한 목적함수를 도출하였다. 그리고 홍수유출 해석에는 첨두유량의 정확성이 중요하므로 이를 고려할 수 있는 다목적함수를 구성하였고, 파레토 최적해의 결정을 통해 결과를 도출하였다. 작성된 자동 보정 모듈은 구로1 빗물펌프장 유역에 내린 2009년 3개의 강우사상에 적용되었다. 다목적함수의 구성을 통해 자동 보정된 결과는 단일 목적함수에 의해 도출된 결과보다 첨두유량과 유출체적의 오차를 포함한 대부분의 모형평가 지표에서 우수한 것으로 나타났다. 또한, 다목적함수에 의해 보정된 모형의 검증 결과도 신뢰적인 것으로 분석되었다. 본 연구에서 개발된 SWMM의 자동 보정 프로그램은 도시유역의 다양한 홍수유출 해석 문제에 활용될 수 있을 것으로 전망된다. SWMM (Storm Water Management Model) has been widely used in the world as a typical model for flood runoff analysis of urban areas. However, the calibration of the model is difficult, which is an obstacle to easy application. The purpose of the study is to develop an automatic calibration module of the SWMM linked with SCE-UA (Shuffled Complex Evolution-University of Arizona) algorithm. Generally, various objective functions may produce different optimization results for an optimization problem. Thus, five single objective functions were applied and the most appropriate one was selected. In addition to the objective function, another objective function was used to reduce peak flow error in flood simulation. They form a multiple objective function, and the optimization problem was solved by determination of Pareto optima. The automatic calibration module was applied to the flood simulation on the catchment of the Guro 1 detention reservoir and pump station. The automatic calibration results by the multiple objective function were more excellent than the results by the single objective function for model assessment criteria including error of peak flow and ratio of volume between observed and calculated flow. Also, the verification results of the model calibrated by the multiple objective function were reliable. The program could be used in various flood runoff analysis in urban areas.

Modelling and forecasting snowmelt runoff process using the HBV model in the eastern part of Turkey

AbstractSnowmelt runoff in the mountainous eastern part of Turkey is of great importance as it constitutes 60–70% in volume of the total yearly runoff during spring and early summer months. Therefore, determining the amount and timing of snowmelt runoff especially in the Euphrates basin, where large dams are located, is an important task in order to use the water resources of the country in an optimal manner.The HBV model, being one of the well‐known conceptual hydrological models used more than 45 countries over the world, is applied for the first time in Turkey to a small basin of 242 km2 on the headwaters of Euphrates river for 2002–2004 water years. The input data are provided from the automatic snow‐meteorological stations installed at various locations and altitudes in upper Euphrates basin operating in real‐time. Since ground‐based observations can only represent a small part of the region of interest, spatially and temporally distributed snow cover data are acquired through the use of Moderate Resolution Imaging Spectroradiometer (MODIS) optical satellite. In the first part of the study, an automatic model parameter estimation method, Shuffled Complex Evolution, University of Arizona (SCE‐UA), is utilized to calibrate the HBV model parameters with a multi‐variable criteria using runoff as well as snow‐covered area (SCA) to ensure the internal validity of the model. Results show that calibrations against SCA in addition to discharge simulate discharge nearly as well as calibrations against discharge only, but further suggest that longer time periods and more study catchments should be included to achieve more comprehensible conclusions. In the second part of the study, the calibrated HBV model is applied to forecast runoff with a 1‐day lead time using gridded input data from Mesoscale Model 5 (MM5) for the 2004 snowmelt period. Promising results indicate the possible operational use of runoff forecasting driven by numerical weather prediction data for flood mitigation, reservoir operation and dam safety. Copyright © 2009 John Wiley &amp; Sons, Ltd.

적설 및 융설 모의를 포함한 탱크모형의 소양강댐 및 충주댐에 대한 적용

Snow accumulation and snow melt was simulated and included in the computation of the watershed runoff for Soyanggang Dam and Chungju Dam. A modified Tank Model was used for the simulation, which has three serial tanks and a pulse response function. The model parameters were estimated through the global optimization method of Shuffled Complex Evolution-University of Arizona (SCE-UA). A watershed was divided into four zones of elevation. The temperature decrease of the zones was a rate of -0.6/100m. Almost all precipitation from December to February become accumulated as snow, and then the snow melts and runs off from March to April. The average runoff with snow melt was greater than the average runoff without snow melt during the period from March to April. The improved amount from snow melt simulation was about one fifth of the observed one for Soyanggang Dam. The increased amount for Chungju Dam was about one fourth of the observed average runoff during the same period. Although the watershed runoff was simulated including snow melt, it was less than the observed one for both of the dams.

EVOLUTIONARY COMPUTING TECHNIQUES FOR OPTIMAL PRESSURE REGULATION IN WATER DISTRIBUTION NETWORKS

This paper addresses the problem of appropriate electrical motor valves setting for the pressure regulation of a water distribution networks for specified nodal demands by using both genetic algorithm (GA) and a relatively new concept known as Shuffled Complex Evolution-University of Arizona (SCEUA). To demonstrate the performance of both techniques, a simple illustrative example of a controlled water distribution networks is presented showing the effectiveness of both algorithms to regulate the pressure at all the network nodes, between upper and lower values and as near as possible to the target values. Regardless the final mathematical solutions of both algorithms which are approximately the same, results show the superiority of SCE-UA technique to reach the optimal solution using less number of function evaluations than GAs. This paper concludes that the SCE-UA algorithm is well suited to deal with water supply networks problems, which provides a rich field for future research.

Parameter identifiability for catchment-scale erosion modelling: a comparison of optimization algorithms

The Simplex and the Shuffled Complex Evolution—University of Arizona (SCE-UA) optimization algorithms and the simple least-squares (SLS) and the heteroscedastic maximum likelihood estimator (HMLE) were applied to identify erosion parameters for a process-based model of catchment runoff and sediment yield. Four parameter identification problems were posed with the objective of studying the behaviour of the model under four optimization procedures. Error-free data as well as data with correlated error provided the means for determining the effectiveness of the optimization procedures studied. Results showed that the Simplex algorithm and the SLS objective function provided the best parameter estimates at lower levels of error, while the Simplex algorithm and HMLE objective function provided the best parameter estimates at higher levels of error. This was contrary to the expected theoretical outcome since the SCE-UA algorithm was more successful than the simplex algorithm in locating the global minimum on the response surface.