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Hydrological stream flow modeling in the Talar catchment (central section of the Alborz Mountains, north of Iran): Parameterization and uncertainty analysis using SWAT-CUP

Gholami A., Habibnejad Roshan M., Shahedi K., Vafakhah M., Solaymani K.

Journal of Water and Land Development

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2016

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no. 30

57--69

EN

There are several methods and techniques for measuring the parameters and forecasting the errors in the hydrological models. In this study, semi distributed Soil and Water Asseeement Tool (SWAT) model and SWAT-CUP (CUP – Calibration and Uncertainty Programs) have been applied using SUFI2 program. After collection of data, the whole Talar watershed located in the central section of the Alborz Mountains, north of Iran was separated into 219 hydrological response units (HRU) in 23 sub-watersheds. In order to improve the simulation parameters and obtain better correlation of observed and simulated values, the sensitive parameters were validated to obtain finally the acceptable value of both R2 and Nash–Sutcliffe (NS) coefficients equal to 0.93. Final P-value and t-state were also estimated for sensitive parameters. As a result, the CN2 parameter, which was critical in the initial stage of this research was replaced by the SOL-K parameter (electrical conductivity saturated soil layers) as a critical parameter in the later stage. Results of this study show that the SWAT model can be an effective and useful tool for the assessment and optimal management of water and soil resources.

PL

Istnieje kilka metod i technik pomiaru parametrów oraz przewidywania błędów w modelach hydrologicznych. W prezentowanej pracy zastosowano modele SWAT i SWAT-CUP z użyciem programu SUFI2. Po zgromadzeniu danych cała zlewnia rzeki Talar, zlokalizowana w środkowej części gór Alborz w północnym Iranie, została podzielona na 219 jednostek hydrologicznych (HRU) w 23 podzlewniach. W celu usprawnienia parametrów symulacji oraz lepszego powiązania wartości symulowanych i obserwowanych zweryfikowano parametry wrażliwe, co w efekcie doprowadziło wartości R2 i współczynnika Nasha– Sutcliffa (NS) do akceptowalnej wartości 0,93. Dla tych parametrów ustalono także końcowe wartości P i t. W wyniku przeprowadzonej analizy parametr CN2, krytyczny na wstępnym etapie badań, został zastąpiony parametrem SOL-K (przewodnictwo elektrolityczne nasyconej warstwy gleby). Wyniki badań świadczą, że model SWAT może być wydajnym i użytecznym narzędziem w ocenie oraz optymalnym zarządzaniu zasobami wody i gleby.

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Oil Formation Volume Factor Determination Through a Fused Intelligence

Gholami A.

Acta Geophysica

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2016

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Vol. 64, no. 6

2510--2529

EN

Volume change of oil between reservoir condition and standard surface condition is called oil formation volume factor (FVF), which is very time, cost and labor intensive to determine. This study proposes an accurate, rapid and cost-effective approach for determining FVF from reservoir temperature, dissolved gas oil ratio, and specific gravity of both oil and dissolved gas. Firstly, structural risk minimization (SRM) principle of support vector regression (SVR) was employed to construct a robust model for estimating FVF from the aforementioned inputs. Subsequently, an alternating conditional expectation (ACE) was used for approximating optimal transformations of input/output data to a higher correlated data and consequently developing a sophisticated model between transformed data. Eventually, a committee machine with SVR and ACE was constructed through the use of hybrid genetic algorithm-pattern search (GA-PS). Committee machine integrates ACE and SVR models in an optimal linear combination such that makes benefit of both methods. A group of 342 data points was used for model development and a group of 219 data points was used for blind testing the constructed model. Results indicated that the committee machine performed better than individual models.

3

NMR Parameters Determination through ACE Committee Machine with Genetic Implanted Fuzzy Logic and Genetic Implanted Neural Network

Asoodeh M., Bagheripour P, Gholami A.

Acta Geophysica

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2015

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Vol. 63, no. 3

735--760

EN

Free fluid porosity and rock permeability, undoubtedly the most critical parameters of hydrocarbon reservoir, could be obtained by processing of nuclear magnetic resonance (NMR) log. Despite conventional well logs (CWLs), NMR logging is very expensive and time-consuming. Therefore, idea of synthesizing NMR log from CWLs would be of a great appeal among reservoir engineers. For this purpose, three optimization strategies are followed. Firstly, artificial neural network (ANN) is optimized by virtue of hybrid genetic algorithm-pattern search (GA-PS) technique, then fuzzy logic (FL) is optimized by means of GA-PS, and eventually an alternative condition expectation (ACE) model is constructed using the concept of committee machine to combine outputs of optimized and non-optimized FL and ANN models. Results indicated that optimization of traditional ANN and FL model using GA-PS technique significantly enhances their performances. Furthermore, the ACE committee of aforementioned models produces more accurate and reliable results compared with a singular model performing alone.