Using gene expression programming to develop a combined runoff estimate model from conventional rainfall-runoff model outputs

نویسنده

  • A. Y. Shamseldin
چکیده

In previous studies, artificial neural networks have been used to develop a model that combines simulated river flows from several individual rainfall-runoff models e.g. Shamseldin et al., 1997; Abrahart & See, 2002; Shamseldin, O'Connor, & Nasr, 2007. The combined runoff estimate model was found to perform better than the individual models in most of the cases. However, no attempts have been made to explain the inner workings of the combined models or the drivers for their success. The research presented in this study investigates the use of gene expression programming (GEP) to develop a combination rainfall-runoff model through the process of symbolic regression. One of the additional advantages of this approach over the neural combination method is the model’s ability to represent itself in the form of mathematical expressions. The GEP model is developed using the daily simulated river flows of four other rainfall runoff models for the Chu catchment which is located in Vietnam. The four models are the linear perturbation model (LPM), the linearly varying gain factor model (LVGFM), the probability-distributed interacting storage capacity (PDISC) model, and the soil moisture accounting and routing (SMAR) models. In this paper, GeneXproTools 4.0, a powerful soft computing software package, is used to develop the combined model. The program provides transparent modeling solutions in the sense that it provides the users with the mathematical equation describing the combined model. The results reveal that combination using symbolic regression is successful and that a superior combined model can be developed using outputs from other individual models. The structure of the combined model is also investigated in this study. The results show that the combined model is dominated by input information from the PDISC model forming the baseline estimate, to which different permutations and combinations of the remaining inputs from the other models are added. This research, limited to one river catchment, paves the way for further investigations into GEP model development for different types of catchment. Over-fitting of the training set data during the model development observed in this study highlights the need to investigate appropriate stopping criteria.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Modeling Ghotour-Chai River’s Rainfall-Runoff process by Genetic Programming

Considering the importance of water and computing the amount of rainfall runoff resulted from precipitation in recent decades, using appropriate methods for predicting the amount of runoff from rainfall date has been really essential. Rainfall-runoff models are used to estimate runoff generated from precipitation in the catchment area. Rainfall-runoff process is totally a non-linear phenomenon....

متن کامل

Modeling Ghotour-Chai River’s Rainfall-Runoff process by Genetic Programming

Considering the importance of water and computing the amount of rainfall runoff resulted from precipitation in recent decades, using appropriate methods for predicting the amount of runoff from rainfall date has been really essential. Rainfall-runoff models are used to estimate runoff generated from precipitation in the catchment area. Rainfall-runoff process is totally a non-linear phenomenon....

متن کامل

Surface runoff estimation in an upper watershed using geo-spatial based soil conservation service-curve number method

Runoff assessment and estimation is crucial for watershed management as it provides information that is needed to expedite the course of watershed planning and development. The most commonly used model due to its simplicity and versatility in runoff estimation is the soil conservation service curve number developed by the United States Department of Agriculture. The study estimates the surface ...

متن کامل

Long Lead Flood Simulation Using Downscaled GCM Data in Arid and Semi-arid Regions: A Case Study

Flood is one of the most calamitous natural disasters that causes extensive property and life damages across theworld. It however, could be a blessing due to its special natural water resources recharging value. By simulating themagnitude of probable floods considering the anthropogenic and natural effects and implementing contingency plans,their damages could be reduced. In this paper, the Gen...

متن کامل

The Effectiveness of Genetic Planning Model in rainfall-runoff Simulation process

The prediction of river, s discharge rate is one of the important issues in water resources engineering. This issue is very important for the planning, management, and policy making in water resources management, especially in the country like Iran, with limited water resources in line the economic and environmental development. Awareness of how the relationship between rainfall and run...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2009