Estimation of monthly discharge using climatological and physiographic parametesr of ungauged basin

Document Type : Complete scientific research article

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Abstract

Estimation of monthly discharge using climatic and physiographic parameters of ungauged basins
Abstract
Estimation of monthly discharge using climatic and physiographic parameters of ungauged basins
Background and Objectives:
Discharge estimation in watersheds with limited statistical data is of interest of researchers especially in developing countries. In many cases, recorded discharges are not available or insufficient in terms of quality and quantity that lead to problems for water resources management plans. Therefore, methods to predict the river discharges in ungauged or with limited recorded data have considerable importance. Various methods including statistical models, time series and expert models have been developed for discharge estimation. The decision tree model is one of expert models that model the nonlinear behavior of data using simple rules production. The objective of this study is evaluation of multivariate regression and decision tree model (M5) to estimate monthly discharge in ungauged watersheds in Golestan Province.
Materials and Methods:
In this study, the Golestan province that includes various watersheds with different characteristics was considered. The physiographic characteristics of studied watershed were extracted and rainfall and temperature climatic parameters were estimated in monthly time scale in GIS environment for the period 1981-2011. The climatic and physiographic parameters were considered as input to multivariate regression and decision trees M5 models and root mean square error (RMSE) and correlation coefficient (R) applied for models evaluation.
Results:
According to the results of multiple regression and decision tree models, discharge estimations in wet months were more accurate than dry months. In application of decision tree model the best prediction belonging to March with R=0.93 and RMSE=1.002 while worst prediction was for August with R=0.571 and RMSE=0.635. Moreover, multivariate regression model led to best results in March with R=0.723 and RMSE=2.043 and low accurate prediction in August with R=0.322 and RMSE=1.979. The results of decision tree model were better than multivariate regression model in all months based in calculations.

Conclusion:
The results of this study showed that the discharge estimation using multivariate regression and decision tree M5 models in wet months are applicable but in dry months predictions are not accurate because of high rainfall variations, storm patterns and error in rainfall zoning and interpolation. The results indicated that the decision tree model had more accurate results than multivariate regression model considering higher precision and lower error. The decision tree model had higher correlation coefficient with respect to model evaluation criteria.
Keywords: River Discharge, Ungauged Basins, M5 Decision Tree Model, Multivariate Regression

Keywords

References

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Journal of Water and Soil Conservation
Volume 23, Issue 3
September 2016
Pages 207-224

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