Abstract
In planning and management of any water resource systems prediction or estimation of runoff over the catchment is considered as a crucial factor. Many researchers over the past two decades addressed these problems by traditional methods as well as with some new techniques. This paper is describable and is focused on the capability of some data driven techniques such as Least Square Support Vector Machines (LS-SVM) and Model Trees with M5 algorithm. These methods were used to estimate runoff of various stations in the catchment area in Upper Krishna basin, Maharashtra State, India, and discussed here two stations namely Shigaon and Gudhe. The specialty of these catchment areas is Shigaon has large area and Gudhe has small area. This was done to see the model performance in both conditions. Additionally metrological data was used in the process to see the performance of models. The quantitative analysis was carried out to check the performance of the accuracy by considering standard statistical performance evaluation metrics and the scatter plots are drawn for evaluating qualitative performances of the developed models. The effect of the various metrological parameters as an input parameter for the rainfall was also investigated.The performance of both the tools was judged with various performance measures and it is found that the results are quite encouraging. LS-SVM models performed better since it has captured all the higher peak discharges for both catchments, indicating LS-SVM is best suited for large sized catchments and MT tool is best suited for the smaller sized catchments. However LS-SVM performance is better as compared to MT as modeling approaches are examined, using the long-term observations of yearly river flow discharges.
Highlights
In hydrology, prediction or estimation of runoff is most complex hydrological phenomena due to temporal and spatial variability of watershed as well as number of variables are involved in the process of rainfall-runoff
The main objective of the study was to explore the potentiality of developing the streamflow estimation models based upon Least Square Support Vector Machines (LS-SVM) and Model Trees (MT) M5P modeling techniques at daily scale using hydrological and metrological data
The performances of LS-SVM might not be considered as good in this catchment taking into considerations the parametric evaluation criteria it can be mentioned that the performance of M5P Model trees increased after adding metrological indicating characteristics of the catchment plays an important model in increasing efficiency of the model and it indicates that the performance of LS-SVM is reduced because of the effect of the metrological parameters on the observed values of rainfall and runoff mighthave lead to inaccurate prediction of the discharge. Another reason for ineffective prediction of runoff value by LS-SVM tool is that rainfall might not occur on all the days of the monsoon and as such there will be zero values of measured rainfall in some of the raingauge stations resulting in less accuracy
Summary
Prediction or estimation of runoff is most complex hydrological phenomena due to temporal and spatial variability of watershed as well as number of variables are involved in the process of rainfall-runoff. In last two decade data driven techniques are being used as an alternative approach for developing the models such as “ANN, Fuzzy logic, and GP, SVM and MT [3]”. Researchers mentions that amongst these there is no doubt that ANN and GP approaches have gained significant importance and popularity for developing rainfall-runoff models but use of tools like LS-SVM and MT can yield good results and can further be explored as an alternative tool for developing rainfall-runoff models“[5], [15]”. The main objective of the study was to explore the potentiality of developing the streamflow estimation models based upon Least Square Support Vector Machines (LS-SVM) and Model Trees (MT) M5P modeling techniques at daily scale using hydrological and metrological data.
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