Remote-Sensing-Based Streamflow Forecasting Using Artificial Neural Network and Support Vector Machine Models

Mohammed M Alquraish,Mosaad Khadr

doi:10.3390/rs13204147

Abstract

In this study, we aimed to investigate the hydrological performance of three gridded precipitation products—CHIRPS, RFE, and TRMM3B42V7—in monthly streamflow forecasting. After statistical evaluation, two monthly streamflow forecasting models—support vector machine (SVM) and artificial neural network (ANN)—were developed using the monthly temporal resolution data derived from these products. The hydrological performance of the developed forecasting models was then evaluated using several statistical indices, including NSE, MAE, RMSE, and R2. The performance measures confirmed that the CHIRPS product has superior performance compared to RFE 2.0 and TRMM data, and it could provide reliable rainfall estimates for use as input in forecasting models. Likewise, the results of the forecasting models confirmed that the ANN and SVM both achieved acceptable levels of accuracy for forecasting streamflow; however, the ANN model was superior (R2 = 0.898–0.735) to the SVM (R2 = 0.742–0.635) in both the training and testing periods.

Highlights

Victor Hugo Coelho RabeloReliable streamflow forecasting is a topic of concern in hydrological studies for the operation of flood and drought mitigation systems and the operation and planning of reservoirs [1,2,3]
The forecasting model and process of model training was (Equations performed(16)–(19))
The forecasting model and process of model training was performed to obtain the best evaluation paselected forecasting model was tested, and the model forecasted values obtained from the and cipitation products was evaluated using the data for the period of

Summary

Introduction

Reliable streamflow forecasting is a topic of concern in hydrological studies for the operation of flood and drought mitigation systems and the operation and planning of reservoirs [1,2,3]. In this regard, numerous attitudes and models have been applied to improve the modeling and simulation of streamflow forecasting [4,5,6,7,8]. Data-driven models can forecast streamflow with appropriate accuracy and rely on the physics of hydrological problems as their key feature [9]. Multilinear regression (MLR) is recognized as performing quite well for long-term forecasting, and it is one of the typical forms of data-driven models [13]

Objectives

Methods

Results

Discussion

Conclusion