Daily streamflow forecasting by Artificial Neural Network in a large-scale basin

2009 ◽  
Author(s):  
Jingwen Xu ◽  
Xuemei Zhu ◽  
Wanchang Zhang ◽  
Xiaoxun Xu ◽  
Junren Xian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Large Scale ◽  
Streamflow Forecasting ◽  
Daily Streamflow ◽  
Artificial Neural

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

2021 ◽  
Vol 13 (20) ◽  
pp. 4147
Author(s):  
Mohammed M. Alquraish ◽  
Mosaad Khadr
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Superior Performance ◽  
Support Vector ◽  
Streamflow Forecasting ◽  
Forecasting Models ◽  
Monthly Streamflow ◽  
Artificial Neural ◽  
Monthly Streamflow Forecasting

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.

scholarly journals A Novel Radial Basis Neural Network-Leveraged Fast Training Method for Identifying Organs in MR Images

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Min Xu ◽  
Pengjiang Qian ◽  
Jiamin Zheng ◽  
Hongwei Ge ◽  
Raymond F. Muzic
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Magnetic Resonance ◽  
Large Scale ◽  
Medical Images ◽  
High Tech ◽  
Mr Images ◽  
Specific Target ◽  
Radial Basis ◽  
Artificial Neural

We propose a new method for fast organ classification and segmentation of abdominal magnetic resonance (MR) images. Magnetic resonance imaging (MRI) is a new type of high-tech imaging examination fashion in recent years. Recognition of specific target areas (organs) based on MR images is one of the key issues in computer-aided diagnosis of medical images. Artificial neural network technology has made significant progress in image processing based on the multimodal MR attributes of each pixel in MR images. However, with the generation of large-scale data, there are few studies on the rapid processing of large-scale MRI data. To address this deficiency, we present a fast radial basis function artificial neural network (Fast-RBF) algorithm. The importance of our efforts is as follows: (1) The proposed algorithm achieves fast processing of large-scale image data by introducing the ε-insensitive loss function, the structural risk term, and the core-set principle. We apply this algorithm to the identification of specific target areas in MR images. (2) For each abdominal MRI case, we use four MR sequences (fat, water, in-phase (IP), and opposed-phase (OP)) and the position coordinates (x, y) of each pixel as the input of the algorithm. We use three classifiers to identify the liver and kidneys in the MR images. Experiments show that the proposed method achieves a higher precision in the recognition of specific regions of medical images and has better adaptability in the case of large-scale datasets than the traditional RBF algorithm.

Modeling effects of changing land use/cover on daily streamflow: An Artificial Neural Network and curve number based hybrid approach

2013 ◽  
Vol 485 ◽  
pp. 103-112 ◽  
Author(s):  
Sabahattin Isik ◽  
Latif Kalin ◽  
Jon E. Schoonover ◽  
Puneet Srivastava ◽  
B. Graeme Lockaby
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Artificial Neural Network ◽  
Hybrid Approach ◽  
Curve Number ◽  
Daily Streamflow ◽  
Artificial Neural
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