Improving Artificial Neural Network Based Streamflow Forecasting Models through Data Preprocessing

2021 ◽  
Author(s):  
Muhammad Hassan ◽  
Ishtiaq Hassan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Preprocessing ◽  
Streamflow Forecasting ◽  
Forecasting Models ◽  
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 Data Preprocessing for Load Forecasting using Artificial Neural Network

2020 ◽  
Author(s):  
Rafael S. F. Ferraz ◽  
Renato S. F. Ferraz ◽  
Lucas F. S. Azeredo ◽  
Benemar A. de Souza
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power System ◽  
Demand Forecasting ◽  
Energy Resource ◽  
Data Preprocessing ◽  
Electric Power System ◽  
Percentage Error ◽  
Gradual Change ◽  
Artificial Neural

An accurate demand forecasting is essential for planning the electric dispatch in power system, contributing financially to electricity companies and helping in the security and continuity of electricity supply. In addition, it is evident that the distributed energy resource integration in the electric power system has been increasing recently, mostly from the photovoltaic generation, resulting in a gradual change of the load curve profile. Therefore, the 24 hours ahead prediction of the electrical demand of Campina Grande, Brazil, was realized from artificial neural network with a focus on the data preprocessing. Thus, the time series variations, such as hourly, diary and seasonal, were reduced in order to obtain a better demand prediction. Finally, it was compared the results between the forecasting with the preprocessing application and the prediction without the  preprocessing stage. Based on the results, the first methodology presented lower mean absolute percentage error with 7.95% against 10.33% of the second one.

scholarly journals Operational Probabilistic Forecasting of Coastal Freak Waves by Using an Artificial Neural Network

2020 ◽  
Vol 8 (3) ◽  
pp. 165
Author(s):  
Dong-Jiing Doong ◽  
Shien-Tsung Chen ◽  
Ying-Chih Chen ◽  
Cheng-Han Tsai
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Forecasting Model ◽  
Probabilistic Forecasting ◽  
Freak Waves ◽  
Forecasting Models ◽  
Artificial Neural

Coastal freak waves (CFWs) are unpredictable large waves that occur suddenly in coastal areas and have been reported to cause casualties worldwide. CFW forecasting is difficult because the complex mechanisms that cause CFWs are not well understood. This study proposes a probabilistic CFW forecasting model that is an advance on the basis of a previously proposed deterministic CFW forecasting model. This study also develops a probabilistic forecasting scheme to make an artificial neural network model achieve the probabilistic CFW forecasting. Eight wave and meteorological variables that are physically related to CFW occurrence were used as the inputs for the artificial neural network model. Two forecasting models were developed for these inputs. Model I adopted buoy observations, whereas Model II used wave model simulation data. CFW accidents in the coastal areas of northeast Taiwan were used to calibrate and validate the model. The probabilistic CFW forecasting model can perform predictions every 6 h with lead times of 12 and 24 h. The validation results demonstrated that Model I outperformed Model II regarding accuracy and recall. In 2018, the developed CFW forecasting models were investigated in operational mode in the Operational Forecast System of the Taiwan Central Weather Bureau. Comparing the probabilistic forecasting results with swell information and actual CFW occurrences demonstrated the effectiveness of the proposed probabilistic CFW forecasting model.

scholarly journals FLOOD FORECASTING MODEL USING THE COMBINATION APPROACH

2020 ◽  
Vol 1 (2) ◽  
pp. 59-64
Author(s):  
Hu Weighuo ◽  
Hu He
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Logic ◽  
Flood Forecasting ◽  
Forecasting Model ◽  
Forecasting Models ◽  
Highly Efficient ◽  
Artificial Neural ◽  
Combination Approach

This paper reviews the qualities of a good flood forecasting model such as timeliness, accuracy, and reliability. The article reviews the current forecasting models which are based on fuzzy logic, artificial neural network, as well as combination. The combination approach is gaining popularity and is found to be more flexible, accurate, reliable, and highly efficient in terms of development and output.

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