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An Efficient Approach of Artificial Neural Network in Runoff Forecasting

Satanand Mishra, Prince Gupta, S. K. Pandey, J. P. Shukla
2014 International Journal of Computer Applications  
The long-term and short-term forecasting model was developed for runoff forecasting using various approaches of Artificial Neural Network techniques.  ...  This study compares various approaches available for runoff forecasting of artificial neural networks (ANNs).  ...  The discrete wavelet transform is used to preprocess the input data for ANN model by decompose time series data into wavelet coefficients.  ... 
doi:10.5120/16003-4991 fatcat:r4d2vpehofeezpelbb2hvljsai

Study of Time Series Data Mining for the Real Time Hydrological Forecasting: A Review

Satanand Mishra, C. Saravanan, V. K. Dwivedi
2015 International Journal of Computer Applications  
Researchers are developed models for runoff forecasting using the data mining tools and techniques like regression analysis, clustering, artificial neural network (ANN), and support vector machine (SVM  ...  Now a day's comparison of ANN, fuzzy logic and genetic algorithms for rainfall-runoff modeling has carried out. The study described pros and cons of these algorithms and suggest  ...  Yoon H. et al developed two nonlinear time series models for predicting ground water level fluctuations using artificial neural networks (ANNs) and support vector machines (SVMs).  ... 
doi:10.5120/20692-3581 fatcat:v3vfg7a4wfgwxfdgxdgvlhvrfq

Time Series Data Mining in Real Time Surface Runoff Forecasting through Support Vector Machine

Vinayak Choubey, Satanand Mishra, S. K. Pandey
2014 International Journal of Computer Applications  
This study presents support vector machine based model for forecasting the runoff-rainfall events. A SVM based model is either implemented through Radial base or Gaussian based Kernel functions.  ...  In this research the Sequential minimal optimization algorithm (SMO) has been implemented as an effective method for training support vector machines (SVMs) on classification tasks defined on large and  ...  The authors would like to thank the Central Water Commission, Ministry of Water Resources, India for providing Water Level and Discharge data.  ... 
doi:10.5120/17163-7223 fatcat:hsnour6fgvdjfh4emdp6ekz6wq

Forecasting monthly groundwater level fluctuations in coastal aquifers using hybrid Wavelet packet–Support vector regression

N. Sujay Raghavendra, Paresh Chandra Deka, Sanjay Shukla
2015 Cogent Engineering  
Wavelet packet-Support vector regression (WP-SVR) model for forecasting monthly groundwater level fluctuations observed in three shallow unconfined coastal aquifers.  ...  The input variables used for modeling were monthly time series of total rainfall, average temperature, mean tide level, and past groundwater level observations recorded during the period 1996-2006 at three  ...  and the Department of Applied Mechanics & Hydraulics, National Institute of Technology Karnataka for the necessary infrastructural support.  ... 
doi:10.1080/23311916.2014.999414 fatcat:tawjgggsvjgvdpnnb5xhvlfiza

A Comparison of Three Soft Computing Techniques, Bayesian Regression, Support Vector Regression, and Wavelet Regression, for Monthly Rainfall Forecast

Ashutosh Sharma, Manish Kumar Goyal
2017 Journal of Intelligent Systems  
This paper presents a comparison between three soft computing techniques, namely Bayesian regression (BR), support vector regression (SVR), and wavelet regression (WR), for monthly rainfall forecast in  ...  A WR model is a combination of discrete wavelet transform and linear regression. Monthly rainfall data for 102 years from 1901 to 2002 at 21 stations were used for this study.  ...  Bayesian regression (BR), support vector regression (SVR), and wavelet regression (WR), for rainfall forecasting.  ... 
doi:10.1515/jisys-2016-0065 fatcat:mss4vfgwb5dv5lbuxcs2koo6oi

A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting

Karim Sherif Mostafa Hassan Ibrahim, Yuk Feng Huang, Ali Najah Ahmed, Chai Hoon Koo, Ahmed El-Shafie
2021 Alexandria Engineering Journal  
Nevertheless, AI models are also required to be optimized in tandem to achieve the best result, leading thus to the desirous forming of hybrid models between a standalone AI model and optimization techniques  ...  However, due to the major global climate changes which affect rainfall trend and weather, it has been a necessity to find an alternative solution for effective conventional water balance.  ...  In 2019 support vector machine has been used for daily forecasting of dam water levels with a time series regression model [47] .  ... 
doi:10.1016/j.aej.2021.04.100 fatcat:fppujuentje67dekhgnfe5qt3y

Crack Prediction Based on Wavelet Correlation Analysis Least Squares Support Vector Machine for Stone Cultural Relics

Bao Liu, Fei Ye, Kun Mu, Jingting Wang, Jinyu Zhang, Nhon Nguyen Thanh
2021 Mathematical Problems in Engineering  
In this paper, under the idea of multisource information fusion, a least squares support vector machine regression method based on multivariate time series wavelet correlation analysis is proposed to achieve  ...  The experimental results show that the proposed method is more effective than the traditional backpropagation neural network, support vector machine, and relevance vector machine regression methods.  ...  Wavelet Correlation Analysis LSSVM-Based Crack Prediction. e structure of the model for crack prediction of stone cultural relics based on wavelet correlation analysis least squares support vector machine  ... 
doi:10.1155/2021/6638521 fatcat:yonk5c5q45h5ldqx5d2bl335ne

The combined use of wavelet transform and black box models in reservoir inflow modeling

Umut Okkan, Zafer Ali Serbes
2013 Journal of Hydrology and Hydromechanics  
Multiple linear regression (MLR), feed forward neural networks (FFNN) and least square support vector machines (LSSVM) were considered as the black box methods.  ...  The discrete wavelet transform approach also increased the accuracy of multiple linear regression and least squares support vector machines.  ...  Regional Directorate of State Hydraulic Works and Turkish State Meteorological Service for their help with data collection.  ... 
doi:10.2478/johh-2013-0015 fatcat:e7lyopmzb5ewhpwgqpytug6pbq

Standard Precipitation Index Drought Forecasting Using Neural Networks, Wavelet Neural Networks, and Support Vector Regression

A. Belayneh, J. Adamowski
2012 Applied Computational Intelligence and Soft Computing  
The Standard Precipitation Index (SPI) is forecast and compared using artificial neural networks (ANNs), support vector regression (SVR), and wavelet neural networks (WN).  ...  The forecast results indicate that the coupled wavelet neural network (WN) models were the best models for forecasting SPI values over multiple lead times in the Awash River Basin in Ethiopia.  ...  Acknowledgments An NSERC Discovery Grant and a FQRNT New Researcher Grant held by Jan Adamowski were used to fund this research.  ... 
doi:10.1155/2012/794061 fatcat:2mxomuearrbuhfk762fkuoleoy

Reservoir Inflow Prediction under GCM Scenario Downscaled by Wavelet Transform and Support Vector Machine Hybrid Models

Gusfan Halik, Nadjadji Anwar, Budi Santosa, Edijatno
2015 Advances in Civil Engineering  
A new proposed hybrid SD model named Wavelet Support Vector Machine (WSVM) was utilized.  ...  It is a combination of the Multiscale Principal Components Analysis (MSPCA) and nonlinear Support Vector Machine regression. The model was validated at Sutami Reservoir, Indonesia.  ...  The wavelet analysis is an important tool to provide information for both frequency and time domain of the time series data.  ... 
doi:10.1155/2015/515376 fatcat:7qq77kf5tnf4lb7yfw7qn5ucwm

Hybrid Models for Weather Parameter Forecasting

Rashmi Bhardwaj, Varsha Duhoon, Roberto Natella
2021 Complexity  
It is observed that, on the basis of error comparison and time taken by the models, the hybrid wavelet-neuro-RBF model gives better results as compared to the other models due to lower values of determined  ...  The forecasts from these models are further compared on the basis of errors calculated and time taken by the hybrid models and simple models in order to forecast weather parameters.  ...  Acknowledgments e authors are thankful to Guru Gobind Singh Indraprastha University for the research facility and financial support.  ... 
doi:10.1155/2021/6758557 fatcat:j6jbwx4iifbyhkxpwcy6j43bey

Satellite Data and Supervised Learning to Prevent Impact of Drought on Crop Production: Meteorological Drought [chapter]

Leonardo Ornella, Gideon Kruseman, Jose Crossa
2019 Drought (Aridity) [Working Title]  
moving average (ARIMA) models to forecast the weather.  ...  Agricultural indices (AIs) reflecting the soil water conditions that influence crop conditions are costly to monitor in terms of time and resources.  ...  Support vector regression (SVR) and least squares support vector regression (LS-SVR) SVR is based on the Vapnik-Chervonenkis (VC) theory [25] , which characterizes the properties of learning machines  ... 
doi:10.5772/intechopen.85471 fatcat:vymoagwbaffsze22j657h2nsrm

Short-Term Prediction of Air Pollution in Macau Using Support Vector Machines

Chi-Man Vong, Weng-Fai Ip, Pak-kin Wong, Jing-yi Yang
2012 Journal of Control Science and Engineering  
Support vector machines (SVMs), a novel type of machine learning technique based on statistical learning theory, can be used for regression and time series prediction.  ...  SVM is capable of good generalization while the performance of the SVM model is often hinged on the appropriate choice of the kernel.  ...  Support Vector Machines. Support vector machines (SVMs) are known as an excellent tool for classification and regression problems [17] [18] [19] , producing good generalization.  ... 
doi:10.1155/2012/518032 fatcat:lj2lp7iqufcyvmfxsddfuhszw4

Spider Monkey Optimization with Statistical Analysis for Robust Rainfall Prediction

Mahmoud Ragab
2022 Computers Materials & Continua  
In this aspect, this paper presents a novel chaotic spider money optimization with optimal kernel ridge regression (CSMO-OKRR) model for accurate rainfall prediction.  ...  Rainfall prediction becomes popular in real time environment due to the developments of recent technologies.  ...  This paper presents a novel chaotic spider money optimization with optimal kernel ridge regression (CSMO-OKRR) model for accurate rainfall prediction.  ... 
doi:10.32604/cmc.2022.027075 fatcat:2vbdsfkmrrcdpmqtnvqiwccswa

Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada

Jan Adamowski, Hiu Fung Chan, Shiv O. Prasher, Bogdan Ozga-Zielinski, Anna Sliusarieva
2012 Water Resources Research  
Sliusarieva (2012), Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban  ...  Multiple linear regression (MLR), multiple nonlinear regression (MNLR), autoregressive integrated moving average (ARIMA), ANN and WA-ANN models for urban water demand forecasting at lead times of one day  ...  the waveletneural network method with other new methods such as support vector machines with localized multiple kernel learning.  ... 
doi:10.1029/2010wr009945 fatcat:2pjhcudc2fg77ggyfkljcyqbja
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