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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).  ...  Wavelet analysis is used for pre-processed the neural network input. The time series hydrological data was decomposed into sub series by wavelet analysis.  ... 
doi:10.5120/16003-4991 fatcat:r4d2vpehofeezpelbb2hvljsai

Wavelet-based multi-resolution analysis and artificial neural networks for forecasting temperature and thermal power consumption

Julien Eynard, Stéphane Grieu, Monique Polit
2011 Engineering applications of artificial intelligence  
The proposed short-term forecast method is based on the concept of time series and uses both a wavelet-based multi-resolution analysis and multi-layer artificial neural networks.  ...  topology of the neural networks used.  ...  Substituting the prediction task of an original time series of high variability by the prediction, using MLP neural networks (many other neural networks have been tried but no significant improvement of  ... 
doi:10.1016/j.engappai.2010.09.003 fatcat:vjbnsnq5bzgnnp5alvurlisamm

Comparative Study between Wavelet Artificial Neural Network (WANN) and Artificial Neural Network (ANN) Models for Groundwater Level Forecasting

Ananda Kumar, B Maheshwara Babu, U Satish Kumar, G.V Srinivasa Reddy
2019 Indian Journal of Agricultural Research  
Groundwater level fluctuation modeling is a prime need for effective utilization and planning the conjunctive use in any basin.The application of Artificial Neural Network (ANN) and hybrid Wavelet ANN  ...  (WANN) models was investigated in predicting Groundwater level fluctuations.  ...  Both evapotranspiration and water table depth time series were decomposed at same level of decomposition Comparative Study between Wavelet Artificial Neural Network (WANN) and Artificial Neural Network  ... 
doi:10.18805/ijare.a-5079 fatcat:uwokoruslfc7dblcic5v3rtxva

Review of Applications of Neuro-Wavelet Techniques in Water Flows

Pradnya Dixit, Shreenivas Londhe, M. C. Deo
2016 INAE Letters  
The wavelet neural network or neuro-wavelet transform (NWT) is a recent effort in this regard to accurately model or predict different types of parameters characterizing river or ocean processes.  ...  The latter coupled with progress in neuro-science had given rise to the method of artificial neural networks (ANNs) which has been applied abundantly since late 1980s to carry out system modeling and prediction  ...  J Eng Mech 127:58-70 Arena F, Puca S (2004) The reconstruction of significant wave height time series by using a neural network approach. J Offshore Mech Arct Eng 126:213-Match  ... 
doi:10.1007/s41403-016-0015-3 fatcat:zuoqebinz5dtdo6oxiegv75adm

A Review on Flood Prediction Algorithms and A Deep Neural Network Model for Estimation of Flood Occurrence

Tabassum Farhana Ullah, Gnana Prakasi O.S., Kanmani P
2020 International Research Journal of Multidisciplinary Technovation  
In this paper, we classify and analyzed the various prediction algorithms which show usage of Deep Neural Network produces better results.  ...  The occurrence and damages caused by flood is very high. Major cause of flood is due to heavy rainfall which in turn increases the water level of the rivers and other water bodies.  ...  [15] used wavelet neural networks in which water flux and water level are used as an input for which the time frequency feature was analysed.  ... 
doi:10.34256/irjmt2052 fatcat:lm5gwa7pujeb3dpgwzpump3334

Comparative Study of the Three Models (ANN-PMC), (DWT-ANN-PMC) and (MLR) for Prediction of the Groundwater Level of the Surface Water Table in the Saïss Plain (North of Morocco)

Abdelhamid Ibrahimi, Abdennasser Baali, Amine Couscous, Touria Kamel, Nadia Hamdani
2017 International Journal of Intelligent Engineering and Systems  
A new method based on the coupling of discrete wavelets (DWT) and artificial neural networks with perceptron multilayers (ANN-PMC) is proposed to predict the groundwater level.  ...  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 Saïss Plain.  ...  [10] used feed-forward and RBF networks neural networks, with Levenberg-Marquardt training algorithms and Bayesian regularization, to predict water levels one month in advance in six wells installed  ... 
doi:10.22266/ijies2017.1031.24 fatcat:fseiewjiyjgjnkvpkelzdwxeau

Monthly Stream Flow Predition in Pungwe River for Small Hydropower Plant Using Wavelet Method

Miguel Meque Uamusse
2015 International Journal of Energy and Power Engineering  
Eight different single-stepahead monthly stream flow neural prediction models were developed. Coupled simulation involving use of MATLAB and of a Wavelet-Neural Network was employed.  ...  The effects of a discrete wavelet-transformation data-preprocessing method on neural-network-based monthly streamflow prediction models in producing energy from small hydro power plants in the Pungwe River  ...  Acknowledgements The authors would like to thank the Department of Water Resources at Lund University and the Department of Chemistry Engineering at Eduardo Mondlane University for their support in my  ... 
doi:10.11648/j.ijepe.20150405.17 fatcat:iti62i5cjbdptjh3rmby3pch3y

A Hybrid-Wavelet Artificial Neural Network Model for Monthly Water Table Depth Prediction

, Anandakumar, A. R. Senthil Kumar, Ravindra Kale, B. Maheshwara Babu, U. Sathishkumar, G. V. Srinivasa Reddy, Prasad S. Kulkarni
2019 Current Science  
Rainfall time series was decomposed using Haar wavelet at third decomposition level and evapotranspiration and water table depth time series was decomposed using Daubechies wavelet at second decomposition  ...  WANN model was developed using decomposed signals of rainfall, evapotranspiration and water table depth time series as inputs in the ANN model to arrive at a prediction of monthly fluctuation of the groundwater  ...  ., Groundwater level forecasting in a shallow aquifer using artificial neural network approach. Water Resour. Manage., 2006, 20, 77-90. 3. Krishna, B., Satyaji Rao, Y.  ... 
doi:10.18520/cs/v117/i9/1475-1481 fatcat:ybdedukctveclgsfcsugty5mm4

Urban Water Flow and Water Level Prediction Based on Deep Learning [chapter]

Haytham Assem, Salem Ghariba, Gabor Makrai, Paul Johnston, Laurence Gill, Francesco Pilla
2017 Lecture Notes in Computer Science  
In addition, it can be used for capturing abnormalities by setting and comparing thresholds to the predicted water flow and water level.  ...  based on deep convolutional neural networks (DeepCNNs).  ...  Deep Convolutional Neural Networks In order to design an effective forecasting model for predicting water flow and water level across several years, we needed to exploit the time series nature of the data  ... 
doi:10.1007/978-3-319-71273-4_26 fatcat:igrq2hcvwnglxoiftqcytstzju

Wavelet neural network model for reservoir inflow prediction

U. Okkan
2012 Scientia Iranica. International Journal of Science and Technology  
Then, effective sub-time series components have been used as the new inputs of neural networks. DWT has been also integrated with multiple linear regressions (WREG) within the study.  ...  The results of Wavelet Neural Network (WNN) model and WREG have been compared with conventional Feed Forward Neural Networks (FFNN) and multiple linear regression (REG) models.  ...  Dalkilic (from Dokuz Eylul University, Ph.D.) and two anonymous reviewers for their valuable contribution to grammar correction of this study as well as workers of II.  ... 
doi:10.1016/j.scient.2012.10.009 fatcat:qjxx7c4mbnesdlyooykt3fvgqi

A Promising Wavelet Decomposition –NNARX Model to Predict Flood

Mohd Azrol Syafiee Anuar, Ribhan Zafira Abdul Rahman, Azura Che Soh, Samsul Bahari Mohd Noor, Zed Diyana Zulkafli
2020 International journal of electrical and computer engineering systems  
This research used the neural network autoregressive exogenous input (NNARX) model to predict floods. One of the research challenges was to develop accurate models and improve the forecasting model.  ...  This research aimed to improve the performance of the neural network model for flood prediction.  ...  In this study, MATLAB software was used to develop a neural network water level prediction system.  ... 
doi:10.32985/ijeces.11.2.4 fatcat:azhhecp47vd2be53tqwa57lhh4

Hybrid Deep Learning Modeling for Water Level Prediction in Yangtze River

Zhaoqing Xie, Qing Liu, Yulian Cao
2021 Intelligent Automation and Soft Computing  
The wavelet transform is applied to decompose time series into details and approximation components for a better understanding of temporal properties, and a novel LSTM network is used to learn generic  ...  In this research, a deep learning approach called long short-term memory network combined with discrete wavelet transform (WA-LSTM) is proposed for daily water level prediction.  ...  Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the present study.  ... 
doi:10.32604/iasc.2021.016246 fatcat:egzibekjjfcb7d5ljff5djnr2m

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  
The wide use of hydrological time series data has initiated a great deal of research and development attempts in the field of data mining.  ...  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  ...  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


A. G. Abubakar, M. R. Mahmud, K. K. W. Tang, A. Hussaini, N. H. Md Yusuf
2019 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
Therefore, conventional harmonic analysis alone does not adequately predict the coastal water level variation, in order to deal with these situations and provide predictions with the desired accuracy,  ...  However, harmonic analysis required a large number of parameters and long-term tidal measured for precise tidal level predictions.  ...  The author would like to thank the Ministry of Education and Universiti Teknologi Malaysia for funding this research under Research University Grant (Vote number: Q.J130000.2527.12H11).  ... 
doi:10.5194/isprs-archives-xlii-4-w16-23-2019 fatcat:a4vsbth4vna53phb6w3tsnzhey

A Wavelet Neural Network Hybrid Model for Monthly Ammonia Forecasting in River Water

Yi Wang, Yuanyuan Wang, Liang Guo, Ying Zhao, Zhichao Zhang, Peng Wang
2013 Research Journal of Applied Sciences Engineering and Technology  
This study presents a combined Wavelet transform (WA) and Artificial Neural Network (ANN) model for monthly ammonia nitrogen series prediction in river water.  ...  The WA decomposed original time series into different subseries, in which the most significant one was chosen as the training data instead of the original series.  ...  ACKNOWLEDGMENT This study was supported by the Funds NSFC for Creative Research Groups of China (No. 51121062) and National Natural Science Foundation of China (No. 71203041).  ... 
doi:10.19026/rjaset.6.4084 fatcat:rmpbsqjnwjbj7ao3bpmcpf52j4
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