Input selection and performance optimization of ANN-based streamflow forecasts in the drought-prone Murray Darling Basin region using IIS and MODWT algorithm

Prasad, Ramendra; Deo, Ravinesh C.; Li, Yan; Maraseni, Tek

doi:10.1016/j.atmosres.2017.06.014

Cited by 150 publications

(63 citation statements)

References 90 publications

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“…It is pertinent to also mention that the neurons in hidden layer were recognized by trial and error process. This is approach is an acceptable norm, in accordance with other studies (Deo & Şahin, 2015b(Deo & Şahin, , 2017Deo, Tiwari, Adamowski, & Quilty, 2017;Prasad et al, 2017) for selecting the best model with lowest mean square error in the training dataset.…”

Section: Predictive Model Developmentsupporting

confidence: 80%

“…An extensive model optimization process was also implemented whereby the best neuronal architecture and model weights for optimal feature extraction from the predictor datasets were attained by a trial and error process following earlier work (Deo & Şahin, 2015a). In this paper, we aimed to attain an optimal forecast model by setting the network training parameters accordance to previous research works (Deo & Şahin, 2017;Prasad, Deo, Li, & Maraseni, 2017). Notwithstanding this, a noteworthy point in the experimental design is also that we have used the LM learning procedure (i.e.…”

Section: Predictive Model Developmentmentioning

confidence: 99%

“…Moreover, issues related to the non-stationarities in the climate datasets, applied as the model inputs, can also be addressed in a separate study by testing the present hybrid model with innovative multi-resolution data pre-processing tools that provide better resolved frequencies that are present within the input dataset. In this regard, the development of an integrated hybrid MLP-QPSO model embedded with empirical mode decomposition (EMD) and nondecimated discrete wavelet transform algorithm (Prasad et al, 2017) can help improve the practical relevance of the hybrid MLP-QPSO model. Finally, to enhance the practicality of the approach, a follow-up study, one could also consider utilizing several other test locations (subject to the availability of such data) to validate the proposed MLP-QPSO method.…”

Section: Conclusion and Future Research Workmentioning

confidence: 99%

See 2 more Smart Citations

Forecasting pan evaporation with an integrated artificial neural network quantum-behaved particle swarm optimization model: a case study in Talesh, Northern Iran

Ghorbani

Kazempour

Chau

et al. 2018

Engineering Applications of Computational Fluid Mechanics

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Accurate simulation of evaporation plays an important role in the efficient management of water Resources. Generally, evaporation is measured using the direct method where Class A panevaporimeter is used, and an indirect method that includes empirical equations. However, despite its widespread usage, Class A pan-evaporimeter method can be affected by human and instrumentation errors. Empirical equations, on the other hand, are generally linked to the different climatic factors that should provide initial or boundary conditions in the mathematical equations that affect the rate of evaporation. Considering these challenging, heuristic soft computing approaches that do not need key information about the physics of evaporation. In this study, a Quantum-behaved Particle Swarm Optimization algorithm, embedded into a multi-layer perceptron technique, is developed to estimate the evaporation rates over a daily forecast horizon. The measured evaporation data from 2012-2014 for Talesh meteorological station located in Northern Iran are employed. The predictive accuracy of the MLP-QPSO model is evaluated with existing methods: i.e. a hybrid MLP-PSO and a standalone MLP model. The results are evaluated in respect to statistical performance criterion: the mean absolute error, root mean square error (RMSE), Willmott's Index and the Nash-Sutcliffe coefficient. In conjunction with these metrics, Taylor diagrams are also utilized to assess the level of agreement between the forecasted and observed evaporation data. Evidently, the hybrid MLP-QPSO model is confirmed to be an optimal forecasting tool applied for estimating daily pan evaporation, outperforming both the hybrid MLP-PSO and the standalone model.In light of these results, the present study justifies the potential utility of the hybrid MLP-QPSO model to be applied for estimating daily evaporation rates in North of Iran.

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Section: Predictive Model Developmentsupporting

confidence: 80%

Section: Predictive Model Developmentmentioning

confidence: 99%

Section: Conclusion and Future Research Workmentioning

confidence: 99%

See 1 more Smart Citation

Forecasting pan evaporation with an integrated artificial neural network quantum-behaved particle swarm optimization model: a case study in Talesh, Northern Iran

Ghorbani

Kazempour

Chau

et al. 2018

Engineering Applications of Computational Fluid Mechanics

View full text Add to dashboard Cite

show abstract

“…However, Tantanee, Patamatammakul et al (2005) proposed the hybrid model of wavelet-autoregressive so called WARM which performed more effective for the long lead time. Prasad [184] proposed another similar hybrid model with integration of WNN and iterative input selection (IIS). The hybrid model was called IIS-W-ANN and benchmarked with M5 model Tree.…”

Section: Long-term Flood Prediction Methods Using Single ML Methodsmentioning

confidence: 99%

Flood Prediction Using Machine Learning, Literature Review

Mosavi¹,

Öztürk²,

Kwok-wing³

2018

Preprint

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Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models has been contributing to risk reduction, policy suggestion, minimizing loss of human life and reducing the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods have highly contributed in the advancement of prediction systems providing better performance and cost effective solutions. Due to the vast benefits and potential of ML, its popularity has dramatically increased among hydrologists. Researchers through introducing the novel ML methods and hybridization of the existing ones have been aiming at discovering more accurate and efficient prediction models. The main contribution is to demonstrate the state of the art of ML models in flood prediction and give an insight over the most suitable models. The literature where ML models are benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed have been particularly investigated to provide an extensive overview on various ML algorithms usage in the field. The performance comparison of ML models presents an in-depth understanding about the different techniques within the framework of a comprehensive evaluation and discussion. As the result, the paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported the most effective strategy in improvement of the ML methods. This survey can be used as a guideline for the hydrologists as well as climate scientists to assist them choosing the proper ML method according to the prediction task conclusions.

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“…Ghosh et al used MODWT to decompose the time series of the individual exchange rates and applied the random forest and bagging method on the decomposed components to model the prediction[24]. He et al combined the MODWT method and mixed wavelet neural network (WNN) architecture to develop a WNN-M prediction model through a data-driven approach[25].Prasad et al used MODWT to resolve the frequencies contained in predictor data while constructing a wavelet-hybrid model for the forecasting of streamflow[26].…”

mentioning

confidence: 99%

Modelling the Publishing Process of Big Location Data Using Deep Learning Prediction Methods

et al. 2020

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Centralized publishing of big location data can provide accurate and timely information to assist in traffic management and for facilitating people to decide travel time and route, mitigate traffic congestion, and reduce unnecessary waste. However, the spatio-temporal correlation, non-linearity, randomness, and uncertainty of big location data make it impossible to decide an optimal data publishing instance through traditional methods. This paper, accordingly, proposes a publishing interval predicting method for centralized publication of big location data based on the promising paradigm of deep learning. First, the adaptive adjusted sampling method is designed to address the challenge of finding a reasonable release time via a prediction mechanism. Second, the Maximal Overlap Discrete Wavelet Transform (MODWT) is introduced for the decomposition of time series in order to separate different features of big location data. Finally, different deep learning models are selected to construct the entire framework according to various time-domain features. Experimental analysis suggests that the proposed prediction scheme is not only feasible, but also improves the prediction accuracy in contrast to the traditional deep learning mechanisms.

show abstract

Input selection and performance optimization of ANN-based streamflow forecasts in the drought-prone Murray Darling Basin region using IIS and MODWT algorithm

Cited by 150 publications

References 90 publications

Forecasting pan evaporation with an integrated artificial neural network quantum-behaved particle swarm optimization model: a case study in Talesh, Northern Iran

Forecasting pan evaporation with an integrated artificial neural network quantum-behaved particle swarm optimization model: a case study in Talesh, Northern Iran

Flood Prediction Using Machine Learning, Literature Review

Modelling the Publishing Process of Big Location Data Using Deep Learning Prediction Methods

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