Journal ArticleDOI
A Novel Method to Water Level Prediction using RBF and FFA
Seyed Ahmad Soleymani,Shidrokh Goudarzi,Mohammad Hossein Anisi,Wan Haslina Hassan,Mohd Yamani Idna Idris,Shahaboddin Shamshirband,Noorzaily Mohamed Noor,Ismail Ahmedy +7 more
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TLDR
The results specify that the developed RBF–FFA model can be used as an efficient technique for accurate prediction of water stage of river.Abstract:
Water level prediction of rivers, especially in flood prone countries, can be helpful to reduce losses from flooding. A precise prediction method can issue a forewarning of the impending flood, to implement early evacuation measures, for residents near the river, when is required. To this end, we design a new method to predict water level of river. This approach relies on a novel method for prediction of water level named as RBF-FFA that is designed by utilizing firefly algorithm (FFA) to train the radial basis function (RBF) and (FFA) is used to interpolation RBF to predict the best solution. The predictions accuracy of the proposed RBF–FFA model is validated compared to those of support vector machine (SVM) and multilayer perceptron (MLP) models. In order to assess the models’ performance, we measured the coefficient of determination (R2), correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE). The achieved results show that the developed RBF–FFA model provides more precise predictions compared to different ANNs, namely support vector machine (SVM) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real time water stage measurements. The results specify that the developed RBF–FFA model can be used as an efficient technique for accurate prediction of water stage of river.read more
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Journal ArticleDOI
Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model
Zaher Mundher Yaseen,Zaher Mundher Yaseen,Isa Ebtehaj,Hossein Bonakdari,Ravinesh C. Deo,Ali Danandeh Mehr,Wan Hanna Melini Wan Mohtar,Lamine Diop,Lamine Diop,Ahmed El-Shafie,Vijay P. Singh +10 more
TL;DR: In this paper, a hybrid adaptive Neuro-Fuzzy Inference Systems (ANFIS) approach was proposed for monthly streamflow forecasting. But the results of the ANFIS-FFA model are compared with the classical ANFis model, which utilizes the fuzzy c-means (FCM) clustering method in the Fuzzy inference system (FIS).
Journal ArticleDOI
Pan evaporation prediction using a hybrid multilayer perceptron-firefly algorithm (MLP-FFA) model: case study in North Iran
Mohammad Ali Ghorbani,Mohammad Ali Ghorbani,Ravinesh C. Deo,Zaher Mundher Yaseen,Zaher Mundher Yaseen,Mahsa H. Kashani,Babak Mohammadi +6 more
TL;DR: The results show that an optimal MLP-FFA model outperforms the MLP and SVM model for both tested stations, and demonstrate the importance of the Firefly Algorithm applied to improve the performance of theMLP- FFA model, as verified through its better predictive performance compared to the MLp and S VM model.
Journal ArticleDOI
Multi-layer perceptron hybrid model integrated with the firefly optimizer algorithm for windspeed prediction of target site using a limited set of neighboring reference station data
Ravinesh C. Deo,Mohammad Ali Ghorbani,Mohammad Ali Ghorbani,Saeed Samadianfard,Tek Narayan Maraseni,Mehmet Bilgili,Mustafa Biazar +6 more
TL;DR: In this paper, a multilayer perceptron (MLP) hybrid model integrated with the Firefly Optimizer algorithm was used to predict wind speed at target sites in north-west Iran using a limited set of historical (monthly) data (2004-2014) for a group of neighboring stations.
Journal ArticleDOI
Implementation of a hybrid MLP-FFA model for water level prediction of Lake Egirdir, Turkey
Mohammad Ali Ghorbani,Mohammad Ali Ghorbani,Ravinesh C. Deo,Vahid Karimi,Zaher Mundher Yaseen,Zaher Mundher Yaseen,Özlem Terzi +6 more
TL;DR: In this article, a hybrid model integrating the Firefly Algorithm (FFA), as a heuristic optimization tool with the multilayer perceptron (MLP-FFA) algorithm for the prediction of water level in Lake Egirdir, Turkey, is investigated.
Journal ArticleDOI
Machine Learning Application in Reservoir Water Level Forecasting for Sustainable Hydropower Generation Strategy
TL;DR: In this paper, four supervised machine learning algorithms for both scenarios were proposed such as Boosted Decision Tree Regression (BDTR), decision forest regression (DFR), Bayesian linear regression (BLR), and neural network regression (NNR) for predicting the changes in water level of a reservoir located in Malaysia with two different scenarios; Scenario 1 (SC1) includes rainfall and water level as input and Scenario 2 (SC2) including rainfall, water level, and sent out.
References
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Book
Linear statistical inference and its applications
TL;DR: Algebra of Vectors and Matrices, Probability Theory, Tools and Techniques, and Continuous Probability Models.
Book
Nature-Inspired Metaheuristic Algorithms
TL;DR: This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including genetic algorithms, bee algorithms, particle swarm optimization, simulated annealing, ant colony optimization, harmony search, and firefly algorithms.
Journal ArticleDOI
Firefly algorithm, stochastic test functions and design optimisation
TL;DR: This paper shows how to use the recently developed firefly algorithm to solve non-linear design problems and proposes a few new test functions with either singularity or stochastic components but with known global optimality and thus they can be used to validate new optimisation algorithms.
Posted Content
Firefly Algorithm, Stochastic Test Functions and Design Optimisation
TL;DR: In this article, the authors used the Firefly Algorithm to solve nonlinear design problems and showed that the optimal solution found by FA is far better than the best solution obtained previously in literature.
Journal ArticleDOI
Linear Statistical Inference and its Applications
J. Aitchison,C. Radhakrishna Rao +1 more
TL;DR: Causal inference in statistics: An overview Linear Statistical Inference And Its Bayesian inference Wikipedia Springer Series in Statistics Stanford University Statistical Modeling, Causal Inference, and Social Science.
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