A hybrid wavelet analysis and adaptive neuro-fuzzy inference system for drought forecasting

Shabri, Ani

doi:10.12988/ams.2014.48263

Cited by 13 publications

(8 citation statements)

References 16 publications

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“…On the other hand, the ANFIS uses the training capability of ANN to assign and adjust the membership functions. The back-propagation algorithm enables the model to adjust the parameters until an acceptable error is reached [25]. Suppose that the system of fuzzy inference include x & y as inputs and z as output.…”

Section: Anfis: Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

Prediction of Hydropower Generation Using Grey Wolf Optimization Adaptive Neuro-Fuzzy Inference System

et al. 2019

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Hydropower is among the cleanest sources of energy. However, the rate of hydropower generation is profoundly affected by the inflow to the dam reservoirs. In this study, the Grey wolf optimization (GWO) method coupled with an adaptive neuro-fuzzy inference system (ANFIS) to forecast the hydropower generation. For this purpose, the Dez basin average of rainfall was calculated using Thiessen polygons. Twenty input combinations, including the inflow to the dam, the rainfall and the hydropower in the previous months were used, while the output in all the scenarios was one month of hydropower generation. Then, the coupled model was used to forecast the hydropower generation. Results indicated that the method was promising. GWO-ANFIS was capable of predicting the hydropower generation satisfactorily, while the ANFIS failed in nine input-output combinations.

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Section: Anfis: Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

Prediction of Hydropower Generation Using Grey Wolf Optimization Adaptive Neuro-Fuzzy Inference System

et al. 2019

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“…An automatic procedure for the optimization of membership function features and adjusting parameters is needed [79], [81], [82]. ANFIS, which is the integration of fuzzy logic and artificial neural network (ANN) is used to overcome this problem [83], [84]. Generally speaking, ANFIS is a multi-layer feed-forward network based on the ANN learning capabilities and fuzzy thinking [79], [85]- [87].…”

Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

Improvements in the Explicit Estimation of Pollutant Dispersion Coefficient in Rivers by Subset Selection of Maximum Dissimilarity Hybridized With ANFIS-Firefly Algorithm (FFA)

et al. 2020

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In this paper, a new hybrid model is proposed using Subset Selection by Maximum Dissimilarity (SSMD) and adaptive neuro-fuzzy inference system (ANFIS) hybridized with the firefly algorithm (FFA) to predict the longitudinal dispersion coefficient (K x). The proposed framework (ANFIS-FFA), combines the specific structures and strengths of both ANFIS and FFA approaches. The FFA is used to derive the optimum ANFIS parameters. The K x data set includes 503 cross-sectional data point from small to large rivers. For pre-processing of the data set, the SSMD method is used, which is superior to the classical trial and error method. The database covers a wide range of river width (0.2-867m), and depths (0.034-19.9 m). Fifteen different combinations of river width (B), depth (H), flow velocity (U) and shear velocity (U *) are implemented as inputs to create fifteen estimative models. The output of the ANFIS-FFA model is compared with the ANFIS and previously published equations to check the performance of the proposed model. The results show that the highest accuracy is attained by the M1 model, with all geometric and hydrodynamic parameters as input variables in comparison with ANFIS and previous equations. The R 2 value, RMSE, MAE and NSE for ANFIS-FFA model are 0.67, 113.14 m 2 /s, 48 m 2 /s, and 0.63 for proposed dimensional model, and 0.35, 874.5, 520.8, and 0.1 in non-dimensional ANFIS-FFA model, respectively. These values were 0.37, 463.34 m 2 /s, 85.69 m 2 /s, and −5.19 for dimensional ANFIS model, and 0.11, 3269.88, 1932.09 and −11.54 for non-dimensional ANFIS model, respectively. Overall, hybridization caused 81%, 75%, 76% improvement in R 2 , RMSE and MAE. In another contribution of the paper, by using the matrix form of developed ANFIS-FFA optimized parameters, a novel explicit calculation procedure for estimation of K x is derived. Based on the results, the proposed ANFIS-FFA model exhibits significant improvements than the classical ANFIS and highlights that optimizing by nature-inspired optimization algorithms plays a critical role in strengthening the ANFIS estimations generality. INDEX TERMS Longitudinal dispersion coefficient, ANFIS-FFA, maximum dissimilarity method, natural rivers, adaptive neuro-fuzzy inference system.

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“…Wavelet is a mathematical procedure that involves the transformation of the original signal (most especially in the time domain) into a different domain in processing and in the analysis [7]. [8], in his study of hybrid wavelet and adaptive neuro-fuzzy inference system for drought forecasting stated that wavelet analysis is one of the most powerful tools to study time series. In another study, [9], described wavelet analysis as a multi-decomposition analysis that provide information for time and frequency domains and provide useful decompositions of the original time series for the wavelet-transformed data to improve the power of a forecasting model.…”

Section: Discrete Wavelet Transform (Dwt)mentioning

confidence: 99%

Drought Forecasting using Wavelet-GMDH Model with Standardized Precipitation Index

Alfa*¹,

Shabri²,

Shaari³

2019

IJRTE

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This paper proposes Wavelet-Group Methods of Data Handling (W-GMDH) model to explore its ability of drought forecasting. The W-GMDH model was developed by combining Discrete Wavelet Transform (DWT) and GMDH model using the Standardized Precipitation Index (SPI) drought data for forecasting to assess the effectiveness of the new (W-GMDH) model. These methods were used on four SPI data sets (SPI3, SPI6, SPI9 and SPI12). To achieve this, a 624 month of SPI data from January 1956 to December 2008 was used and divided into two parts (80% for training and 20% for testing). The results of the W-GMDH model were then compared with the conventional GMDH model using Root Mean Square Error (RMSE), Mean Average Error (MAE) and coefficient of correlation as the performance evaluation measures. Both results of the proposed W-GMDH model and the GMDH showed very clearly that the propose method can achieve the best forecasting performance in terms of accuracy for each of the SPI data series. The key role played by the DWT is to smooth the analysis of SPI data obtained after the wavelet decomposition which is also used to decompose the SPI data into different number of component series to minimize the forecasting error. In all the results computed, the proposed model has a minimum error indicating its superiority over the GMDH model. This indicates that W-GMDH model’s performance has outweighed that of the conventional GMDH model in SPI drought forecasting. The research contributes to the discovering of viable forecasting of drought and demonstrates that the established model is good and appropriate for drought. In all the analysis W-GMDH model has the minimum error. The overall results showed that SPI12 has the minimum error among all the SPI data considered.

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A hybrid wavelet analysis and adaptive neuro-fuzzy inference system for drought forecasting

Cited by 13 publications

References 16 publications

Prediction of Hydropower Generation Using Grey Wolf Optimization Adaptive Neuro-Fuzzy Inference System

Prediction of Hydropower Generation Using Grey Wolf Optimization Adaptive Neuro-Fuzzy Inference System

Improvements in the Explicit Estimation of Pollutant Dispersion Coefficient in Rivers by Subset Selection of Maximum Dissimilarity Hybridized With ANFIS-Firefly Algorithm (FFA)

Drought Forecasting using Wavelet-GMDH Model with Standardized Precipitation Index

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