Wavelet Artificial Neural Network Model Research Articles

Application of wavelet and seasonal-based emotional ANN (EANN) models to predict solar irradiance

This study models solar irradiance at six stations in Iran and the USA on an hourly scale. We explored two seasonal emotional artificial neural networks (EANN): sequence-EANN (SEANN) and wavelet EANN (WEANN). Analyzing ten years of climatic and solar data, we evaluated uncertainty using prediction intervals (PIs) computed via the bootstrap method based on artificial neural networks (ANNs). Unlike standalone EANNs, the proposed seasonal models effectively captured seasonal information and leveraged time series processing advantages. Utilizing Wavelet and Fourier transforms, these models captured long-short autoregressive dependencies in solar irradiance, addressing extended seasonal dependencies. Results showed that the seasonal EANN models outperformed the classic EANN model by approximately 15 % and the classic feed-forward neural network (FFNN) by about 25 % in both training and testing. The WEANN model demonstrated the highest performance in PIs, with an average normalized mean PI width (NMPIW) of 0.8 and an average PI coverage probability (PICP) of 0.96.

Developing a Hybrid Wavelet-Artificial Neural Network model for simulating high-resolution Antarctic ice core CO2 concentration records during 9–120 thousand years ago

Developing a Hybrid Wavelet-Artificial Neural Network model for simulating high-resolution Antarctic ice core CO₂ concentration records during 9–120 thousand years ago

Enhancing short-term streamflow forecasting of extreme events: A wavelet-artificial neural network hybrid approach

Accurate short-term streamflow forecasting models are crucial for effective water resource management, enabling timely responses to extreme flood or drought events and mitigating potential socioeconomic damage. This study proposes robust hybrid Wavelet Artificial Neural Network (WANN) models for real-world hydrological applications. Two WANN variants, WANNone and WANNmulti, are proposed for short-term streamflow forecasting of extreme (high and low) flows at eight gauging stations within Brazil's Paraíba do Sul River basin. WANNone directly feeds both the original streamflow data and the decomposed components obtained through an À Trous wavelet transform into the ANN architecture. Conversely, WANNmulti utilizes separate ANNs for the original data, with the final streamflow estimate reconstructed via the inverse wavelet transform of the individual ANN outputs. The performance of these WANN models is then compared against conventional ANN models. In both approaches, Bayesian optimization is employed to fine-tune the hyperparameters within the ANN architecture. The WANN models achieved superior performance for 7-day streamflow forecasts compared to conventional ANN models. WANN models yielded high R2 values (>0.9) and low MAPE (4.8%–14.7%) within the expected RMSE range, demonstrating statistically significant improvements over ANN models (71% and 75% reduction in RMSE and MAPE, respectively, and 69% increase in R2). Further analysis revealed that WANNmulti models generally exhibited superior performance for low extreme flow predictions, while WANNone models achieved the highest accuracy for high extreme flows at most stations. WANN models' strong performance suggests their value for real-time flood warnings, enabling improved decision-making in areas like flood/drought mitigation and urban water planning.

Novel Ocean Wave Height and Energy Spectrum Forecasting Approaches: An Application of Semi-Analytical and Machine Learning Models

Accurate and reliable wave forecasting is crucial for optimizing the performance of various marine operations, such as offshore energy production, shipping, and fishing. Meanwhile, predicting wave height and wave energy is crucial for achieving sustainability as a renewable energy source, as it enables the harnessing of the power of wave energy efficiently based on the water-energy nexus. Advanced wave forecasting models, such as machine learning models and the semi-analytical approach, have been developed to provide more accurate predictions of ocean waves. In this study, the Sverdrup Munk Bretschneider (SMB) semi-analytical approach, Emotional Artificial Neural Network (EANN) approach, and Wavelet Artificial Neural Network (WANN) approach will be used to estimate ocean wave parameters in the Gulf of Mexico and Aleutian Basin. The accuracy and reliability of these approaches will be evaluated, and the spatial and temporal variability of the wave field will be investigated. The available wave characteristics are used to generate hourly, 12-hourly, and daily datasets. The WANN and SMB model shows good performance in the daily prediction of the significant wave height in both case studies. In the SMB model, specifically on a daily time scale, the Nash–Sutcliffe Efficiency (NSE) and the peak deviation coefficient (DCpeak) were determined to be 0.62 and 0.54 for the Aleutian buoy and 0.64 and 0.55 for the Gulf of Mexico buoy, respectively, for significant wave height. In the context of the WANN model and in the testing phase at the daily time scale, the NSE and DCpeak indices exhibit values of 0.85 and 0.61 for the Aleutian buoy and 0.72 and 0.61 for the Gulf of Mexico buoy, respectively, while the EANN model is a strong tool in hourly wave height prediction (Aleutian buoy (NSEEANN = 0.60 and DCpeakEANN = 0.88), Gulf of Mexico buoy (NSEEANN = 0.80 and DCpeakEANN = 0.82)). In addition, the findings pertaining to the energy spectrum density demonstrate that the EANN model exhibits superior performance in comparison to the WANN and SMB models, particularly with regard to accurately estimating the peak of the spectrum (Aleutian buoy (DCpeakEANN= 0.41), Gulf of Mexico buoy (DCpeakEANN = 0.59)).

Predictive uncertainty assessment in flood forecasting using quantile regression

Abstract Floods and their associated impacts are topics of concern in land development planning and management, which call for efficient flood forecasting and warning systems. The performance of flood warning systems is affected by uncertainty in water level forecasts, which is due to their inability to measure or calculate a modeled value accurately. Predictive uncertainty is an emerging type of uncertainty modeling technique that emphasizes total uncertainty quantified as a probability distribution conditioned on all available knowledge. Predictive uncertainty analysis was done using quantile regression (QR) for machine learning-based flood models – Hybrid Wavelet Artificial Neural Network model (WANN) and Hybrid Wavelet Support Vector Machine model (WSVM) for different lead times. Comparing QR models of WANN and WSVM revealed that the slope, intercept, spread of forecast, and width of confidence band of the WANN model are more for each quantile indicating more uncertainty as compared to the WSVM model. In both models, with an increase in lead time, uncertainty has shown an increasing trend as well. The performance evaluation of inference obtained from QR models was evaluated using uncertainty statistics such as prediction interval coverage probability, average relative interval length (ARIL), and mean prediction interval (MPI).

Investigation of Impact of Vapor Pressure on Hybrid Streamflow Prediction Modeling

In this study, daily streamflow prediction models have been developed for Aksu Stream, in the Eastern Black Sea Basin of Turkey. To reach at this aim, hybrid artificial intelligence models have been developed, by using a new parameter, vapor pressure. Vapor pressure efficiency has been investigated for hybrid streamflow prediction models. Streamflow prediction models have been developed by using Artificial Neural Network (ANN), Multivariate Adaptive Regression Splines (MARS), and their hybrid models. Hybridization of streamflow prediction models has been made with Wavelet Transform (WT). 10 yearly daily hydrological (discharge (m3/s)), meteorological (precipitation (mm), vapor pressure (hPA)) data, and seasonality coefficient have been used as input data of streamflow prediction models. In the selection of the best streamflow prediction model, 14 different day-delayed input combinations have been established by using 10 yearly data. As a result of the study, the highest flow forecast performance model has been determined as Wavelet Artificial Neural Network (WANN) in the study area. In the WANN model, the vapor pressure parameter was found to reduce the error by about 18.5% and improve the forecast performance. This study has concluded that, vapor pressure may be used in the future studies as a new parameter for streamflow prediction models.

How does economic policy uncertainty respond to the global oil price fluctuations? Evidence from BRICS countries

In this paper, we explore the effect of global crude oil price on the economic policy uncertainty (EPU) in BRICS countries. For this purpose, we have adopted the hybrid wavelet artificial neural network model (wavelet-ANN) and the threshold vector auto-regression model (TVAR) to decompose the global crude oil price series into different market participants' horizons and to examine its comprehensive and detailed impact on different underlying oil price change regimes. The empirical results are as follows: Firstly, we find that oil price has heterogeneous effects on EPU in different BRICS countries. In contrast, the EPU of oil importers among BRICS countries (China and India) significantly negatively affect global oil price in the long run. Secondly, in each country, the influence of oil price on EPU shows a nonlinear and asymmetric feature. For China, a significant effect exists in the high-fluctuation regime in the short run. Moreover, the significant negative response of Brazil's EPU to oil prices also occurred in the high-fluctuation regime. Regarding Russia, the significant negative effect occurs in the low-fluctuation regime. Similar results can also be found in India. In the long run, only in India, the fluctuation of global crude oil price has a significant positive impact on India's EPU.

A Hybrid ARFIMA Wavelet Artificial Neural Network Model for DJIA Index Forecasting

This paper proposes a hybrid modelling approach for forecasting returns and volatilities of the stock market. The model, called ARFIMA-WLLWNN model, integrates the advantages of the ARFIMA model, the wavelet decomposition technique (namely, the discrete MODWT with Daubechies least asymmetric wavelet filter) and artificial neural network (namely, the LLWNN neural network). The model develops through a two-phase approach. In phase one, a wavelet decomposition improves the forecasting accuracy of the LLWNN neural network, resulting in the Wavelet Local Linear Wavelet Neural Network (WLLWNN) model. The Back Propagation and Particle Swarm Optimization (PSO) learning algorithms optimize the WLLWNN structure. In phase two, the residuals of an ARFIMA model of the conditional mean become the input to the WLLWNN model. The hybrid ARFIMA-WLLWNN model is evaluated using daily returns of the Dow Jones Industrial Average index over 01/05/2010 to 02/11/2020. The experimental results indicate that the PSO-optimized version of the hybrid ARFIMA-WLLWNN outperforms the LLWNN, WLLWNN, ARFIMA-LLWNN, and the ARFIMA-HYAPARCH models and provides more accurate out-of-sample forecasts over validation horizons of one, five and twenty-two days.

Wavelet Decomposition and Machine Learning Technique for Predicting Occurrence of Spiders in Pigeon Pea

Influence of weather variables on occurrence of spiders in pigeon pea across locations of seven agro-climatic zones of India was studied in addition to development of forecast models with their comparisons on performance. Considering the non-normal and nonlinear nature of time series data of spiders, non-parametric techniques were applied with developed algorithm based on combinations of wavelet–regression and wavelet–artificial neural network (ANN) models. Haar wavelet filter decomposed each of the series to extract the actual signal from the noisy data. Prediction accuracy of developed models, viz., multiple regression, wavelet–regression, and wavelet–ANN, tested using root mean square error (RMSE) and mean absolute percentage error (MAPE), indicated better performance of wavelet–ANN model. Diebold Mariano (DM) test also confirmed that the prediction accuracy of wavelet–ANN model, and hence its use to forecast spiders in conjunction with the values of pest–defender ratios, would not only reduce insecticidal sprays, but also add ecological and economic value to the integrated pest management of insects of pigeon pea.

Spatio-temporal variations of land surface temperature and precipitation due to climate change in the Jhelum river basin, India

The present study investigated the spatio-temporal variations of precipitation and temperature for the projected period (2011-2100) in the Jhelum basin, India. The precipitation and temperature variables are projected under RCP 8.5 scenario using statistical down scaling techniques such as Artificial Neural Network (ANN) and Wavelet Artificial Neural Network (WANN) models. Firstly, the screened predictors were downscaled to predictand using ANN and WANN models for all the study stations. On the basis of the performance criteria, the WANN model is selected as an efficient model for downscaling of precipitation and temperature. The future screened predictor data pertaining to RCP 8.5 of CanESM2 model were downscaled to monthly temperature and precipitation for future periods (2011-2100) using WANN models. The investigation of the future projections revealed an average increase of 17-25% in the mean annual precipitation and 20-25% average increase in the monthly mean precipitation for all the selected stations towards the end of 21st century. The monthly mean temperature also showed an increase of 2-3 °C for all the study stations towards the end of 21st century. The mean seasonal temperature of the projected period is found to be increasing for all the four seasons in most parts of the basin.

Wavelet neural network modeling for the retention efficiency of sub-15 nm nanoparticles in ultrafiltration under small particle to pore diameter ratio

Ultrafiltration (UF) using membranes with a small ratio of particle to pore diameter (PPD) would be very desirable for energy saving. The nanoparticle (NP) retention efficiency of membranes with a small PPD ratio depends on various physical and chemical properties of NPs, membranes and solutions as well as the filtration conditions. Until now, no simple model is available for the calculation of NP retention efficiency in UF membranes, besides, it is unlikely to conduct experiments covering all conditions for obtaining the efficiency. The artificial neural network (ANN) has been attracting much attention for studying the performance of a highly nonlinear system. In this study, a wavelet ANN model was developed to predict the NP retentions in membranes for the dead-ended UFs under different conditions. A total of 13 parameters, including the membrane features, particle properties, water solution characteristics, operating conditions, etc., are considered as ANN inputs and the NP retention efficiency as the output. A total of 200 datasets with high quality from literature were selected, in which 50% were for the model training, 30% for the model validation and the remaining 20% for the model testing. A high correlation between the output and inputs was obtained and the significance of the 13 parameters on the NP retention was ranked. A case study was performed to further validate the trained ANN model in the prediction of the retention efficiency of 10 nm gold NPs in a 50 nm pore sized polycarbonate track etched (PCTE) membrane at different pH conditions (5–9). Focusing on the variation of pH, an excellent agreement between the model prediction and the calculation by the modified extended Derjaguin Landau Verwey Overbeek-Maxwell (MEDLVO-Maxwell) model originating from classical and extended DLVO theory was obtained. The interaction energy in the MEDLVO-Maxwell model was based on the separation distance calculated by a quench molecular dynamics (MD) simulation. This study illustrates and validates the application of ANN modeling on the NP retention efficiency prediction in the UF with small PPD ratios.

Predictive analytics modifications in wavelet: case study on Songkhla Lake basin runoff

Accurate short-term rainfall-runoff prediction is very important for flood mitigation and the safety of infrastructures in Southern Thailand. This study aims to utilize both analysis and prediction of the runoff forecast by combining the wavelet technique with regression and artificial neural network. The daily rainfall and runoff data were collected from 1,031 days during January 2017 to October 2019 in Songkhla Lake Basin, Thailand. The performance of calibration and validation of the models is evaluated with appropriate statistical methods; coefficient of determination (R 2 ), root mean square error (RMSE) and Nash-Sutcliffe efficiency coefficient (ENS). The results of daily runoff series modeling indicated that the wavelet artificial neural network model performed the best among those models. This model showed the Coefficient of Determination, Nash-Sutcliffe Efficiency and Root mean Square Error in the value of 0.9999, 0.9998 and 0.0037, respectively. These values explained that the model can describe 99.99% of the variation of the current runoff in Songkhla Lake Basin.

Predictive analytics modifications in wavelet: case study on Songkhla Lake basin runoff

Accurate short-term rainfall-runoff prediction is very important for flood mitigation and the safety of infrastructures in Southern Thailand. This study aims to utilize both analysis and prediction of the runoff forecast by combining the wavelet technique with regression and artificial neural network. The daily rainfall and runoff data were collected from 1,031 days during January 2017 to October 2019 in Songkhla Lake Basin, Thailand. The performance of calibration and validation of the models is evaluated with appropriate statistical methods; coefficient of determination (R 2 ), root mean square error (RMSE) and Nash-Sutcliffe efficiency coefficient (ENS). The results of daily runoff series modeling indicated that the wavelet artificial neural network model performed the best among those models. This model showed the Coefficient of Determination, Nash-Sutcliffe Efficiency and Root mean Square Error in the value of 0.9999, 0.9998 and 0.0037, respectively. These values explained that the model can describe 99.99% of the variation of the current runoff in Songkhla Lake Basin.

Improved Water Quality Prediction with Hybrid Wavelet-Genetic Programming Model and Shannon Entropy

Prediction of biochemical oxygen demand (BOD) as the main pollution indicators of organic pollution in freshwater resources is necessary. In the present work, a hybrid wavelet-genetic programming (WGP) method was implemented to improve prediction of BOD. The Shannon entropy was used to identify the optimal input combinations of WGP. In addition, an investigation was done to find which functions of wavelet and decomposition levels have better results in conjunction with genetic programming (GP). For comparison of WGP efficiency, five machine learning methods consisting of WANN (wavelet-artificial neural network), ANN (artificial neural network), GP, DT (decision tree) and BN (Bayesian network) were considered. Experiments on wavelet-processed data revealed that the best results were obtained when the models WGP and WANN were calibrated at three levels of decomposition using the Dmey mother wavelet function. The WGP model created rational forecasts for the peak BOD values. The results show that the use of Shannon entropy is suitable for determining the optimal composition of inputs to machine learning methods. Comparison of the results indicate that the WGP model is superior to the GP, ANN, DT, BN and WANN models based on data from the Varian Hotel and Dam Input stations.

Sea-water-level prediction via combined wavelet decomposition, neuro-fuzzy and neural networks using SLA and wind information

Sea-water-level (SWL) prediction significantly impacts human lives and maritime activities in coastal regions, particularly at offshore locations with shallow water levels. Long-term SWL forecasts, which are conventionally obtained via harmonic analysis, become ineffective when nonperiodic meteorological events predominate. Artificial intelligence combined with other data-processing methods can effectively forecast highly nonlinear and nonstationary inflow patterns by recognizing historical relationships between input and output. These techniques are considerably useful in time-series data predictions. This paper reports the development of a hybrid model to realize accurate multihour SWL forecasting by combining an adaptive neuro-fuzzy inference system (ANFIS) with wavelet decomposition while using sea-level anomaly (SLA) and wind-shear-velocity components as inputs. Numerous wavelet-ANFIS (WANFIS) models have been tested using different inputs to assess their applicability as alternatives to the artificial neural network (ANN), wavelet ANN (WANN), and ANFIS models. Different error definitions have been used to evaluate results, which indicate that integrated wavelet-decomposition and ANFIS models improve the accuracy of SWL prediction and that the inputs of SLA and wind-shear velocity exhibit superior prediction capability compared to conventional SWL-only models.

Hybrid ARFIMA Wavelet Artificial Neural Network Model for DJIA Index Forecasting

This paper proposes a hybrid modelling approach for forecasting returns and volatilities of the stock market. The model, called ARFIMA-WLLWNN model, integrates the advantages of the ARFIMA model, the wavelet decomposition technique (namely, the discrete MODWT with Daubechies least asymmetric wavelet filter) and artificial neural network (namely, the LLWNN neural network). The model develops through a two-phase approach. In phase one, a wavelet decomposition improves the forecasting accuracy of the LLWNN neural network, resulting in the Wavelet Local Linear Wavelet Neural Network (WLLWNN) model. The Back Propagation (BP) and Particle Swarm Optimization (PSO) learning algorithms optimize the WLLWNN structure. In phase two, the residuals of an ARFIMA model of the conditional mean become the input to the WLLWNN model. The hybrid ARFIMA-WLLWNN model is evaluated using daily closing prices for the Dow Jones Industrial Average (DJIA) index over 01/01/2010 to 02/11/2020. The experimental results indicate that the PSO-optimized version of the hybrid ARFIMA-WLLWNN outperforms the LLWNN, WLLWNN, ARFIMA-LLWNN, and the ARFIMA-HYAPARCH models and provides more accurate out-of-sample forecasts over validation horizons of one, five and twenty-two days.

Modelling seasonal flow regime and environmental flow in Punarbhaba river of India and Bangladesh

Abstract Investigations of changing the flow regime and flow prediction are of vital societal and hydro-ecological importance in a transboundary river like Punarbhaba river between India and Bangladesh. The present paper investigated flow regime though advance periodicity models (Morlet’s wavelet transformation) at the seasonal scale. Flow prediction using advanced machine learning techniques like Support vector machine (SVM), Artificial Neural Network (ANN), Hybrid wavelet ANN (W-ANN), Random forest(RF) capturing the periodicity, duration, cyclic or semi-cyclic nature of flow wave or wavelet from the historical time series data (1978–2017) is very crucial for environmental flow management and estimating present and future states of environmental flow. Flow alteration modeling (using heat map) and estimation of environmental flow (using duration curve shifting and RVA methods) are another vital objectives of this work to know the present and future hydro-ecological state. The result of periodicity clearly identified two distinct flow regimes before and after 1992-93 triggered by damming over there in 1992. All the prediction models identified the declining trend of flow in all the seasons, however hybrid wavelet ANN model could be treated as the best suited because of its very high-performance level. The degree of hydrological alteration is identified very high in the post-hydrological alteration (PHA) (post-1992) period and it is likely to be intensified predicted period. The estimated environmental flow state in PHA falls under moderate to critically modified states but if alteration goes on in this way ecological deficit will be the obvious result. For the survival of the river estimated environmental flow could be released primarily.

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

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. The RMSE of ANN model during calibration and validation were found to be 0.2868 and 0.3648 respectively, whereas for the WANN model the respective values were 0.1946 and 0.1695. Efficiencies during calibration and validation for ANN model were 0.8862 per cent and 0.8465 per cent respectively, whereas for WANN model were found to be much higher with the respective values of 0.9436 per cent and 0.9568 per cent indicating substantial improvement in the model performance. Hence hybrid ANN model is the promising tool to predict water table fluctuation as compared to ANN model.

Long-term monthly streamflow forecasting in humid and semiarid regions

Long-term monthly streamflow forecasting has great importance in the water resource system planning. However, its modelling in extreme cases is difficult, especially in semiarid regions. The main purpose of this paper is to evaluate the accuracy of artificial neural networks (ANNs) and hybrid wavelet-artificial neural networks (WA-ANNs) for multi-step monthly streamflow forecasting in two different hydro-climatic regions in Northern Algeria. Different issues have been addressed, both those related to the model’s structure and those related to wavelet transform. The discrete wavelet transform has been used for the preprocessing of the input variables of the hybrid models, and the multi-step streamflow forecast was carried out by means of a recursive approach. The study demonstrated that WA-ANN models outperform the single ANN models for the two hydro-climatic regions. According to the performance criteria used, the results highlighted the ability of WA-ANN models with lagged streamflows, precipitations and evapotranspirations to forecast up to 19 months for the humid region with good accuracy [Nash–Sutcliffe criterion (Ns) equal 0.63], whereas, for the semiarid region, the introduction of evapotranspirations does not improve the model’s accuracy for long lead time (Ns less than 0.6 for all combinations used). The maximum lead time achieved, for the semiarid region, was about 13 months, with only lagged streamflows as inputs.

A wavelet neural network approach to predict daily river discharge using meteorological data

This paper reports some part of modelling and data analysis work carried out within the frame of a comprehensive project on the web-based development of watershed information system. This work basically aims to present the daily discharge predictions from the actual discharge along with the meteorological data using a wavelet neural network approach, which combines two methods: discrete wavelet transform and artificial neural networks. The wavelet–artificial neural network model developed provides a good fit with the measured data, in particular with zero discharge in the summer months and also with the peaks and sudden changes in discharge on the test data collected throughout the year. The results indicate that the wavelet–artificial neural network model based predictions are distinctly superior to that of conventional artificial neural network model that corresponds up to an 80% reduction in the mean-squared error between the artificial neural network model and measured data.