Wavelet Artificial Neural Network Model Research Articles

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

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).

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.

Efficacy in simulating the peak discharge response using soft computing techniques in the Jhelum river basin, India

ABSTRACTAccurate simulation of extreme events of runoff is very important for public safety and hydrological engineering. The simulation of extreme events mainly employs the rainfall-runoff models. This paper tests the potential of soft computing technique-based rainfall-runoff models to simulate the peak runoff events in order to develop an alarming system. This study mainly focusses on hybrid Artificial Neural Networks (ANNs) and Multivariate Adaptive Regression Splines (MARS). The six different regression models used are conventional Artificial Neural Network (ANN), Wavelet Artificial Neural Network (WANN), Bootstrapped Artificial Neural Network (BANN), Wavelet Bootstrapped Artificial Neural Network (WBANN), Multivariate Adaptive Regression Splines (MARS) and Wavelet Multivariate Adaptive Regression Splines (WMARS). The potential inputs were selected based on Auto Correlation Function (ACF) and Cross Correlation Functions (CCF). To implement the methodology, the Jhelum basin in the northern part of India was selected. Based on the results, it was found that all the models except BANN showed overall good performance. The WANN model (NSE = 0.95, RMSE = 1943.15 cusecs, MAPE = 25.5, R = 0.96, DA = 46.8) shows slightly better performance than ANN, WBANN, MARS and WMARS and far better than BANN. The accuracy of the forecasts was checked between WANN and ANN, WBANN and BANN, WMARS and MARS. The results show that decomposition method improves the forecasting accuracy of time series data. Again, the simulation of the peak events was done using the above six models. The efficiency of the models was evaluated on the basis of Normalized Root Mean Square Error (NRMSE). It was found that WANN outperforms the other five models with NRMSE (0.37) for all peak events. All the other models except BANN showed fair results with NRMSE for ANN = 0.66, MARS = 0.68, WBANN = 0.68 and WMARS = 0.53. Thus, it is recommended from the present study that WANN model is more promising for the simulation of peak events in time series data.

Improved Forecasting of Extreme Monthly Reservoir Inflow Using an Analogue-Based Forecasting Method: A Case Study of the Sirikit Dam in Thailand

Reservoir inflow forecasting is crucial for appropriate reservoir management, especially in the flood season. Forecasting for this season must be sufficiently accurate and timely to allow dam managers to release water gradually for flood control in downstream areas. Recently, several models and methodologies have been developed and applied for inflow forecasting, with good results. Nevertheless, most were reported to have weaknesses in capturing the peak flow, especially rare extreme flows. In this study, an analogue-based forecasting method, designated the variation analogue method (VAM), was developed to overcome this weakness. This method, the wavelet artificial neural network (WANN) model, and the weighted mean analogue method (WMAM) were used to forecast the monthly reservoir inflow of the Sirikit Dam, located in the Nan River Basin, one of the eight sub-basins of the Chao Phraya River Basin in Thailand. It is one of four major dams in the Chao Phraya Basin, with a maximum storage of 10.64 km3, which supplies water to 22 provinces in this basin, covering an irrigation area of 1,513,465 hectares. Due to the huge extreme monthly inflow in August, with inflow of more than 3 km3 in 1985 and 2011, monthly or longer lead time inflow forecasting is needed for proper water and flood control management of this dam. The results of forecasting indicate that the WANN model provided good forecasting for whole-year forecasting including both low-flow and high-flow patterns, while the WMAM model provided only satisfactory results. The VAM showed the best forecasting performance and captured the extreme inflow of the Sirikit Dam well. For the high-flow period (July–September), the WANN model provided only satisfactory results, while those of the WMAM were markedly poorer than for the whole year. The VAM showed the best capture of flow in this period, especially for extreme flow conditions that the WANN and WMAM models could not capture.

Rainfall time series disaggregation in mountainous regions using hybrid wavelet-artificial intelligence methods

In mountainous regions, rainfall can be extremely variable in space and time. The need to simulate rainfall time series at different scales on one hand and the lack of recording such parameters in small scales because of administrative and economic problems, on the other hand, disaggregation of rainfall time series to the desired scale is an essential topic for hydro-environmental studies of such mountainous regions. Hybrid models development by combining data-driven methods of least square support vector machine (LSSVM) and Artificial Neural Network (ANN) and wavelet decomposition for disaggregation of rainfall time series are the purpose of this paper. In this study, for disaggregating the Tabriz and Sahand rain-gauges time series, according to nonlinear characteristics of observed time scales, wavelet-least square support vector machine (WLSSVM) and wavelet-artificial neural network (WANN) hybrid models were proposed. For this purpose, daily data of four rain-gauges and monthly data of six rain-gauges from mountainous basin of the Urmia Lake for seventeen years were decomposed with wavelet transform and then using mutual information and correlation coefficient criteria, the sub-series were ranked and superior sub-series were used as input data of LSSVM and ANN models for disaggregating the monthly rainfall time series to the daily time series. Results obtained by these hybrid disaggregation models were compared with the results of LSSVM, ANN and classic multiple linear regression (MLR) models. The efficiency of WANN model with regard to the WLSSVM, ANN, LSSVM and MLR models at validation stage in the optimized case for Tabriz rain-gauge showed up to 9.1%, 22%, 20% and 50% increase and in the optimized case for Sahand rain-gauge showed up to 4.5%, 21.1%, 30.2% and 53.3% increase, respectively.