Wavelet Adaptive Neuro-fuzzy Inference System Research Articles

Modeling the total outflow of reservoirs using Wavelet-developed approaches: a case study of the Mahabad Dam reservoir, Iran

Abstract Lack of water reserves in artificial reservoirs poses serious challenges in meeting various human requirements, especially during periods of water scarcity. In the current research, the Total Outflow (TO) of the Mahabad Dam reservoir has been estimated under six scenarios including the Monthly Cumulative Rainfall (MCR), Snow Water Equivalent (SWE), Stream Flow (SF), Mean Temperature (T), Pan Evaporation (Ep), Sediment Flushing Gate Outlet (SFGO), Penstock Outflow (PO), Evaporation Losses (EL), Cumulative Non-Scheduled Discharge (CNSD), Live Storage Volume (LSV), Water Surface Area (WSA), Monthly Water Level (MWL), Total Allocated Water (TAW), and Generated Power (GP) variables for the 2001–2021 period. Estimation of TO is accomplished via individual and wavelet-developed (W-developed) data-mining approaches, including Artificial Neural Networks (ANNs), wavelet-ANNs (WANNs), adaptive neuro-fuzzy inference system (ANFIS), wavelet-ANFIS (WANFIS), Gene Expression Programming (GEP), and wavelet-GEP (WGEP). The obtained values of RMSE for WGEP1–WGEP6 models account for 5.917, 2.319, 4.289, 8.329, 10.713, and 9.789 million cubic meters (MCM), respectively, based on the following scenarios: reservoir inlet elements, reservoir outlet elements, consumption, storage characteristic, climate, and energy. This research revealed that combining the wavelet theory (WT) with individual models can be a powerful method to improve the modeling performance in the TO estimation.

Temporal and spatial estimation of groundwater electrical conductivity using soft computing approaches: Analysis of East Azerbaijan Province, Iran

Abstract The evaluation of groundwater quality plays an important role in the management of groundwater. The main objectives of the present work are to develop a novel soft computing framework including Adaptive Neuro-Fuzzy Inference System (ANFIS), Wavelet-ANFIS (WANFIS), Gene Expression Programming (GEP), and Wavelet-GEP (WGEP) for the temporal and spatial estimation of groundwater electrical conductivity (EC) in the East Azerbaijan province, Iran over 2001–2020. The results demonstrate the importance of wavelet transform application; the performance percentage enhancement of the WANFIS and WGEP models compared to the ANFIS and GEP, using the RMSE criterion, ranged from 15.48 to 51.09% and from 5.06 to 86.95%, respectively. All the developed models showed the WGEP superior compared to others. The impact of land use characteristics, climatic conditions, and geological features on groundwater quality showed that there is a direct relationship between the extent of agricultural land, semi-arid climate conditions and groundwater EC amounts. The results demonstrated that the values of EC increase from east to west, indicating the direct exchange of surface and groundwater in the study area. Moreover, groundwater quality changes significantly across the width of the fault, with groundwater EC in the northern part of the fault higher than that in the southern part.

Application of wavelet theory to enhance the performance of machine learning techniques in estimating water quality parameters (case study: Gao-Ping River).

There are several methods for modeling water quality parameters, with data-based methods being the focus of research in recent decades. The current study aims to simulate water quality parameters using modern artificial intelligence techniques, to enhance the performance of machine learning techniques using wavelet theory, and to compare these techniques to other widely used machine learning techniques. EC, Cl, Mg, and TDS water quality parameters were modeled using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). The study area in the present research is Gao-ping River in Taiwan. In the training state, using hybrid models with wavelet transform improved the accuracy of ANN models from 8.1 to 22.5% and from 25.7 to 55.3% in the testing state. In addition, wavelet transforms increased the ANFIS model's accuracy in the training state from 6.7 to 18.4% and in the testing state from 9.9 to 50%. Using wavelet transform improves the accuracy of machine learning model results. Also, the WANFIS (Wavelet-ANFIS) model was superior to the WANN (Wavelet-ANN) model, resulting in more precise modeling for all four water quality parameters.

Evaluation of the success of the hybrid wavelet-based ANFIS approach in the estimation of monthly stream flows of the Bitlis River, Turkey

Abstract This study aimed to predict monthly flows using an adaptive neuro-fuzzy inference system (ANFIS) and wavelet-ANFIS (W-ANFIS) and to determine the effect of wavelet transformation on the success of the machine learning model. For this purpose, the model inputs are divided into three subcomponents with Daubechies 10 mother wavelets. Subcomponents with the highest correlation were chosen as inputs. The most suitable models were selected by dividing the inputs into 3–7 sub-sets, using 11 different lagged input combinations, and testing various membership functions. In establishing the ANFIS model, 75% of the data were used for training and 25% for testing. The performance of ANFIS models was evaluated with root mean square error, Pearson correlation coefficient, determination coefficients, and Taylor diagram. A model with two sub-sets, a hybrid learning algorithm, a Gbellmf membership function, and 400 iterations was selected as the most suitable. It was concluded that the W-ANFIS model used with the wavelet transform method increased the success of the established ANFIS model. Moreover, it was suggested that the W-ANFIS hybrid machine learning model established in the study can be used effectively in similar climatic regions fed by snowmelt and dominated by a semi-arid climate.

Fault Prediction Based on Leakage Current in Contaminated Insulators Using Enhanced Time Series Forecasting Models.

To improve the monitoring of the electrical power grid, it is necessary to evaluate the influence of contamination in relation to leakage current and its progression to a disruptive discharge. In this paper, insulators were tested in a saline chamber to simulate the increase of salt contamination on their surface. From the time series forecasting of the leakage current, it is possible to evaluate the development of the fault before a flashover occurs. In this paper, for a complete evaluation, the long short-term memory (LSTM), group method of data handling (GMDH), adaptive neuro-fuzzy inference system (ANFIS), bootstrap aggregation (bagging), sequential learning (boosting), random subspace, and stacked generalization (stacking) ensemble learning models are analyzed. From the results of the best structure of the models, the hyperparameters are evaluated and the wavelet transform is used to obtain an enhanced model. The contribution of this paper is related to the improvement of well-established models using the wavelet transform, thus obtaining hybrid models that can be used for several applications. The results showed that using the wavelet transform leads to an improvement in all the used models, especially the wavelet ANFIS model, which had a mean RMSE of 1.58 , being the model that had the best result. Furthermore, the results for the standard deviation were 2.18 , showing that the model is stable and robust for the application under study. Future work can be performed using other components of the distribution power grid susceptible to contamination because they are installed outdoors.

An improved adaptive neuro fuzzy inference system model using conjoined metaheuristic algorithms for electrical conductivity prediction

Precise prediction of water quality parameters plays a significant role in making an early alert of water pollution and making better decisions for the management of water resources. As one of the influential indicative parameters, electrical conductivity (EC) has a crucial role in calculating the proportion of mineralization. In this study, the integration of an adaptive hybrid of differential evolution and particle swarm optimization (A-DEPSO) with adaptive neuro fuzzy inference system (ANFIS) model is adopted for EC prediction. The A-DEPSO method uses unique mutation and crossover processes to correspondingly boost global and local search mechanisms. It also uses a refreshing operator to prevent the solution from being caught inside the local optimal solutions. This study uses A-DEPSO optimizer for ANFIS training phase to eliminate defects and predict accurately the EC water quality parameter every month at the Maroon River in the southwest of Iran. Accordingly, the recorded dataset originated from the Tange-Takab station from 1980 to 2016 was operated to develop the ANFIS-A-DEPSO model. Besides, the wavelet analysis was jointed to the proposed algorithm in which the original time series of EC was disintegrated into the sub-time series through two mother wavelets to boost the prediction certainty. In the following, the comparison between statistical metrics of the standalone ANFIS, least-square support vector machine (LSSVM), multivariate adaptive regression spline (MARS), generalized regression neural network (GRNN), wavelet-LSSVM (WLSSVM), wavelet-MARS (W-MARS), wavelet-ANFIS (W-ANFIS) and wavelet-GRNN (W-GRNN) models was implemented. As a result, it was apparent that not only was the W-ANFIS-A-DEPSO model able to rise remarkably the EC prediction certainty, but W-ANFIS-A-DEPSO (R = 0.988, RMSE = 53.841, and PI = 0.485) also had the edge over other models with Dmey mother in terms of EC prediction. Moreover, the W-ANFIS-A-DEPSO can improve the RMSE compared to the standalone ANFIS-DEPSO model, accounting for 80%. Hence, this model can create a closer approximation of EC value through W-ANFIS-A-DEPSO model, which is likely to act as a promising procedure to simulate the prediction of EC data.

Multivariate modeling of river water withdrawal using a hybrid evolutionary data-driven method

Abstract Water resources are one of the most important features of the environment to meet human needs. In the current research, morphological, quantitative and qualitative hydrological, and land use factors as well as the combined factor, which is the combination of effective variables of the aforementioned factors, have been used to estimate River Water Withdrawal (RWW) for agricultural uses. Lavasanat and Qazvin are selected as study areas, located in the Namak Lake basin in Iran, with Bsk and Csa climate categories, respectively. Estimation of RWW is performed using single and Wavelet–hybrid (W-hybrid) data-driven methods, including Artificial Neural Networks (ANNs), Wavelet–ANN (WANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS), Wavelet–ANFIS (WANFIS), Gene Expression Programming (GEP), and Wavelet–GEP (WGEP). Due to the evaluation criteria, the performance of the WGEP model is the best among the others for estimating RWW variables in both study areas. Considering the W-hybrid models with data de-noising for estimating RWW in the Lavasanat and Qazvin study areas, the obtained values of RMSE for WGEP11 to WGEP15 and WGEP21 to WGEP25 equal 67.268, 54.659, 80.871, 50.796, 15.676 and 105.532, 96.615, 105.018, 160.961, 44.332, respectively. The results indicate that WGEP and ANN are the best and poorest models in both study areas without regarding climate condition effects. Also, a combined factor which includes River Width (RW), minimum flow rate (QMin), average flow rate (QMean), Electrical Conductivity (EC), and Cultivated Area (CA) variables is introduced as the best factor to estimate RWW variables compared with the other factors in both the Bsk and Csa climate categories. On the other hand, qualitative hydrological and land use factors were the weakest ones to estimate RWW variables in the Bsk and Csa climate categories, respectively. Therefore, the current study explores how the mathematical relations for estimating RWW have a significant effect on water resources management and planning by policymakers in the future.

Artificial intelligence for surface water quality monitoring and assessment: a systematic literature analysis

The goal of this paper was to conduct a systematic literature analysis on the application of different types of artificial intelligence models in surface water quality monitoring. The analysis focused on the methods used, the location of the experiments, the input parameters, and the output metrics applied to categorise the results presented. Furthermore, the main contribution of this paper is to synthesise the existing body of knowledge in the state of the art (the last decade) and identified common threads and gaps that would open up new challenging, exciting and significant research directions. From the study, it was observed that Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANN) are the most utilised artificial intelligence models for water quality monitoring and assessment in the last decade. Most of the studies using Neural Networks in surface water quality monitoring and assessment are originated in Iran and south-east Asia. ANFIS, Wavelet-ANN (W-ANN) and Wavelet-ANFIS (W-ANFIS) were most accurate for the prediction of surface water quality. There was no clear relationship between data size and R2 value (at the testing stage). Biochemical oxygen demand (BOD) was the most investigated parameter in surface water quality monitoring and assessment. An appraisal of recent literature was also presented and knowledge gaps and future perspective in the research area were proposed.

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.

Evolutionary Techniques for Optimizing Air Quality Model

In this study, the adaptive neuro-fuzzy inference system (ANFIS) is used to perform predictive analysis of air pollutant-fine particulate matter. Fine particulate matter are of size less than or equal to 2.5 microns (PM2.5) which is the prime pollutant because of its severe effects on pulmonary and nervous systems. The shortcomings of ANFIS to get trapped in local optima or computational complexity lead to the introduction of algorithms inspired by nature. The evolutionary algorithms form on flocking and genetics are employed in artificial intelligence. Many attempts have been made and are still going on to improve the accuracy in forecasting air quality. The study contributes to the advancing research by examining pertinence of evolutionary algorithms-particle swarm optimization (PSO) and genetic algorithm (GA) with ANFIS in predicting fine particulate matter. The non-stationary PM2.5 time series is decomposed using the discrete wavelet transform and sub-band predictive analysis is carried out. The wavelet ANFIS-PSO method is also compared with wavelet ANFIS (WANFIS), WANFIS-GA and classic ANFIS. The analysis revealed that WANFIS-PSO outperforms the other models.

Application of artificial intelligence models for the prediction of standardized precipitation evapotranspiration index (SPEI) at Langat River Basin, Malaysia

Drought forecasting is a vital for mitigating the impact of drought events on the economy, tourism, agriculture and water resource systems. This paper adopts the proposed Wavelet-ARIMA-ANN (WAANN) model and the latest Wavelet-Adaptive Neuro-Fuzzy Inference System (WANFIS) model to predict the Standardized Precipitation Evapotranspiration Index (SPEI) at the Langat River Basin for different time scales (1-month, 3-months and 6-months). Model input data pre-processing with wavelet decomposition for improving the performance of the models was carried out apriori. The historical SPEI from 1976 to 2007 were used in the WAANN and WANFIS models for predicting the SPEI for the test period from 2008 to 2015. The Adjusted Coefficient of Determination (R2adj), Root-Mean-Square-Error (RMSE), Mean Absolute Error (MAE), Willmott's Index of Agreement (d) and the Nash-Sutcliffe Coefficient of Efficiency (E) were used to assess the models. It was found that the prediction accuracy of the two models improved with time scale length. For the prediction of SPEI-1 (1-month), the errors associated with both models were considered relatively high. Based on the performance measures and graphical plots, the WAANN model is better for the prediction of SPEI-3 and SPEI-6. The WANFIS model had satisfactory prediction of the mid-term drought forecasting for all stations. The WAANN model developed in this study however, gives better accuracy for both, the short-term and mid-term drought forecasting.

Lake Level Forecasting Using Wavelet-SVR, Wavelet-ANFIS and Wavelet-ARMA Conjunction Models

Accurate predicting of lake level fluctuations is essential and basic in water resources management for water supply purposes. The predicting of lake level is complicated because of it is affected by nonlinear hydrological processes. This paper applies integrated wavelet and auto regressive moving average (ARMA), adaptive neuro fuzzy inference system (ANFIS) and support vector regression (SVR) models for forecasting monthly lake level fluctuations. First, lake level time series is decomposed into low and high frequency components by using discrete wavelet transform. Then, each component is separately predicted by using ARMA, ANFIS and SVR models. Finally, the predicted components are summed to obtain estimated original lake level time series. The performance of the proposed WSVR (Wavelet-SVR), WANFIS (Wavelet-ANFIS) and WARMA (Wavelet-ARMA) models is compared with single ARMA, SVR and ANFIS models. Results show that the integrated models give better precision in forecasting lake levels in the study region when compared to single models. WSVR model is found to be slightly better than the other integrated models.

A Wavelet-ANFIS Model to Estimate Sedimentation in Dam Reservoir

An accurate prediction of sedimentation in dam reservoir is a challenging issue due to the complex and non-linear physics of the problem. Anyhow, soft-computing-based techniques showed great ability for predicting non-linear phenomena and have been used for different purposes. The main objective of this study is to estimate the volume of sedimentation in Karaj dam using a wavelet-ANFIS (WANFIS) and a wavelet-neural network (WANN) model. Monthly average flow is used to estimate monthly averaged suspended sediment load for a thirty-year period. The amount of bed load is computed based on the suspended sediment load and the river slope and the total volume of sedimentation in the reservoir is calculated with subtracting the upstream (Karaj River) and downstream (Beylaghan River) total sediment load. In WANFIS and WANN models, monthly average flow time series are decomposed to several sub-time series using different wavelet decomposition levels. The total volume of sedimentation in Karaj dam obtained from different techniques such as WANFIS, WANN, ANFIS, ANN and hydrography are compared together. The comparison demonstrates that WANFIS model is superior to the other techniques. For WANFIS and WANN models, the best model is obtained by two and three wavelet decomposition levels respectively. Findings of this study reveal that Wavelet-ANFIS models can be applied as a successful tool to predict the volume of sedimentation.

Effect of Utilization of Discrete Wavelet Components on Flood Forecasting Performance of Wavelet Based ANFIS Models

Wavelet based flood forecasting models are known to perform better than conven- tional models, yet the effect of the way wavelet components are combined to develop a model on the forecasting performance, is inadequately investigated. To demonstrate this, two types of wavelet- adaptive neuro-fuzzy inference system (WANFIS), i.e. WANFIS-split data model (WANFIS-SD) and WANFIS-modified time series model (WANFIS-MS) are developed to forecast river water levels with 1-day lead time. To develop these models, first the original level time series (OLTS) is decomposed into discrete wavelet components (DWCs) by discrete wavelet transform (DWT) upto three resolution levels. In WANFIS-SD, all wavelet compo- nents are used as inputs while WANFIS-MS ignores the noise wavelet components and utilizes only the effective wavelet components. The effectiveness of the developed models are evaluated through application to two Indian rivers, Kamla and Kosi, which vary significantly in their catchment area and flow patterns. The proposed models are found to forecast river water levels accurately. On comparison, the WANFIS-SD is found to perform better than WANFIS-MS for high flood levels.

Vulnerability assessment of southern coastal areas of Iran to sea level rise: evaluation of climate change impact

Recent investigations have demonstrated global sea level rise as being due to climate change impact. Probable changes in sea level rise need to be evaluated so that appropriate adaptive strategies can be implemented. This study evaluates the impact of climate change on sea level rise along the Iranian south coast. Climatic data simulated by a GCM (General Circulation Model) named CGCM3 under two-climate change scenarios A1b and A2 are used to investigate the impact of climate change. Among the different variables simulated by this model, those of maximum correlation with sea level changes in the study region and least redundancy among themselves are selected for predicting sea level rise by using stepwise regression. Two Discrete Wavelet artificial Neural Network (DWNN) models and a Discrete Wavelet Adaptive Neuro-Fuzzy Inference System (DWANFIS) are developed to explore the relationship between selected climatic variables and sea level changes. In these models, wavelets are used to disaggregate the time series of input and output data into different components. ANFIS/ANN are then used to relate the disaggregated components of predictors and predictand (sea level) to each other. The results show a significant rise in sea level in the study region under climate change impact, which should be incorporated into coastal area management.