A novel hybrid framework based on the ANFIS, discrete wavelet transform, and optimization algorithm for the estimation of water quality parameters

Kadkhodazadeh, Mojtaba; Farzin, Saeed

doi:10.2166/wcc.2022.078

Cited by 6 publications

(2 citation statements)

References 42 publications

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“…It is worth mentioning that the existing research recognizes the critical role played by the DWT in water-related (e.g., meteorological, hydrological, etc.) time series due to the discrete feature of the corresponding time series [59]. In addition, the DWT is more efficient compared to the CWT since it limits the production of excessively redundant data which is a consequence of computing all wavelet coefficients in any possible scale.…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

Drought Forecasting Using Integrated Variational Mode Decomposition and Extreme Gradient Boosting

Ekmekcioğlu

2023

Water

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The current study seeks to conduct time series forecasting of droughts by means of the state-of-the-art XGBoost algorithm. To explore the drought variability in one of the semi-arid regions of Turkey, i.e., Denizli, the self-calibrated Palmer Drought Severity Index (sc-PDSI) values were used and projections were made for different horizons, including short-term (1-month: t + 1), mid-term (3-months: t + 3 and 6-months: t + 6), and long-term (12-months: t + 12) periods. The original sc-PDSI time series was subjected to the partial autocorrelation function to identify the input configurations and, accordingly, one- (t − 1) and two-month (t − 2) lags were used to perform the forecast of the targeted outcomes. This research further incorporated the recently introduced variational mode decomposition (VMD) for signal processing into the predictive model to enhance the accuracy. The proposed model was not only benchmarked with the standalone XGBoost but also with the model generated by its hybridization with the discrete wavelet transform (DWT). The overall results revealed that the VMD-XGBoost model outperformed its counterparts in all lead-time forecasts with NSE values of 0.9778, 0.9405, 0.8476, and 0.6681 for t + 1, t + 3, t + 6, and t + 12, respectively. Transparency of the proposed hybrid model was further ensured by the Mann–Whitney U test, highlighting the results as statistically significant.

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Section: Discrete Wavelet Transformmentioning

confidence: 99%

Drought Forecasting Using Integrated Variational Mode Decomposition and Extreme Gradient Boosting

Ekmekcioğlu

2023

Water

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“…As a result, even for a highly nonlinear system, ANFIS is expected to generate very accurate predictions. It has been widely used in predicting water quality parameters in rivers (Kisi and Zounemat-Kermani 2014 ; Kadkhodazadeh and Farzin 2022 ; Almadani and Kheimi 2023 ). In line with the objective of this study, specifically, Table 1 summarizes the applications of ANFIS models in modeling BOD in rivers.…”

Section: Introductionmentioning

confidence: 99%

Modelling biochemical oxygen demand using improved neuro-fuzzy approach by marine predators algorithm

Adnan,

Dai,

Kisi

et al. 2023

Environ Sci Pollut Res

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Biochemical oxygen demand (BOD) is one of the most important parameters used for water quality assessment. Alternative methods are essential for accurately prediction of this parameter because the traditional method in predicting the BOD is time-consuming and it is inaccurate due to inconstancies in microbial multiplicity. In this study, the applicability of four hybrid neuro-fuzzy (ANFIS) methods, ANFIS with genetic algorithm (GA), ANFIS with particle swarm optimization (PSO), ANFIS with sine cosine algorithm (SCA), and ANFIS with marine predators algorithm (MPA), was investigated in predicting BOD using distinct input combinations such as potential of hydrogen (pH), dissolved oxygen (DO), electrical conductivity (EC), water temperature (WT), suspended solids (SS), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (T-P) acquired from two river stations, Gongreung and Gyeongan, South Korea. The applicability of multi-variate adaptive regression spline (MARS) in determination of the best input combination was examined. The ANFIS-MPA was found to be the best model with the lowest root mean square error and mean absolute error and the highest determination coefficient. It improved the root mean square error of ANFIS-PSO, ANFIS-GA, and ANFIS-SCA models by 13.8%, 12.1%, and 6.3% for Gongreung Station and by 33%, 25%, and 6.3% for Gyeongan Station in the test stage, respectively.

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Evolutionary support vector regression for monitoring Poisson profiles

Yeganeh,

Abbasi,

Shongwe

et al. 2023

Soft Comput

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Many researchers have shown interest in profile monitoring; however, most of the applications in this field of research are developed under the assumption of normal response variable. Little attention has been given to profile monitoring with non-normal response variables, known as general linear models which consists of two main categories (i.e., logistic and Poisson profiles). This paper aims to monitor Poisson profile monitoring problem in Phase II and develops a new robust control chart using support vector regression by incorporating some novel input features and evolutionary training algorithm. The new method is quicker in detecting out-of-control signals as compared to conventional statistical methods. Moreover, the performance of the proposed scheme is further investigated for Poisson profiles with both fixed and random explanatory variables as well as non-parametric profiles. The proposed monitoring scheme is revealed to be superior to its counterparts, including the likelihood ratio test (LRT), multivariate exponentially weighted moving average (MEWMA), LRT-EWMA and other machine learning-based schemes. The simulation results show superiority of the proposed method in profiles with fixed explanatory variables and non-parametric models in nearly all situations while it is not able to be the best in all the simulations when there are with random explanatory variables. A diagnostic method with machine learning approach is also used to identify the parameters of change in the profile. It is shown that the proposed profile diagnosis approach is able to reach acceptable results in comparison with other competitors. A real-life example in monitoring Poisson profiles is also provided to illustrate the implementation of the proposed charting scheme.

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A novel hybrid framework based on the ANFIS, discrete wavelet transform, and optimization algorithm for the estimation of water quality parameters

Cited by 6 publications

References 42 publications

Drought Forecasting Using Integrated Variational Mode Decomposition and Extreme Gradient Boosting

Drought Forecasting Using Integrated Variational Mode Decomposition and Extreme Gradient Boosting

Modelling biochemical oxygen demand using improved neuro-fuzzy approach by marine predators algorithm

Evolutionary support vector regression for monitoring Poisson profiles

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