A Novel Approach for Prediction of Monthly Ground Water Level Using a Hybrid Wavelet and Non-Tuned Self-Adaptive Machine Learning Model

Malekzadeh, Maryam; Kardar, Saeid; Saeb, Keivan; Shabanlou, Saeid; Taghavi, Lobat

doi:10.1007/s11269-019-2193-8

Cited by 56 publications

(9 citation statements)

References 17 publications

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“…According to Babran, the preservation of the river ecosystem relied on the quantity and quality of the river flow regime, and the construction of large dams caused quantitative and qualitative changes in the downstream rivers, which were effective in the sanitary consumption of river water (6). Therefore, in addition to the amount of the demands of surface water and groundwater, their quality should also be considered (25). Accordingly, it is essential to keep the reliable quantity and quality of the water required to maintain the ecological function on which humans depend.…”

Section: Discussionmentioning

confidence: 99%

Determination of the amount of Sanitary Water Flow from Dams based on the Quality of Drinking Water: A Case Study, Taleghan River, Iran

2020

ircmj

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Introduction: Given the crisis of water shortage and the industrial development in Iran, comprehensive water-resource management, planning, and serious handling of water quality of the rivers in Iran are the critical issues to tackle with. The concentration of river pollutants is a function of both the quantity and quality of the river flow regime. In this regard, the construction of large dams leads to quantitative and qualitative changes in downstream rivers. These changes are effective in the health of the river environment for such uses as drinking, agriculture, and industry. Accordingly, it is essential to consider the quality of river water in addition to the amount of river water needs. Materials and Methods: This study examined a 22-km long section of the river at the riffle of Taleghan Dam in Alborz Province (103 km from Karaj), Iran. The average annual and monthly discharges of the river in four 6-km-apart stations were estimated. The statistics of eight hydrometric stations and a discharge-surface method were used to calculate the average annual discharge of each sub-basin downstream of Taleghan Dam. Moreover, the discharge non-dimensionalization method, along with the observational statistics of the index station, was used to calculate the average monthly discharge in the examined stations. The Hydrologic Engineering Centers River Analysis System (RAS-HEC) software was then utilized to determine the values of river flow rates hydraulically. Additionally, water quality parameters were compared with the standard concentrations proposed by the World Health Organization (WHO) for drinking-water quality to examine possible changes in pollutant concentrations during the study. Correlation and regression statistical tests in SPSS software (version 24) were then used to analyze the relationship between discharge and pollutant concentration. Results: The experimental equation of Q = 0.0372A0.8641 was obtained to estimate the discharge based on the sub-basins area using the discharge-surface method. The average annual discharge at stations 2, 3, and 4 (B, C, and D) were estimated at 1.39, 2.11, and 3.39 m3/s, respectively, using this equation. Subsequently, the average monthly discharges in the studied stations in September were calculated at 0.21, 0.29, and 0.46 m3/s, respectively. Afterward, the discharge was measured using HEC-RAS software in the same month at 0.34, 0.44, 0, and 0.62 m3/s, respectively. The examination of water quality values from among the 17 water quality parameters revealed that physicochemical elements, pH concentration, lead (Pb), and electrical conductivity were higher than the standard concentration of drinking water proposed by the WHO. Conclusion: A model was presented to estimate sanitary water flow by performing correlation tests and linear regression calculations between the river discharge at the dam downstream and the concentration of water quality parameters. According to the proposed model, the minimum flow of sanitary water was estimated at 1.82 m3/s to be considered to release from the dam in the driest month of the year. Therefore, the release of water as the minimum flow of sanitary water less than 1.82 m3/s was not allowed in any other month of the year.

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Section: Discussionmentioning

confidence: 99%

Determination of the amount of Sanitary Water Flow from Dams based on the Quality of Drinking Water: A Case Study, Taleghan River, Iran

2020

ircmj

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“…There are various statistical indices for evaluating results of numerical models. Firstly, the applied criteria are universally valid and have been extensively applied in different numerical studies (Ebtehaj et al, 2015; Azimi et al, 2018; Malekzadeh et al, 2019). Secondly, different criteria should be used to evaluate numerical models appropriately because applied statistical indices have different acceptable ranges.…”

Section: Methodsmentioning

confidence: 99%

Simulation of rainfall and runoff time series using robust machine learning*

Alizadeh

Rajabi

Shabanlou

et al. 2020

Irrigation and Drainage

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In this paper, the precipitation and runoff time‐series data of the Shaharchay River basin from 2000 to 2017 are simulated by a modern hybrid artificial intelligence technique. In order to develop the mentioned artificial intelligence model, the extreme learning machine (ELM), differential evolution and wavelet transform are combined and the SAELM and WASAELM hybrid models are provided. Initially, the most effective lags of the time‐series data are distinguished using an autocorrelation function. Using the lags, seven artificial intelligence models are defined for each of the SAELM and WSAELM models. To simulate precipitation and runoff, the sym and coif mother wavelets are chosen as the optimal ones, respectively. For the best model, the values of R2, the scatter index and the Nash–Sutcliffe efficiency coefficient (NSC) for simulating precipitation yielded 0.967, 0.208 and 0.965, respectively. Furthermore, a sensitivity analysis shows that the lags (t‐1), (t‐2) and (t‐12) are regarded as the most effective input lags. Ultimately, an uncertainty analysis is carried out for the superior model that the performance of this model in simulating precipitation and runoff is over‐ and underestimated, respectively.

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“…WT is a data preprocessing method that removes noise from the data. Wavelet, which can provide information about frequency and time simultaneously, is a function with zero means (Malekzadeh et al 2019). WT has many advantages over other data analysis techniques and is used to extract hidden information from time-series data ( Jamei et al 2020).…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

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

Kadkhodazadeh

Farzin

2022

Journal of Water and Climate Change

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Improving the performance of machine learning (ML) algorithms is essential for accurately estimating water quality parameters (WQPs). For the first time, a novel hybrid framework, namely the adaptive neural fuzzy inference system–discrete wavelet transform–gradient-based optimization (ANFIS–DWT–GBO), for estimation of electrical conductivity (EC) and total dissolved solids (TDS) is used. Before estimating WQPs, the performance of the ANFIS–DWT–GBO is proven by several benchmark data sets. In addition, three benchmark algorithms, including ANFIS, ANFIS–DWT, and ANFIS–GBO, are used to demonstrate the strength of the novel framework. The principal component analysis (PCA) method determines the best input combination in EC and TDS estimation. The consequences show that the ANFIS–DWT–GBO produces very successful and competitive results in benchmark data sets modeling and WQPs estimation compared to other algorithms. This result is due to the simultaneous use of DWT and optimization algorithm in the proposed framework. DWT can process WQP data before applying it to the algorithms. The GBO is utilized to optimize the hyperfine parameters in the ANFIS. The results show that the highest accuracy of estimating EC and TDS is in Mollasani and Gotvand stations, respectively. The correlation coefficient (R) value in the Mollasani station is 0.99, and in the Gotvand station is 0.98.

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A Novel Approach for Prediction of Monthly Ground Water Level Using a Hybrid Wavelet and Non-Tuned Self-Adaptive Machine Learning Model

Cited by 56 publications

References 17 publications

Determination of the amount of Sanitary Water Flow from Dams based on the Quality of Drinking Water: A Case Study, Taleghan River, Iran

Determination of the amount of Sanitary Water Flow from Dams based on the Quality of Drinking Water: A Case Study, Taleghan River, Iran

Simulation of rainfall and runoff time series using robust machine learning*

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

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