A New Approach for Regional Groundwater Level Simulation: Clustering, Simulation, and Optimization

Kayhomayoon, Zahra; Milan, Sami Ghordoyee; Azar, Naser Arya; Moghaddam, Hamid Kardan

doi:10.1007/s11053-021-09913-6

Cited by 22 publications

(7 citation statements)

References 53 publications

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“…Along with the developed ANFIS model, several metaheuristic algorithms were used to train the ANFIS to develop hybrid models. According to the literature, each of these algorithms has unique features that can significantly improve the performance of the traditional ANFIS model [1,19,45,46]. The structure of ANFIS-metaheuristic algorithms hybrid models used in this study to predict GWL is depicted in Figure 5.…”

Section: Development Of the Anfis Using Metaheuristic Optimization Al...mentioning

confidence: 99%

“…Groundwater resources are becoming increasingly important, especially in arid and semi-arid regions affected by climate change [1,2]. In many areas, surface water resources are rapidly decreasing, inducing larger pressure on groundwater [2].…”

Section: Introductionmentioning

confidence: 99%

“…These models do not require structural and physical information of the system, instead time-dependent variables are considered. They have been used extensively by various researchers in groundwater management and GWL prediction [1,6,20,21].…”

Section: Introductionmentioning

confidence: 99%

“…Results showed that HHO had the highest performance in improving the ANFIS model [19]. In another study, evaluated the efficiency of ANFIS, ANFIS-HHO, and LSSVM models in predicting the shortage of groundwater reserves [1]. They reported that the optimized ANFIS with HHO had higher performance than other models.…”

Section: Introductionmentioning

confidence: 99%

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A Combination of Metaheuristic Optimization Algorithms and Machine Learning Methods Improves the Prediction of Groundwater Level

Kayhomayoon

Babaeian

Milan

et al. 2022

Water

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Groundwater is a crucial source of water supply in drought conditions, and an auxiliary water source in wet seasons. Due to its increasing importance in view of climate change, predicting groundwater level (GWL) needs to be improved to enhance management. We used adaptive neuro-fuzzy inference systems (ANFIS) to predict the GWL of the Urmia aquifer in northwestern Iran under various input scenarios using precipitation, temperature, groundwater withdrawal, GWL during the previous month, and river flow. In total, 11 input patterns from various combinations of variables were developed. About 70% of the data were used to train the models, while the rest were used for validation. In a second step, several metaheuristic algorithms, such as genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization for continuous domains (ACOR), and differential evolution (DE) were used to improve the model and, consequently, prediction performance. The results showed that (i) RMSE, MAPE, and NSE of 0.51 m, 0.00037 m, and 0.86, respectively, were obtained for the ANFIS model using all input variables, indicating a rather poor performance, (ii) metaheuristic algorithms were able to optimize the parameters of the ANFIS model in predicting GWL, (iii) the input pattern that included all input variables resulted in the most appropriate performance with RMSE, MAPE, and NSE of 0.28 m, 0.00019 m, and 0.97, respectively, using the ANIFS-ACOR hybrid model, (iv) results of Taylor’s diagram (CC = 0.98, STD = 0.2, and RMSD = 0.30), as well as the scatterplot (R2 = 0.97), showed that best prediction was achieved by ANFIS-ACOR, and (v) temperature and evaporation exerted stronger influence on GWL prediction than groundwater withdrawal and precipitation. The findings of this study reveal that metaheuristic algorithms can significantly improve the performance of the ANFIS model in predicting GWL.

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Section: Development Of the Anfis Using Metaheuristic Optimization Al...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Combination of Metaheuristic Optimization Algorithms and Machine Learning Methods Improves the Prediction of Groundwater Level

Kayhomayoon

Babaeian

Milan

et al. 2022

Water

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“…Kardan Moghaddam et al [18] instead used spatial clustering approaches in a combination with machine learning models to predict aquifer groundwater level. Not only the prediction task but clustering techniques were also combined with simulation and optimization methods to accurately simulate groundwater level data, see [19]. Moreover, effects of water level and precipitations on a reservoir landslide as a target variable were tested with two-way ANOVA coupled with the K-means clustering [20].…”

Section: Introductionmentioning

confidence: 99%

Hydrological Time Series Clustering: A Case Study of Telemetry Stations in Thailand

Prakaisak

Wongchaisuwat

2022

Water

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Water level data from telemetry stations typically demonstrate diverse behaviors over time. Specific characteristics can be observed among distinct station groups that are different from others. Clustering time series data into a specified number of groups based on their similarity is an initial step for further analysis in water management analytics. Our main goal in this work is to develop a clustering framework based on a combination of feature representations, feature reduction techniques, as well as clustering algorithms. Thorough experiments on multiple combinations of these methods were conducted and compared. Based on collected water level data in Thailand, UMAP reduced representations of engineered features using HAC clustering with euclidean distance outperformed other methods. Its performance reached 0.8 Fowlkes-Mallows score. Out of 81 stations, only nine unclear cases were incorrectly clustered. Distinct behaviors with abrupt and frequent fluctuations could be perfectly identified.

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Groundwater estimation of Ghayen plain with regression-based and hybrid time series models

Eslami

Nasirian

Akbarpour

et al. 2022

Paddy Water Environ

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A New Approach for Regional Groundwater Level Simulation: Clustering, Simulation, and Optimization

Cited by 22 publications

References 53 publications

A Combination of Metaheuristic Optimization Algorithms and Machine Learning Methods Improves the Prediction of Groundwater Level

A Combination of Metaheuristic Optimization Algorithms and Machine Learning Methods Improves the Prediction of Groundwater Level

Hydrological Time Series Clustering: A Case Study of Telemetry Stations in Thailand

Groundwater estimation of Ghayen plain with regression-based and hybrid time series models

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