A Comparative Study of Artificial Intelligence Models and A Statistical Method for Groundwater Level Prediction

Poursaeid, Mojtaba; Poursaeid, Amir Houssain; Shabanlou, Saeid

doi:10.1007/s11269-022-03070-y

Cited by 45 publications

(14 citation statements)

References 59 publications

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“…Humans are always looking for new methods to reach their goals quickly. Since the water challenge is one of the essential human concerns (Poursaeid, Poursaeid, et al 2022), scientists are also looking for new ways to solve human problems. For this reason, AI models are one of the latest scienti c developments and the most powerful approaches to solving complex problems (Parmar and Bhardwaj 2014).…”

Section: Aim and Scopementioning

confidence: 99%

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Modeling the Water Pollutants Nonlinear-based Problems Using Optimized Intelligence Techniques and Determination of Uncertainties

Poursaeid

Poursaeed

2022

Preprint

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Along with the global population growth, the human need for safe drinking water sources has increased. With global warming, the water challenge is perhaps the most crucial challenge for the world community. At the same time, scientific methods are one of the best tools to help humanity. Considering that in many natural phenomena, it is possible to describe them based on complex relationships, it is almost impossible to solve them analytically and mathematically. Therefore, it is necessary to use methods with the ability, accuracy, and high speed to justify nonlinear relationships. One of these methods is Artificial Intelligence (AI). This research used the Extreme Learning Machine (ELM) model and Genetic Algorithm (GA) to create a new hybrid model Genetic Extreme Learning Machine (GAELM). AI and hybrid models were used to simulate and predict the water quality parameter changes. The study area in this work was the Colorado River Basin in the United States. The desired qualitative parameters were Electrical Conductivity (EC) and Dissolved Oxygen (DO). Finally, using seven approaches, the models' performance was compared. The results showed that the best simulation related to the GAELM hybrid model in the EC parameter modeling with indices RMSE and R2 equal to 0.1304, and 0.8619, respectively. Also, the ELM model was ranked in second place in accuracy. Based on the uncertainty analysis (UA-WSM) results, the GAELM(EC) model was the most accurate, with the minimum average prediction error equal to 0.01.

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Section: Aim and Scopementioning

confidence: 99%

“…Finally, it should be mentioned that EC is measured by a tool called an EC meter. According to WHO standards, the value of this index for water is less than or equal to 2500 µS/cm (Poursaeid, Poursaeid, et al 2022).…”

Section: Electrical Conductivity (Ec)mentioning

confidence: 99%

Modeling the Water Pollutants Nonlinear-based Problems Using Optimized Intelligence Techniques and Determination of Uncertainties

Poursaeid

Poursaeed

2022

Preprint

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“…It is worth mentioning that the MODFLOW models require a huge quantity of hydrological data and a long operational period [3], which has high requirements for users and also increases the problem of accurate simulations. In the last 20 years, artificial intelligence models have been widely applied to overcome the constraints of classical numerical models for GWL simulation [16]. Support vector machine (SVM) [17], artificial neural network (ANN) [18], random forest (RF) [19], and adaptive neuro-fuzzy inference systems [20] are ML techniques that have been utilised in studies to forecast GWL.…”

Section: Introductionmentioning

confidence: 99%

Climate change impact assessment on groundwater level changes: A study of hybrid model techniques

Afrifa

Zhang

Xin

et al. 2023

IET Signal Processing

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One of the most important sources of water supply is groundwater. However, the groundwater level (GWL) is significantly impacted by the global climate change. Therefore, under these more severe climate change conditions, the accurate and simple forecast of farmland GWL is a crucial component of agricultural water management. A hybrid model (HM) of Bayesian random forest (BRF), Bayesian support vector machine (BSVM), and Bayesian artificial neural network (BANN) is built in this study. The HM is made up of a Bayesian model averaging (BMA) and three machine learning models: random forest (RF), support vector machine (SVM), and artificial neural network. These three HMs are employed to help automate logical inference and decision‐making in business intelligence for groundwater management. For this purpose, data on 8 separate climatic factors that impact GWL changes in the study area were acquired. Nine distinct farming communities' GWL change data were utilised as the dependent variables for each model fit (community data). The effectiveness of the HM techniques was assessed using the evaluation metrics of mean absolute error (MAE), coefficient of determination (R2), mean absolute percent error (MAPE), and root mean square error (RMSE). The model fit in Suhum had the greatest performance with the highest accuracy (R2 varied from 0.9051 to 0.9679) and the lowest error scores (RMSE ranged from 0.0653 to 0.0727, and MAE ranged from 0.0121 to 0.0541), according to the models' evaluation results. The BRF delivered the greatest results when compared to the two independent HMs, the BSVM and BANN. Future GWL and climatic variable data may be trained using the trained HM techniques to determine the effects of climate change. Farmers, businesses, and civil society organisations might benefit from continuous monitoring of GWL data and education on climate change to help control and prevent excessive deteriorations of global climate change on GWL.

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“…Machine learning-based methods, compared to numerical models, are accurate and capable of forecasting various variables despite their cost-effectiveness (Azari et al, 2021;Fatemi & Parvini, 2022;Zeynoddin et al, 2018). Some of the other research carried out on this topic within recent years is Poursaeid et al (2021Poursaeid et al ( , 2020; Yosefvand & Shabanlou (2020); Malekzadeh et al (2019); Azizpour et al (2021Azizpour et al ( , 2022; Poursaeid et al (2022); Zeinali et al (2020aZeinali et al ( , 2020b; and Bayesteh & Azari (2021).…”

Section: Introductionmentioning

confidence: 99%

Modelling and prediction of groundwater level using wavelet transform and machine learning methods: A case study for the Sahneh Plain, Iran

Azizi

Yosefvand

Yaghoubi

et al. 2023

Irrigation and Drainage

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Due to the complexity of numerical models and the need for much information and data in these models, one of the important solutions is to use artificial intelligence models with a small number of required inputs and a simpler structure. In this study, using wavelet transform analysis (cross wavelet transform and wavelet transform coherence), a model was developed to extract the relationship between climatic data (i.e., temperature and precipitation) and groundwater level changes. The developed model provides the best relationship between the main effective climatic parameters and groundwater level changes in the Sahneh Plain. The results showed that the climatic variables of average temperature and precipitation are the parameters with the highest correlation coefficient with groundwater level changes in the main wells of the study area. The results of verification and validation of the numerical prediction model of groundwater level based on the climatic parameters indicate that the average error is between 3% and 10% in different areas of the plain, which indicates the high accuracy of the developed model. In areas with a lack of data and information required for numerical simulation, the proposed model used in this research can be used as a comprehensive model for assessing and predicting groundwater levels based on climatic parameters.

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A Comparative Study of Artificial Intelligence Models and A Statistical Method for Groundwater Level Prediction

Cited by 45 publications

References 59 publications

Modeling the Water Pollutants Nonlinear-based Problems Using Optimized Intelligence Techniques and Determination of Uncertainties

Modeling the Water Pollutants Nonlinear-based Problems Using Optimized Intelligence Techniques and Determination of Uncertainties

Climate change impact assessment on groundwater level changes: A study of hybrid model techniques

Modelling and prediction of groundwater level using wavelet transform and machine learning methods: A case study for the Sahneh Plain, Iran

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