Infilling the missing values of groundwater level using time and space series: case of Nantong City, east coast of China

He, Liang; Chen, Suozhong; Liang, Ying; Hou, Manqin; Chen, Junyi

doi:10.1007/s12145-020-00489-y

Cited by 12 publications

(2 citation statements)

References 27 publications

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“…As a result, a timely and accurate forecast of WL is critical for water resource planning and disaster risk decrease [1,2]. WL prediction is an essential responsibility of hydrologists, engineers, and relevant authorities in designing a workable conceptual design of water infrastructures and drought management measures and assessing river/lake/reservoir behaviour for operational purposes [3,4].…”

Section: Introductionmentioning

confidence: 99%

Application of Metaheuristic Algorithms and ANN Model for Univariate Water Level Forecasting

Mohammed

Zubaidi

Al‐Ansari

et al. 2023

Advances in Civil Engineering

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With the rapid development of machine learning (ML) models, the artificial neural network (ANN) is being increasingly applied for forecasting hydrological processes. However, researchers have not treated hybrid ML models in much detail. To address these issues, this study herein suggests a novel methodology to forecast the monthly water level (WL) based on multiple lags of the Tigris River in Al-Kut, Iraq, over ten years. The methodology includes preprocessing data methods, and the ANN model optimises with a marine predator algorithm (MPA). In the optimisation procedure, to decrease uncertainty and expand the predicting range, the slime mould algorithm (SMA-ANN), constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithms (CPSOCGSA-ANN), and particle swarm optimisation (PSO-ANN) are applied to compare and validate the MPA-ANN model performance. Analysis of results revealed that the data pretreatment methods improved the original data quality and selected the ideal predictors’ scenario by singular spectrum analysis and mutual information methods, respectively. For example, the correlation coefficient of the first lag improved from 0.648 to 0.938. Depending on various evaluation metrics, MPA-ANN tends to forecast WL better than SMA-ANN, PSO-ANN, and CPSOCGSA-ANN algorithms with coefficients of determination of 0.94, 0.81, 0.85, and 0.90, respectively. Evidence shows that the proposed methodology yields excellent results, with a scatter index equal to 0.002. The research outcomes represent an additional step towards evolving various hybrid ML techniques, which are valuable to practitioners wishing to forecast WL data and the management of water resources in light of environmental shifts.

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Section: Introductionmentioning

confidence: 99%

Application of Metaheuristic Algorithms and ANN Model for Univariate Water Level Forecasting

Mohammed

Zubaidi

Al‐Ansari

et al. 2023

Advances in Civil Engineering

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show abstract

“…A greater need for the development and use of water resources has been raised as a result of the intensification of global climate change, the regular occurrence of droughts, and the detrimental effects they have on social and economic growth [1,2]. Water level (WL) prediction is a crucial duty of hydrologists, engineers, and relevant authorities in establishing a sustainable conceptual design of water infrastructures, drought management strategies, providing food, and also evaluating river/lake/reservoir behavior for operational purposes [3,4].…”

Section: Introductionmentioning

confidence: 99%

Hybridisation of artificial neural network with particle swarm optimisation for water level prediction

Mohammed,

Zubaidi

2023

ejuow

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Accurate water level (WL) prediction is essential for the efficient management of various water resource projects. The creation of a reliable model for WL forecasting is still a difficult task in water resource management. This study applies an artificial neural network (ANN) integrated with the particle swarm optimisation algorithm (PSO-ANN) for simulating monthly WL of the Tigris River in Alkut City, Iraq. Data pre-treatment methods are utilised for improving raw data quality and detect the optimal predictors. Monthly WL and climatic variables from 2011 to 2020, were used to construct and validate the proposed technique. The results showed that singular spectrum analysis (SSA) is a high-performance technique for denoising time series. The PSO-ANN model produces good results coefficient of determination (R2) of 0.85.

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A comparative study on prediction of survival event of heart failure patients using machine learning algorithms

Karakuş

2022

Neural Comput & Applic

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Infilling the missing values of groundwater level using time and space series: case of Nantong City, east coast of China

Cited by 12 publications

References 27 publications

Application of Metaheuristic Algorithms and ANN Model for Univariate Water Level Forecasting

Application of Metaheuristic Algorithms and ANN Model for Univariate Water Level Forecasting

Hybridisation of artificial neural network with particle swarm optimisation for water level prediction

A comparative study on prediction of survival event of heart failure patients using machine learning algorithms

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