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

Mohammed, Sarah Jasim; Zubaidi, Salah L.; Al‐Ansari, Nadhir; Ridha, Hussein Mohammed; Dulaimi, Anmar; Al-Khafaji, Ruqayah

doi:10.1155/2023/9947603

Cited by 4 publications

(1 citation statement)

References 72 publications

(88 reference statements)

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“…However, SVM's performance is closely related to kernel parameters and penalty factors [12], which, to some extent, limits its application. Particle swarm optimization (PSO) has a fast convergence rate and fewer hyperparameters, making it a popular choice for optimizing hyperparameters [13][14][15][16]. For example, Jia et al [17] combined PSO with locally supported SVM and used PSO to optimize parameters for landslide prediction.…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization of Support Vector Machine Inversion Model for Overhead Upright Piers Damage-Inducing Factor

Zhou,

Tang,

et al. 2023

Advances in Civil Engineering

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In the Three Gorges reservoir area, the overhead upright pier is the primary structural form. For intelligent monitoring of existing terminals, this research chooses Chongqing Xintian Port as the study object and proposes a support vector machine (SVM) damage-inducing factor (DIF) inversion model based on particle swarm optimization (PSO). To apply the finite element method to analyze the stress distribution characteristics of quay pile groups under three main DIFs, including the stacking effect, ship impact load effect, and bank slope effect. After characterizing the stress data, it becomes evident that there exists a correlation between stress and each DIF parameter. Before generating the training sample set, principal component analysis is employed to reduce dimensionality and eliminate a substantial amount of redundant data. The model has an accuracy of 0.999 for the identification of the type of DIF and 0.975 for the identification of the location of the action of the DIF with F1 coefficients of 0.999 and 0.978, respectively. For the strength of DIF predictions, MAE and MSE were 4.871 and 1.202, respectively, R2 was 0.986, NSE was 0.986, WI was 0.996, and PBIAS was 0.095. After extracting every sample, the relative error for the ship impact load effect is 0.05, and the highest relative error for the bank slope effect is 0.02; the error for the stacking effect is limited to 0.08. The results suggest that the damage inducement inversion model of the SVM optimized by the PSO algorithm can effectively identify the DIF of the overhead upright pier.

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Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization of Support Vector Machine Inversion Model for Overhead Upright Piers Damage-Inducing Factor

Zhou,

Tang,

et al. 2023

Advances in Civil Engineering

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Groundwater Level Modeling Using Multiobjective Optimization with Hybrid Artificial Intelligence Methods

Banadkooki,

Haghighi

2023

Environ Model Assess

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Estimating groundwater level (GWL) uctuations is essential for integrated water resource management in arid and semi-arid regions. This study promotes the multi-layer perceptron (MLP) learning process using hybrid evolutionary algorithms. This hybrid metaheuristic algorithm was applied to overcome MLP di culties in the learning process, including its low conversions and local minimum. Also, the hybrid model bene ts from the advantages of two objective function procedures in nding MLP parameters that result in a robust model regardless of over and under-estimating problems. These algorithms include none dominated sorting genetic algorithm (NSGA II) and multi-objective particle swarm optimisation (MOPSO) in different patterns, including MLP-NSGA-II, MLP-MOPSO, MLP-MOPSO-NSGA-II, and MLP-2NSGA-II-MOPSO. Temperature, precipitation and GWL datasets were used in various combinations and delays as model input candidates. Finally, the best model inputs were selected using the correlation coe cient (R 2 ). Input parameters include temperature and precipitation delays of 3, 6, and 9 months and GWL delays of 1 to 12 months. In the next step, the performance of the different combinations of MLP and hybrid evolutionary algorithms was evaluated using The root mean square error (RMSE), correlation coe cient (R), and mean absolute error (MAE) indices. The outcomes of these evaluations revealed that the MLP-2NSGA-II-MOPSO model, with an RMSE=0.073, R=0.98, and MAE=0.059, outperforms other models in estimating GWL uctuations. The selected model bene ts from the advantages of both MOPSO and NSGA-II regarding accuracy and speed. The results also indicated the superiority of multi-objective optimization algorithms in promoting MLP performance. quanti cation for better management along with environmental protection (Li et al., 2015;Noori et al., 2021;Torabi Haghighi et al., 2020). Furthermore, precise GWL modeling can optimize the monitoring of GWL, which has time and resource limitations (Kazemi et al., 2021) and needs signi cant in-situ measurements (Lee et al., 2019) and interpretation of data quality.The GWL uctuations can be due to (i) climate change and variability and (ii) anthropogenic activity, i.e., GW exploitation (Malekinezhad & Banadkooki, 2018). Therefore, GWL modelling needs deep analysis, extensive and reliable data sources, and an understanding of interactions between different environmental parameters (Liang & Zhang, 2015). In recent decades many attempts have been made to develop accurate Page 3/31 and robust models to predict GWL uctuations properly. These attempts resulted in a variety of GWL models that can be categorized into four groups, (i) the univariate time series models (Khorasani et al.,

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Hybrid deep learning models for multi-ahead river water level forecasting

Kashem,

Das,

Hasan

et al. 2024

Earth Sci Inform

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

Cited by 4 publications

References 72 publications

Particle Swarm Optimization of Support Vector Machine Inversion Model for Overhead Upright Piers Damage-Inducing Factor

Particle Swarm Optimization of Support Vector Machine Inversion Model for Overhead Upright Piers Damage-Inducing Factor

Groundwater Level Modeling Using Multiobjective Optimization with Hybrid Artificial Intelligence Methods

Hybrid deep learning models for multi-ahead river water level forecasting

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