Application of Particle Swarm Optimization and Extreme Learning Machine Forecasting Models for Regional Groundwater Depth Using Nonlinear Prediction Models as Preprocessor

Liu, Dong; Li, Guangxuan; Fu, Qiang; Li, Mo; Faiz, Muhammad Abrar; Khan, Muhammad Imran; Li, Tianxiao; Cui, Song

doi:10.1061/(asce)he.1943-5584.0001711

Cited by 35 publications

(11 citation statements)

References 26 publications

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“…Therefore, this research paper utilized PSO to optimize the parameter set (input weights and hidden biases) of ELM to achieve better learning ability of ELM. According to literature studies, PSO with a combination of ELM models has been considered and developed in many areas with high reliability [47][48][49]; however, they have still not been considered and prepared for predicting M r values. The development of the hybrid PSO-ELM was used to design a prediction model for M r , which was compared with the hybrid PSO-ANN [23] and KELM [22] to assess the performance of the designed model.…”

Section: Hybridization (Pso-ann Pso-elm)mentioning

confidence: 99%

Particle Swarm Optimization Algorithm-Extreme Learning Machine (PSO-ELM) Model for Predicting Resilient Modulus of Stabilized Aggregate Bases

et al. 2019

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Stabilized base/subbase materials provide more structural support and durability to both flexible and rigid pavements than conventional base/subbase materials. For the design of stabilized base/subbase layers in flexible pavements, good performance in terms of resilient modulus (Mr) under wet-dry cycle conditions is required. This study focuses on the development of a Particle Swarm Optimization-based Extreme Learning Machine (PSO-ELM) to predict the performance of stabilized aggregate bases subjected to wet-dry cycles. Furthermore, the performance of the developed PSO-ELM model was compared with the Particle Swarm Optimization-based Artificial Neural Network (PSO-ANN) and Kernel ELM (KELM). The results showed that the PSO-ELM model significantly yielded higher prediction accuracy in terms of the Root Mean Square Error (RMSE), the Mean Absolute Error (MAE), and the coefficient of determination (r2) compared with the other two investigated models, PSO-ANN and KELM. The PSO-ELM was unique in that the predicted Mr values generally yielded the same distribution and trend as the observed Mr data.

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Section: Hybridization (Pso-ann Pso-elm)mentioning

confidence: 99%

Particle Swarm Optimization Algorithm-Extreme Learning Machine (PSO-ELM) Model for Predicting Resilient Modulus of Stabilized Aggregate Bases

et al. 2019

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“…e empirical mode decomposition (EMD) was combined with Elman neural network, and the coupling forecasting model was constructed to groundwater depth forecasting [15]. e EMD is also used in combination with phase space reconstruction, particle swarm optimization, and extreme learning machine [16].…”

Section: Introductionmentioning

confidence: 99%

A Grey Seasonal Index Model for Forecasting Groundwater Depth of Ningxia Plain

Yin

Zhang

2021

Discrete Dynamics in Nature and Society

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Forecasting the depth of groundwater in arid and semiarid areas is a great challenge because these areas are complex hydrogeological environments and the observational data are limited. To deal with this problem, the grey seasonal index model is proposed. The seasonal characteristics of time series were represented by indicators, and the grey model with fractional-order accumulation was employed to fit and forecast different periodic indicators and long-term trends, respectively. Then, the prediction results of the two were combined together to obtain the prediction results. To verify the model performance, the proposed model is applied to groundwater prediction in Yinchuan Plain. The results show that the fitting error of the proposed model is 2.08%, while for comparison, the fitting error of the grey model of data grouping and Holt–Winters model is 3.94% and 5%, respectively. In the same way, it is concluded that the fitting error of groundwater in Weining Plain by the proposed model is 2.26%. On the whole, the groundwater depth in Ningxia Plain including Yinchuan Plain and Weining Plain will increase further.

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“…ANNs exhibit the ability to capture short-term volatility and long-term persistence effects often seen in groundwater time series data, either explicitly through input specification or implicitly using specific architectures (Principe et al, 2000). As such, ANNs have been widely used to model groundwater level changes at individual wells (Liu et al, 2018;Nizar Shamsuddin et al, 2017;Trichakis et al, 2011;Uddameri, 2007;Nayak et al, 2006) and simultaneously at a group of wells (Mohanty et al, 2015). While forecasting water levels at individual wells is sufficient in certain groundwater applications, capturing the spatiotemporal dynamics of groundwater levels is critical for regional-scale aquifer management.…”

Section: Introductionmentioning

confidence: 99%

Physics-inspired integrated space–time artificial neural networks for regional groundwater flow modeling

Ghaseminejad

Uddameri

2020

Hydrol. Earth Syst. Sci.

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Abstract. An integrated space–time artificial neural network (ANN) model inspired by the governing groundwater flow equation was developed to test whether a single ANN is capable of modeling regional groundwater flow systems. Model-independent entropy measures and random forest (RF)-based feature selection procedures were used to identify suitable inputs for ANNs. L2 regularization, five-fold cross-validation, and an adaptive stochastic gradient descent (ADAM) algorithm led to a parsimonious ANN model for a 30 691 km2 agriculturally intensive area in the Ogallala Aquifer of Texas. The model testing at 38 independent wells during the 1956–2008 calibration period showed no overfitting issues and highlighted the model's ability to capture both the observed spatial dependence and temporal variability. The forecasting period (2009–2015) was marked by extreme climate variability in the region and served to evaluate the extrapolation capabilities of the model. While ANN models are universal interpolators, the model was able to capture the general trends and provide groundwater level estimates that were better than using historical means. Model sensitivity analysis indicated that pumping was the most sensitive process. Incorporation of spatial variability was more critical than capturing temporal persistence. The use of the standardized precipitation–evapotranspiration index (SPEI) as a surrogate for pumping was generally adequate but was unable to capture the heterogeneous groundwater extraction preferences of farmers under extreme climate conditions.

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Application of Particle Swarm Optimization and Extreme Learning Machine Forecasting Models for Regional Groundwater Depth Using Nonlinear Prediction Models as Preprocessor

Cited by 35 publications

References 26 publications

Particle Swarm Optimization Algorithm-Extreme Learning Machine (PSO-ELM) Model for Predicting Resilient Modulus of Stabilized Aggregate Bases

Particle Swarm Optimization Algorithm-Extreme Learning Machine (PSO-ELM) Model for Predicting Resilient Modulus of Stabilized Aggregate Bases

A Grey Seasonal Index Model for Forecasting Groundwater Depth of Ningxia Plain

Physics-inspired integrated space–time artificial neural networks for regional groundwater flow modeling

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