Soil salinity prediction using hybrid machine learning and remote sensing in Ben Tre province on Vietnam’s Mekong River Delta

Nguyen, Huu Duy; Van, Chien Pham; Nguyen, Tien Giang; Dang, Dinh Kha; Phạm, Tiến Sơn; Nguyen, Quoc Hung; Bui, Quang‐Thanh

doi:10.1007/s11356-023-27516-x

Cited by 4 publications

(2 citation statements)

References 91 publications

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“…In order to explore the effect of combining intelligent optimization algorithms with traditional machine learning models for inversion of SSC, there have been scholars combining the two for inversion of SSC, using intelligent optimization algorithms such as GA [26], seagull optimization algorithm (SOA) [49], sparrow search algorithm (SSA), bird swarm algorithm (BSA), moth search algorithm (MSA), Harris hawk optimization algorithm (HHO), grasshopper optimization algorithm (GOA), particle swarm optimization algorithm (PSO) [50], and so on. In this paper, on the basis of the previous studies, using measured SSC and different combinations of spectral indices as modeling input, we improve the crayfish optimization algorithm based on the one proposed in 2023 and combine the improved optimization algorithm with the RELM model to train the SSC inverse model.…”

Section: Discussionmentioning

confidence: 99%

Soil Salinity Inversion in Yellow River Delta by Regularized Extreme Learning Machine Based on ICOA

Wang,

Zhang

et al. 2024

Remote Sensing

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Soil salinization has seriously affected agricultural production and ecological balance in the Yellow River Delta region. Rapid and accurate monitoring of soil salinity has become an urgent need. Traditional machine learning models tend to fall into local optimal values during the learning process, which reduces their accuracy. This paper introduces Circle map to enhance the crayfish optimization algorithm (COA), which is then integrated with the regularized extreme learning machine (RELM) model, aiming to improve the accuracy of soil salinity content (SSC) inversion in the Yellow River Delta region. We employed Landsat5 TM remote sensing images and measured salinity data to develop spectral indices, such as the band index, salinity index, vegetation index, and comprehensive index, selecting the optimal modeling variable group through Pearson correlation analysis and variable projection importance analysis. The back propagation neural network (BPNN), RELM, and improved crayfish optimization algorithm–regularized extreme learning machine (ICOA-RELM) models were constructed using measured data and selected variable groups for SSC inversion. The results indicate that the ICOA-RELM model enhances the R2 value by an average of about 0.1 compared to other models, particularly those using groups of variables filtered by variable projection importance analysis as input variables, which showed the best inversion effect (test set R2 value of 0.75, MAE of 0.198, RMSE of 0.249). The SSC inversion results indicate a higher salinization degree in the coastal regions of the Yellow River Delta and a lower degree in the inland areas, with moderate saline soil and severe saline soil comprising 48.69% of the total area. These results are consistent with the actual sampling results, which verify the practicability of the model. This paper’s methods and findings introduce an innovative and practical tool for monitoring and managing salinized soils in the Yellow River Delta, offering significant theoretical and practical benefits.

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Section: Discussionmentioning

confidence: 99%

Soil Salinity Inversion in Yellow River Delta by Regularized Extreme Learning Machine Based on ICOA

Wang,

Zhang

et al. 2024

Remote Sensing

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“…The number of input variables and optimal salinity prediction models need to be decided based on their importance assessments and the application of robust ML algorithms [12]. In Vietnam, some studies have applied the Landsat-8 OLI data in the GEE platform and ML algorithms in predicting the salinity intrusion in the Mekong Delta in recent years [13][14][15][16]. However, these studies have not used the BMA algorithm, evaluated the importance of input variables, or mentioned the number of optimal salinity prediction models.…”

Section: Introductionmentioning

confidence: 99%

Application of the bayesian model averaging algorithm in evaluating and selecting optimal salinity prediction models

Quynh,

Hang,

Hieu

et al. 2023

JSTCE

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Salinity intrusion poses significant challenges to coastal regions worldwide. Reliable salinity prediction models can provide valuable information to mitigate the impact and influence of salinity intrusion. However, their accuracy relies mainly on the selected input variables and used optimal models. This study employs the BayesianModel Averaging (BMA) algorithm to evaluate input variable importance and select the most reliable salinityprediction model. Based on an analysis of observed salinity data and climate data extracted from Landsat 8 OLI in the Google Earth Engine platform, the BMA algorithm identifies the significance of critical variables andoptimal salinity prediction models. Various statistical metrics, including R-squared, Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) from the Random Forest method, were used to verify the performance of these optimal salinity prediction models. These obtained results offer foundational knowledge and valuable insights for future studies in determining appropriate input variables and selecting the best optimalsalinity prediction model.

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Nonlinear crossing strategy-based particle swarm optimizations with time-varying acceleration coefficients

Watanabe,

2024

Appl Intell

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In contemporary particle swarm optimization (PSO) algorithms, to efficiently explore global optimum solutions, it is common practice to set the inertia weight to monotonically decrease over time for stability, while allowing the two acceleration coefficients, representing cognitive and social factors, to adopt decreasing or increasing functions over time, including random variations. However, there has been little discussion on a unified design approach for these time-varying acceleration coefficients. This paper presents a unified methodology for designing monotonic decreasing or increasing functions to construct nonlinear time-varying inertia weight and two acceleration coefficients in PSO, along with a control strategy for exploring global optimum solutions. We first construct time-varying coefficients by linearly amplifying well-posed monotonic functions that decrease or increase over normalized time. Here, well-posed functions ensure satisfaction of specified conditions at the initial and terminal points of the search process. However, many of the functions employed thus far only satisfy well-posedness at either the initial or terminal points of the search time, prompting the proposal of a method to adjust them to virtually meet specified initial or terminal points. Furthermore, we propose a crossing strategy where the developed cognitive and social acceleration coefficients intersect within the search time interval, effectively guiding the search process by pre-determining crossing values and times. The performance of our Nonlinear Crossing Strategy-based Particle Swarm Optimization (NCS-PSO) is evaluated using the CEC2014 (Congress on Evolutionary Computation in 2014) benchmark functions. Through comprehensive numerical comparisons and statistical analyses, we demonstrate the superiority of our approach over seven conventional algorithms. Additionally, we validate our approach, particularly in a drone navigation scenario, through an example of optimal 3D path planning. These contributions advance the field of PSO optimization techniques, providing a robust approach to addressing complex optimization problems.

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Soil salinity prediction using hybrid machine learning and remote sensing in Ben Tre province on Vietnam’s Mekong River Delta

Cited by 4 publications

References 91 publications

Soil Salinity Inversion in Yellow River Delta by Regularized Extreme Learning Machine Based on ICOA

Soil Salinity Inversion in Yellow River Delta by Regularized Extreme Learning Machine Based on ICOA

Application of the bayesian model averaging algorithm in evaluating and selecting optimal salinity prediction models

Nonlinear crossing strategy-based particle swarm optimizations with time-varying acceleration coefficients

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