Magnetic data interpretation for 2D dikes by the metaheuristic bat algorithm: sustainable development cases

Essa, Khalid S.; Diab, Zein E.

doi:10.1038/s41598-022-18334-1

Cited by 21 publications

(7 citation statements)

References 72 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When bats navigate and hunt, they all have similar behaviour; but are different in weight and size. Microbats broadly use echolocation characteristic that helps them to seek prey and avoid hurdles in complete darkness 64 . Artificial bats have a velocity vector, frequency vector, and position vector in BA, updated in the period of repetitions.…”

Section: Methodsmentioning

confidence: 99%

Suspended sediment load prediction using sparrow search algorithm-based support vector machine model

Samantaray,

Sahoo,

Satapathy

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Prediction of suspended sediment load (SSL) in streams is significant in hydrological modeling and water resources engineering. Development of a consistent and accurate sediment prediction model is highly necessary due to its difficulty and complexity in practice because sediment transportation is vastly non-linear and is governed by several variables like rainfall, strength of flow, and sediment supply. Artificial intelligence (AI) approaches have become prevalent in water resource engineering to solve multifaceted problems like sediment load modelling. The present work proposes a robust model incorporating support vector machine with a novel sparrow search algorithm (SVM-SSA) to compute SSL in Tilga, Jenapur, Jaraikela and Gomlai stations in Brahmani river basin, Odisha State, India. Five different scenarios are considered for model development. Performance assessment of developed model is analyzed on basis of mean absolute error (MAE), root mean squared error (RMSE), determination coefficient (R2), and Nash–Sutcliffe efficiency (ENS). The outcomes of SVM-SSA model are compared with three hybrid models, namely SVM-BOA (Butterfly optimization algorithm), SVM-GOA (Grasshopper optimization algorithm), SVM-BA (Bat algorithm), and benchmark SVM model. The findings revealed that SVM-SSA model successfully estimates SSL with high accuracy for scenario V with sediment (3-month lag) and discharge (current time-step and 3-month lag) as input than other alternatives with RMSE = 15.5287, MAE = 15.3926, and ENS = 0.96481. The conventional SVM model performed the worst in SSL prediction. Findings of this investigation tend to claim suitability of employed approach to model SSL in rivers precisely and reliably. The prediction model guarantees the precision of the forecasted outcomes while significantly decreasing the computing time expenditure, and the precision satisfies the demands of realistic engineering applications.

show abstract

Section: Methodsmentioning

confidence: 99%

Suspended sediment load prediction using sparrow search algorithm-based support vector machine model

Samantaray,

Sahoo,

Satapathy

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Numerous studies have been conducted on developing various optimization algorithms, especially those based on natural phenomena, and their application to solve optimization problems in various fields of science and engineering (Nama et al., 2017). These algorithms have also been used to solve ill‐posed magnetic inverse problems, including Ant Colony Optimization (Liu et al., 2015; Srivastava et al., 2014), Barnacles Mating Optimization (Ai et al., 2022), Bat Optimization Algorithm (Essa & Diab, 2022), Differential Evolution (DE) (Balkaya et al., 2017; Du et al., 2021), Differential Search (Balkaya & Kaftan, 2021; Özyalın, 2023), Genetic Algorithm (Kaftan, 2017; Montesinos et al., 2016; Sohouli et al., 2022), Genetic‐Price Algorithm (GPA) (Di Maio et al., 2020), Gray Wolf Optimization (Agarwal et al., 2018), Hunger Games Search Algorithm (Ai et al., 2023), Manta Ray Foraging Optimization Algorithm (MRFO) (Ben, Ekwok, et al, 2022; Ben et al., 2021), Particle Swarm Optimization (PSO) (Ekinci et al., 2020; Ekwok et al., 2023; Liu et al., 2018; Srivastava & Agarwal, 2010), Social Spider Optimization (Ben, Akpan, et al., 2022), Whale Optimization Algorithm (WOA) (Divakar et al., 2018; Gobashy et al., 2020) and Simulated Annealing (SA) (Biswas et al., 2022; Biswas & Rao, 2021; Shinu & Dubey, 2023). The choice of the most appropriate algorithm for a given optimization problem may depend on several factors, such as the complexity of the problem, the size of the search space, the required precision, and the available computational resources (Dragoi & Dafinescu, 2021).…”

Section: Introductionmentioning

confidence: 99%

Fault Structure Reconstruction From Magnetic Anomalies Using an Improved Backtracking Search Optimization Algorithm

Balkaya,

Ekinci,

et al. 2024

Earth and Space Science

View full text Add to dashboard Cite

Fault structure reconstruction is an important aspect of magnetic prospecting as it contributes to understanding tectonic history, hazard assessment, and infrastructure planning. Metaheuristics have proven useful in estimating fault structure parameters from geophysical anomalies. Recently, a modified version of the Backtracking Search Optimization Algorithm (BSA), named mBSA, has been proposed that combines mutation steps of BSA and Differential Evolution (DE) algorithms to achieve a better balance between exploration and exploitation. Here, we present imBSA as an efficient approach that builds on the improvements of mBSA. It uses an adaptive amplitude control factor in the mutation step derived from a normally distributed random number obtained via an exponential function. The performances of these approaches were tested on some widely used benchmark functions such as Rosenbrock, Rastrigin, Griewank, and Himmelblau. Synthetic magnetic fault anomalies were evaluated with and without noise, as well as three real anomalies from the Dehri (India), Perth Basin, and Lachlan Fold Belt (Australia). Prior to the optimizations, the solvability of the model parameters was examined using some error topography images. The obtained solutions in the synthetic cases were evaluated using principal component analysis. In addition, a novel visualization option for high‐dimensional optimization problems was introduced. The experiments showed that imBSA has a faster convergence rate and better optimization statistics compared to BSA and mBSA. Therefore, it is believed to be an efficient and good alternative tool to widely used metaheuristics such as Particle Swarm Optimization and DE in explaining geophysical anomalies and optimization problems in other geoscientific disciplines.

show abstract

“…However, because of the well‐known ill‐posedness and non‐uniqueness nature of the geomagnetic data inversion problem, explanation of anomaly sources, that is, model parameter estimations, necessitate some special strategies and efficient approaches (Ekinci et al., 2019). Over the recent years, instead of derivative‐based local optimizers, derivative‐free nature‐inspired global optimizers and metaheuristics such as Particle Swarm Optimization (PSO) (Essa, Abo‐Ezz, et al., 2022; Essa & Elhussein, 2020; Fernández‐Martínez et al., 2010; Pallero et al., 2015; Roy et al., 2022; Santos, 2010), Very Fast Simulated Annealing (VFSA) (Biswas, 2016; Biswas & Acharya, 2016; Biswas & Rao, 2021), Ant Colony Optimization (Liu et al., 2014, 2015; Srivastava et al., 2014); Gray Wolf Optimizer (Agarwal et al., 2018; Chandra et al., 2017), Genetic‐Price Algorithm (Di Maio et al., 2020), Cuckoo Search Algorithm (Turan‐Karaoğlan & Göktürkler, 2021), Differential Search Algorithm (Alkan & Balkaya, 2018; A. Balkaya & Kaftan, 2021; Özyalın & Sındırgı, 2023), Bat Algorithm (Essa & Diab, 2022; Gobashy et al., 2021), Differential Evolution Algorithm (Ç. Balkaya, 2013; Du et al., 2021; Ekinci, Balkaya, & Göktürkler, 2020; Ekinci et al., 2023; Göktürkler et al., 2016; Hosseinzadeh et al., 2023; Roy et al., 2021a; Sungkono, 2020); Backtracking Search Algorithm (Ekinci, Balkaya, & Göktürkler, 2021), Manta‐Ray Foraging Optimization and Social Spider Optimization (Ben et al., 2022a, 2022b, 2022c), Barnacles Mating Optimization (BMO) (Ai et al., 2022) have gained increasing attention in geophysical inversion applications. Unlike local search algorithms, these stochastic optimizers do not need a well‐designed starting point in the model space to reach the global minimum (Sen & Stoffa, 2013; Tarantola, 2005).…”

Section: Introductionmentioning

confidence: 99%

Inversion of Geomagnetic Anomalies Caused by Ore Masses Using Hunger Games Search Algorithm

Ai,

Ekinci,

Balkaya

et al. 2023

Earth and Space Science

Self Cite

View full text Add to dashboard Cite

Geomagnetic anomaly interpretation through inversion procedures often yields useful results in determining the key details of ore masses. However, the problem is complicated due to the known ambiguous phenomena of the inversion process. Thus, details such as location, depth and shape can only be estimated using an efficient algorithm. To this end, we presented here a novel global optimization algorithm called Hunger Games Search (HGS) for the inversion of geomagnetic anomalies caused by ore masses. HGS is a well‐organized metaheuristic inspired by the hunger‐driven instincts and social behavioral decisions of animals. This is the first work in the literature to introduce this optimizer for the inversion of geophysical anomalies. We revealed the model parameter dependencies and the optimum control parameter values of the algorithm by performing some modal analyses and parameter tuning studies, respectively. The capability of HGS was demonstrated on synthetically produced geomagnetic responses and on three real anomalies obtained from some exploration fields in India and the USA. Uncertainty appraisal studies showed the solidity of the outputs of the algorithm. Moreover, some comparative studies with the fine‐tuned standard Particle Swarm Optimization, a commonly used tool for the inversion of geophysical anomalies, indicated that HGS algorithm provides better results in terms of convergence characteristics and solution stabilities in the presented inverse problem. We therefore recommend the use of this metaheuristic in model parameter estimation studies when dealing with this kind of geophysical potential field problems.

show abstract

Magnetic data interpretation for 2D dikes by the metaheuristic bat algorithm: sustainable development cases

Cited by 21 publications

References 72 publications

Suspended sediment load prediction using sparrow search algorithm-based support vector machine model

Suspended sediment load prediction using sparrow search algorithm-based support vector machine model

Fault Structure Reconstruction From Magnetic Anomalies Using an Improved Backtracking Search Optimization Algorithm

Inversion of Geomagnetic Anomalies Caused by Ore Masses Using Hunger Games Search Algorithm

Contact Info

Product

Resources

About