Teaching Learning Based Optimization Research Articles

Intelligent optimization algorithm-driven informational teaching model for English reading and writing in universities

English reading and writing are important parts of language teaching. In order to improve the English reading and writing ability of college students, the TLBO (teaching learning-based optimization) algorithm is used in this research to improve the way that English reading and writing are taught in colleges and universities. It is chosen as the primary model for this study. The TLBO algorithm is further optimized in this paper, and a convergence analysis is performed between the optimized model M-TLBO (multi-learning teaching learning-based optimization) algorithm and other TLBO algorithms in order to address the issues that the TLBO algorithm has an excessively single teaching ability and readily settles into local optimal solutions for some large-scale complex problems. In terms of stability and convergence accuracy, M-TLBO outperforms other algorithms. In order to investigate the impact of the M-TLBO algorithm on students’ writing performance, this paper uses the teaching-learning optimization algorithm to conduct a pre-and post-test on students’ English reading and writing performance in five dimensions. The study’s findings revealed that students’ pre-test writing scores had a mean value of 8.4770 and a standard deviation of 1.72449, and that their post-study writing scores had increased by 5.05 points. The English reading and writing information-based teaching model can improve students’ English writing performance. It is hoped to promote the development of English teaching and improve the efficiency of students’ English learning.

Integrated neural network and evolutionary algorithm approach for liver fibrosis staging: Can artificial intelligence reduce patient costs?

Staging liver fibrosis is important, and liver biopsy is the gold standard diagnostic tool. We aim to design and evaluate an artificial neural network (ANN) method by taking advantage of the Teaching Learning-Based Optimization (TLBO) algorithm for the prediction of liver fibrosis stage in blood donors and hepatitis C patients. We propose a method based on a selection of machine learning classification methods including multilayer perceptron (MLP) neural network, Naive Bayesian (NB), decision tree, and deep learning. Initially, the synthetic minority oversampling technique (SMOTE) is performed to address the imbalance in the dataset. Afterward, the integration of MLP and TLBO is implemented. We propose a novel algorithm that reduces the number of required patient features to seven inputs. The accuracy of MLP using 12 features is 0.903, while that of the proposed MLP with TLBO is 0.891. Besides, the diagnostic accuracy of all methods, except the model designed with the Bayesian network, increases when the SMOTE balancer is applied. The decision tree-based deep learning methods show the highest levels of accuracy with 12 features. Interestingly, with the use of TLBO and seven features, MLP reached an accuracy rate of 0.891, which is quite satisfactory when compared with those of similar studies. The proposed model provides high diagnostic accuracy, while reducing the required number of properties from the samples. The results of our study show that the recruited algorithm of our study is more straightforward, with a smaller number of required properties and similar accuracy.

A metaheuristic algorithm for regulating virtual inertia of a standalone microgrid incorporating electric vehicles

AbstractModern electrical networks, particularly microgrids, have seen a sharp rise in acquiring non‐conventional sources. The inertia of the microgrid decreases drastically because RESs have been used in place of traditional synchronous generators. The reduced inertia negatively impacts the dynamics and performance of the microgrid with RESs, which decreases the microgrid's stability, especially in operation on an island. The primary purpose of this research study is to enhance the dynamic security of an island microgrid by merging an electric vehicle with a frequency control technique based on virtual inertia control. A Proportional Integral Derivative Filter Constant (PIDFN) controller optimally created using the Modified Differential Evolution (MDE) method served as the foundation for control in the virtual inertia control loop. The effectiveness of the MDE‐based PIDFN controller was examined considering the diverse operational scenarios are compared and contrasted with those of traditional methods Differential Evolution (DE) and Teaching Learning Based Optimization (TLBO)‐based PIDFN controllers. Real‐time wind and solar power statistics and random load fluctuations were incorporated to provide realistic simulation settings. The outcomes demonstrate that the MDE‐based PIDFN controller performs better in reference frequency tracking and reducing frequency disturbances than the other optimization strategies.

The Hiking Optimization Algorithm: A novel human-based metaheuristic approach

In this paper, a novel metaheuristic called ‘The Hiking Optimization Algorithm’ (HOA) is proposed. HOA is inspired by hiking, a popular recreational activity, in recognition of the similarity between the search landscapes of optimization problems and the mountainous terrains traversed by hikers. HOA’s mathematical model is premised on Tobler’s Hiking Function (THF), which determines the walking velocity of hikers (i.e. agents) by considering the elevation of the terrain and the distance covered. THF is employed in determining hikers’ positions in the course of solving an optimization problem. HOA’s performance is demonstrated by benchmarking with 29 well-known test functions (including unimodal, multimodal, fixed-dimension multimodal, and composite functions), three engineering design problems (EDPs), (including I-beam, tension/compression spring, and gear train problems) and two N-P Hard problems (i.e. Traveling Salesman’s and Knapsack Problems). Moreover, HOA’s results are verified by comparison to 14 other metaheuristics, including Teaching Learning Based Optimization (TLBO), Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization, Grey Wolf Optimizer (GWO) as well as newly introduced algorithms such as Komodo Mlipir Algorithm (KMA), Quadratic Interpolation Optimization (QIO), and Coronavirus Optimization Algorithm (COVIDOA). In this study, we employ statistical tests such as the Wilcoxon rank sum, Friedman test, and Dunn’s post hoc test for the performance evaluation. HOA’s results are competitive and, in many instances, outperform the aforementioned well-known metaheuristics.

A Novel Solution for the Power Transmission Congestion of Deregulated Power System Using TCSC and TLBO Algorithm

The expansion of the electrical power sector has introduced heightened competition in electric markets, compelling utilities to enhance the efficiency of their power system equipment to provide customers with cost-effective and premium-quality power. Deregulation has, however, led to challenges such as congestion in transmission lines, adversely affecting power quality, voltage profiles, and system security. The congestion can be alleviated either by technical or non-technical methods. To address this issue, the manuscript proposes the implementation of Thyristor Control Series Capacitors (TCSC) as a technical solution for congestion mitigation. TCSC is used as a variable reactance device. The study employs line utilization factors (LUF) and disparity line utilization factors (DLUF) to obtain the optimal locations for TCSC installation. Additionally, the Teaching-Learning-Based Optimization (TLBO) algorithm is employed for the optimization of TCSC reactance. The manuscript also suggests another approach by simultaneously optimizing the location and parameters of multiple TCSCs using the TLBO algorithm without using the sensitivity factors. Comparative analyses with other optimization algorithms like Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) are conducted to determine the superior method for ensuring stable system operation. By application on the standard IEEE-30 bus system, the TLBO algorithm is validated, demonstrating its effectiveness in mitigating congestion by minimizing power loss, and voltage deviation, and enhancing security margins. The findings contribute valuable insights for practitioners seeking robust solutions in managing congestion within deregulated electric markets.

Optimum Cropping Pattern in the Command Area of Nyari-2 Reservoir Using Teaching Learning-Based Optimization Algorithm

Optimum Cropping Pattern in the Command Area of Nyari-2 Reservoir Using Teaching Learning-Based Optimization Algorithm

Energy efficiency security in urban areas: Challenges and implementation

In this paper, we explore the transformative potential of data-driven methodologies in the intricate landscape of urban energy transactions, with a keen focus on enhancing decision-making intelligence, amplifying the integration of renewable energy sources, and fostering active community engagement. A pivotal contribution to this research lies in the introduction of an innovative Intrusion Detection System (IDS) fortified by the robust capabilities of deep evolving convolutional Generative Adversarial Networks (GANs). This novel security measure is designed to fortify urban energy transactions against potential cyber threats, ensuring the integrity and reliability of the energy infrastructure. Furthermore, our study introduces a groundbreaking hybridization of the Bat Algorithm (BA) and Teaching-Learning-Based Optimization (TLBO) for the optimization of GAN model training. This fusion of optimization techniques enhances the efficiency and accuracy of the training process, elevating the reliability of the generated models within the context of urban energy management. To validate and demonstrate the practical applicability of these innovations, we deploy them on the real-time dataset of a smart city's digital twin. This implementation serves as a tangible proof of concept, offering valuable insights into the performance and adaptability of the proposed approaches within real-world urban energy scenarios. As the findings unfold, it becomes evident that our research significantly contributes to the evolution of urban energy management, providing a robust foundation for the development of secure, resilient, and community-centric smart city energy systems. Through these advancements, we pave the way for a sustainable urban future where energy transactions are not only efficient but also secure and inclusive, aligning with the broader goals of smart city development.

Optimizing the series cascade control structure for nonminimum phase system regulation

AbstractThis work elucidates the control of integrating a nonminimum phase system via a series cascade scheme with fractional‐order P.I. (Proportional–Integral) plus D (Derivative) controller. The traditional Internal Model Control (IMC) is adopted for inner loop controller design. The feedback D controller is synthesized with the outer loop process model, showing the proposed work's universality. The outer loop controller is suggested in the IMC framework after the accountability of fractional‐filter and inverse response compensator. This combination is revealed to enhance performance without compromising robustness. The Riemann sheet principle is explored to compute the stability of the suggested controller. The sensitivity analysis has asserted the robustness. More importantly, the optimal value of controller settings is achieved via the Teaching Learning Based Optimization (TLBO) algorithm. This TLBO algorithm uses an objective function that minimizes Integral Square Error. Two illustrative problems are utilized to examine the recommended control structure's virtue.

Advanced hybrid algorithms for precise multipath channel estimation in next-generation wireless networks

Multipath channels continue to present challenges in wireless communication for both 5G and 6G networks. A multipath channel is a phenomenon in wireless communications where signals traverse from the sender to the receiver along various paths. This end occurs due to the reflection, diffraction, and refraction of signals of various objects and structures in the environment. Such pathways can cause symbol interference in the transmitted signal, leading to communication issues. To this end, our paper proposes the integration of three algorithms: teaching-learning-based optimization (TLBO), particle swarm optimization (PSO), and artificial neural networks (ANN). This combination effectively analyzes and stabilizes the transmission channel, minimizing symbol interference. We have developed, simulated, and evaluated this hybrid approach for multipath fading channels. We apply it to various coding schemes, including tail-biting convolutional code, turbo codes, low-density parity-check, and polar code. Additionally, we have explored various decoding methods such as Viterbi, maximum logarithmic maximum a posteriori, minimum sum, and cyclic redundancy check soft cancellation list. Our study encompasses new channel equalization schemes and coding gains derived from simulations and mathematical analysis. Our proposed method significantly enhances channel equalization, reducing interference and improving error correction in wireless communication systems.

Optimal proportional-integral speed control for closed-loop engine timing system

In internal combustion engines, adjusting the air-fuel ratio is essential to control the speed and minimize the burnt fuel. The throttle opening is the actuator to control the air-fuel. A better design for the used/conventional controller can give a better response without additional cost. In this work, the proposed controller gains of the proportional-integral (PI) controller are tuned to enhance the speed in constant and variable drive cycle modes. The tuning process is conducted based on two of the most efficient performance indices used in this field. The performance indices are integral absolute error (IAE) and integral time absolute error (ITAE). The optimization problem is solved using three reliable stochastic optimization algorithms to ensure mature convergence of the solutions, to avoid local optima solutions, and to ensure effective shrinking of the search space. The optimization algorithms are teaching-learning-based optimization (TLBO), particle swarm optimization (PSO), and genetic algorithm (GA). Different simulations are conducted to validate the results. The results are compared with conventional tuning methods regarding the system's time response.

Robust Adaptive Control of the Offshore Produced Water Treatment Process: An Improved Multivariable MRAC-Based Approach

The application of deoiling hydrocyclone systems as the downstream of three-phase gravity separator (TPGS) systems is one of the most commonly deployed produced water treatment processes in offshore oil and gas production. Due to the compact system’s complexity and tailor-made features, it is always challenging to develop some optimally coordinated control solution for the coupled hydrocyclone and TPGS systems. It is obvious that coordinated control can better fulfill legislative discharge regulation by robustly maintaining high separation efficiencies. This paper presents a new control solution for a set of integrated hydrocyclone and TPGS systems by applying an improved multi-variable model reference adaptive control (MV-MRAC) approach with the aim of achieving both asymptotic output tracking and unknown disturbance rejection. A robust MV-MRAC controller design is proposed based on a control parameterization derived from a factorization of a high-frequency gain matrix Kp=LDS as a product of three matrices, where L represents unity lower triangular, D=sign(D) represents diagonal, and S represents positive definite, and a teaching–learning-based optimization (TLBO) algorithm for optimizing the adaption rates. The developed solution is analyzed and compared with a commonly deployed PI control solution on a model that is derived from a lab-scale produced water treatment process. This simulation study demonstrates the promising potential of the proposed control solution compared with the currently deployed PI control solution.

Reinforcement Learning-Based Multi-Objective Optimization for Generation Scheduling in Power Systems

Multi-objective power scheduling (MOPS) aims to address the simultaneous minimization of economic costs and different types of environmental emissions during electricity generation. Recognizing it as an NP-hard problem, this article proposes a novel multi-agent deep reinforcement learning (MADRL)-based optimization algorithm. Within a custom multi-agent simulation environment, representing power-generating units as collaborative types of reinforcement learning (RL) agents, the MOPS problem is decomposed into sequential Markov decision processes (MDPs). The MDPs are then utilized for training an MADRL model, which subsequently offers the optimal solution to the optimization problem. The practical viability of the proposed method is evaluated across several experimental test systems consisting of up to 100 units featuring bi-objective and tri-objective problems. The results demonstrate that the proposed MADRL algorithm has better performance compared to established methods, such as teaching learning-based optimization (TLBO), real coded grey wolf optimization (RCGWO), evolutionary algorithm based on decomposition (EAD), non-dominated sorting algorithm II (NSGA-II), and non-dominated sorting algorithm III (NSGA-III).

Optimizing Chemical Reaction Mechanisms: Evaluating Parameter-Free Metaheuristic Algorithms and Gradient-Based Optimization

ABSTRACT In the present work, we compare the performance of four different metaheuristic algorithms in reaction mechanism optimization problems. Mechanism optimization is an essential step to develop detailed and skeletal reaction mechanisms. Stochastic metaheuristic algorithms that mimic biological evolution, such as the genetic algorithm (GA), are often used for this purpose. While this technique has proven effective over a broad class of problems, it requires the specification of many hyperparameters or user-defined inputs, such as the crossover rate, mutation rate, offspring selection criterion, among others. In the present work, we show that extensive knowledge about the underlying system is needed to ensure efficient performance from the GA. Particle swarm optimization (PSO) is another metaheuristic technique that has gained popularity in the chemical kinetics community. However, it also suffers from sensitive dependence on the input parameters. The performance of two parameter-free algorithms, the Teaching-Learning-Based Optimization (TLBO) and the Artificial Bee Colony (ABC) algorithm are compared against GA and PSO. The TLBO and ABC algorithms have not been tested previously in reaction mechanism optimization problems to the best knowledge of the authors. We show that TLBO provides comparable or superior performance than other optimization strategies in terms of fitness value, computational time, recovery of the original coefficients, and the prediction of the species mole fractions. Additionally, as the method is hyperparameter-free, the sensitive dependence on user-defined input is eliminated. In the last section of the study, the performance of a gradient-based optimization algorithm (Adam) is compared against the metaheuristic algorithms. The performance of Adam is sensitive to the learning rate, which is a hyperparameter for the algorithm. The effectiveness of TLBO method for the optimization of reaction mechanisms is established.

Optimizing performance assessment of multi-reservoir operations for sustainable water management in a semi-arid region

Abstract Global challenges, such as population growth, rapid urbanization, and the impacts of climate change, are creating unprecedented demands on water resources in semi-arid regions. Meeting the surging needs for irrigation and water supply requires a departure from the limitations of single reservoir systems. Instead, the construction of multi-reservoir systems within semi-arid river basins is imperative. The research employs an integrated reservoir operation approach that facilitates the controlled release of surplus water from upstream reservoirs to downstream ones. The novel Teaching-Learning-Based Optimization (TLBO) model is utilized to determine optimal irrigation releases, subsequently forming the basis for evaluating reservoir operation performance through the lenses of reliability, resilience, and vulnerability. The findings of this study shed light on the performance of these reservoirs under different models. Notably, the Aji-2 reservoir operation exhibits higher levels of reliability and resilience when the TLBO model is employed, surpassing the outcomes of the Linear Programming (LP) model. Conversely, the vulnerability of the Aji-3 reservoir operation is more pronounced with the TLBO model, albeit reduced when compared to actual release years 2005, 2009, and 2013. This study adds to the development of reservoir operation policies that favor reduced vulnerability and increased reliability.

Forecasting of energy-related carbon dioxide emission using ANN combined with hybrid metaheuristic optimization algorithms

Energy-related CO2 emissions are one of the biggest concerns facing urban design today, increasing rapidly as cities grow. This study uses as inputs the GDP of the G8 nations (from 1990 to 2016) depending on the utilization of various energy sources, including coal, oil, natural gas, and renewable energy. Multilayer perceptrons (MLP) are combined with various nature-inspired optimization algorithms, such as Heap-Based Optimizer (HBO), Teaching-Learning-Based Optimization (TLBO), Whale Optimization Algorithm (WOA), Vortex Search algorithm (VS), and Earthworm Optimization Algorithm (EWA), to create a dependable predictive network that takes the complexity of the problem into account. Our key contributions lie in developing and comprehensively evaluating these hybrid models assessing their efficacy in capturing the intricate dynamics of carbon emissions. The study found that TLBO and VS outperform other algorithms in CO2 emission computation accuracy. TLBO has a higher training MSE (3.6778) and lower testing MSE (4.4673), suggesting larger squared errors on training data and lower testing MSE, suggesting less overfitting due to better generalization to the testing set.

An enhanced and efficient approach for feature selection for chronic human disease prediction: A breast cancer study

Computer-aided diagnosis (CAD) systems play a vital role in modern research by effectively minimizing both time and costs. These systems support healthcare professionals like radiologists in their decision-making process by efficiently detecting abnormalities as well as offering accurate and dependable information. These systems heavily depend on the efficient selection of features to accurately categorize high-dimensional biological data. These features can subsequently assist in the diagnosis of related medical conditions. The task of identifying patterns in biomedical data can be quite challenging due to the presence of numerous irrelevant or redundant features. Therefore, it is crucial to propose and then utilize a feature selection (FS) process in order to eliminate these features. The primary goal of FS approaches is to improve the accuracy of classification by eliminating features that are irrelevant or less informative. The FS phase plays a critical role in attaining optimal results in machine learning (ML)-driven CAD systems. The effectiveness of ML models can be significantly enhanced by incorporating efficient features during the training phase. This empirical study presents a methodology for the classification of biomedical data using the FS technique. The proposed approach incorporates three soft computing-based optimization algorithms, namely Teaching Learning-Based Optimization (TLBO), Elephant Herding Optimization (EHO), and a proposed hybrid algorithm of these two. These algorithms were previously employed; however, their effectiveness in addressing FS issues in predicting human diseases has not been investigated. The following evaluation focuses on the categorization of benign and malignant tumours using the publicly available Wisconsin Diagnostic Breast Cancer (WDBC) benchmark dataset. The five-fold cross-validation technique is employed to mitigate the risk of over-fitting. The evaluation of the proposed approach's proficiency is determined based on several metrics, including sensitivity, specificity, precision, accuracy, area under the receiver-operating characteristic curve (AUC), and F1-score. The best value of accuracy computed through the suggested approach is 97.96%. The proposed clinical decision support system demonstrates a highly favourable classification performance outcome, making it a valuable tool for medical practitioners to utilize as a secondary opinion and reducing the overburden of expert medical practitioners.

Modeling of flexible AC transmission system devices and fuzzy controller for automatic generation control of electric vehicle-injected power system

The automatic generation control (AGC) is a paradigm topic of interest that is continuously evolving for adequate power generation control in the power system. In this proposed study, a fuzzy adapted PID (FPID) controller is assigned for the AGC of the hybrid power system in various electrical risks and hazards. The hybrid power system includes traditional thermal and hydroelectric facilities along with gas and nuclear power plants. Two FACTS components, the superconducting magnetic energy storage (SMES) and the interline power flow controller (IPFC), have been included in to the system to improve stability of the system. The proposed FPID controller is developed to its full potential using the newly developed teaching learning-based optimization (TLBO) method. The TLBO technique's capability has been compared against a few common evolutionary strategies in order to demonstrate its superiority. It also shows how FPID controllers outperform traditional PID controllers. Investigations are also done on how IPFC and SMES might improve system stability performance. Owing to the outcome study, it is revealed that suggested TLBO-FPID, IPFC and SMES are most effective tool to advance the AGC mechanism. It is revealed from the outcomes that anticipated FPID controller reduces the settling time of area1 frequency (ΔF1) by 135.72 %, 428.58 % and 471.42 % over TLBO;PID, DE;PID and PSO;PI respectively. Finally, the proposed mechanisms are discussed with employing in electric vehicle integrated three-area distributed energy source-based power network to justify the efficacy of the proposed approaches.

Parametric optimizing of electro-discharge machining for LM25Al/VC composite material machining using deterministic and stochastic methods

AbstractThe electro-discharge machining (EDM) process is investigated using deterministic and stochastic methods to determine and model the effects of process parameters on machining performance. The workpiece utilized for the investigation was an LM25 aluminum alloy reinforced with vanadium carbide (VC), processed through a stir casting technique. EDM process parameters like peak current, discharge voltage, and pulse on-time are considered to analyze material removal rate, electrode wearing rate, and surface roughness. This study applied four multi-criteria decision-making (MCDM) and analytical methodologies to evaluate EDM performance. Then, the MCDM scores were compared using two objective verification mechanisms. In this case, the teaching-learning-based optimization (TLBO) technique delivered the best-desired results relative to the VIKOR, Grey relational grade (GRG), and the response surface method (RSM). Also, the RSM and analytical methods are simpler than the other methods, though they produced nearly identical results as the sophisticated MDCM and deterministic methods.

A comprehensive review on the effect of turbulence promoters on heat transfer augmentation of solar air heater and the evaluation of thermo-hydraulic performance using metaheuristic optimization algorithms

AbstractSolar air heaters are characterized by poor thermal performance due to limited heat transfer capability of air, thereby necessitating the need for design modifications. Among a different system performance augmentation technique, turbulence promotors are widely used owing to its effectiveness. Based on design parameters such as geometry, size, pitch and arrangement of turbulence promoters, varying levels of heat transfer increment with the pressure drop penalty is achievable. This led to the development of new designs which could offer on optimum thermo-hydraulic performance for a wide range of Reynolds number. Such research invariably requires a thorough insight of data related to various design parameters and optimal thermal–hydraulic performance range. This article provides a detailed overview of various turbulence promotor designs and their optimal thermal–hydraulic performance ranges compiled from a wide spectrum of experimental and numerical studies. Apart from outlining the general flow characteristics of each turbulator design, this study also evaluates different metaheuristic optimization algorithm such as bonobo optimization (BO), particle swarm optimization and teaching–learning-based optimization algorithm for enhancing the thermal–hydraulic performance parameter (THPP). The study shows that the BO algorithm does not exhibit local trapping due to its self-adapting nature of the optimized parameters which makes it a promising choice for THPP optimization studies in air heater applications. The extensive review also shows that the arrangement pattern of rib turbulator plays a key role in thermo-hydraulic performance augmentation. Based on the BO optimization analysis, the range of THPP is determined for the optimized geometry of turbulence promoters. In the pool of rib design, transverse prism rib, multi-V-rib, multi-V-shaped rib with staggered rib, sinewave-shaped rib with gap and S-shaped ribs exhibits an optimal THPP range of 2.05–3.32, 2.43–2.94, 3.00–3.61, 1.58–3.40 and 2.05–3.74, respectively. Other turbulence promotor designs such as winglet vortex generator, dimple protrusion in arc shape and multi-V-baffles exhibits optimal THPP range of 1.95–2.2, 2.44–3.68 and 1.75–2.01, respectively. At the end, the study proposes key research gaps such as the use of combined ribs and vortex generators and discrete fin arrays of different geometry as future scope of research. Graphical abstract

Consolidation Coefficient of Soil Prediction by Using Teaching Learning based Optimization with Fuzzy Neural Network

A key factor in constructing buildings leaning on soft soil is the consolidating coefficient of the soil referred as Cv. It is a crucial lab-measured engineering parameter utilized during the design and verification of geotechnical structures. Nevertheless, experimental experiments take a lot of time and money. In this study, the   is projected using Fuzzy Neural Network (FNN) with optimized feature selection using Teaching Learning-based Optimization, estimating Cv as the most crucial step (TLO), which has enhanced the quality of the prediction model by removing unnecessary characteristics and relying solely on crucial ones. The experimental results demonstrate that the projected FNN, followed by the Multi-layer Training algorithm Neural Network (MLP), Impact of changing Optimization (BBO), a support vector regression (SVR), Back - propagation algorithm Multi-layer Training algorithm Bayesian Network (Bp-MLP Neural Nets), has the highest predictive validity for the prediction of   (Root Mean Squared Error (RMSE )= 0.379, Mean Absolute Error (MAE) = 0.26, and coefficient of determination r = 0.835). Hence, it can be said that even if all used models perform well in predicting the soil consolidation coefficient, the FNN-TLO performs the best.