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Predicting the Height of Water-Conducting Fissure Zones in a Jurassic Coalfield Based on AdaBoost-WOA-BPNN

MINE WATER AND THE ENVIRONMENT 2025年 1-17页

作者： Hou, Enke Bi, Meng Long, Tianwen Xie, Xiaoshen Hou, Pengfei Li, Qianlong Xian Univ Sci & Technol Coll Geol & Environm Xian 710054 Shaanxi Peoples R China China Coal Technol & Engn Grp Xian Res Inst Grp Co Xian 710077 Peoples R China

Understanding the development of water-conducting fissure zones in the Huanglong Jurassic coalfield and accurately predicting the height of these fissures are crucial to preventing and controlling damage to the overlying sandstone aquifer of the Luohe Formation. To develop a predictive model applicable to the Huanglong Jurassic coalfield, data from measurements of 27 water-conducting fissure zones in the coalfield were used as samples, and the coal seam burial depth, coal seam mining thickness, and oblique length of the working face were used as training indicators. The whale optimisation algorithm (WOA), back-propagation neural network (BPNN), and AdaBoost algorithm were combined to develop the AdaBoost-WOA-BPNN model for predicting the height of water-conducting fissure zones. The accuracies of the models were compared, and the height of the water-conducting fissure zone in the 4105 working face of the Wenjiapo coal mine in the Binchang mining area of the Huanglong Coalfield was predicted. The AdaBoost-WOA-BPNN model outperformed the other models in terms of error, predictive accuracy, and applicability. Moreover, this predictive accuracy met the requirements of engineering practice. The results of this study provide a valuable reference for predicting the height of the water-conducting fissure zones and for preventing and controlling roof water hazards.

关键词： Coal mining AdaBoost principle whale optimisation algorithm Back-propagation neural network Predictive model Preventing and controlling roof water hazards

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Prediction of the bed expansion and pressure drop in microirrigation media filter backwashing using artificial neural networks and comparison with other machine learning techniques

SMART AGRICULTURAL TECHNOLOGY

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SMART AGRICULTURAL TECHNOLOGY 2025年 11卷

作者： Garcia-Nieto, Paulino Jose Garcia-Gonzalo, Esperanza Graciano-Uribe, Jonathan Arbat, Gerard Duran-Ros, Miquel Pujol, Toni Puig-Bargues, Jaume Univ Oviedo Dept Math Fac Sci Oviedo 33007 Asturias Spain Univ Girona Dept Chem & Agr Engn & Technol Girona 17003 Catalonia Spain Univ Girona Dept Mech Engn & Ind Construct C Univ Girona 4 Girona 17003 Catalonia Spain

The filtration capacity of media filters, which are widely used in drip irrigation systems to prevent emitter clogging, must be periodically restored by backwashing, which fluidizes the media bed and removes those trapped particles. Bed expansion (BE) and pressure drop (PD) are the key parameters for assessing the hydraulic performance of backwashing, but the available equations and models frequently fall short of their prediction. An experiment with three medium types, four filter underdrain designs, two bed heights and different backwashing superficial velocities as input variables was conducted to measure both BE and PD. A dataset of 705 backwashing runs was obtained and with 80 % of data for training and 20 % for testing, a machine learning-based model that uses Artificial Neural Networks (ANN) to predict both BE and PD was developed and compared with the Ridge, Elastic-net, and Lasso regression models. With coefficients of determination of 0.9932 and 0.9988 for BE and PD, respectively, the results demonstrated that the ANN model not only ranked the importance of the input variables and showed strong agreement with experimental data but also attained superior predictive accuracy regarding the Lasso, Elastic-net, and Ridge models. This study presents a novel and optimized approach for predicting bed expansion and pressure drop, enhancing the reliability of media filter backwashing performance assessments in smart irrigation systems.

关键词： Drip irrigation Smart irrigation whale optimisation algorithm Lasso Ridge Elastic-net regressions

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A novel method to increase the security in 5G networks using deep learning

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INTERNATIONAL JOURNAL OF ELECTRONIC SECURITY AND DIGITAL FORENSICS 2025年第3期17卷 419-431页

作者： Rajasekar, A. Ramamoorthi, R. Ramya, M. Arunachalam, Vinod Sri Sairam Inst Technol Dept Artificial Intelligence & Data Sci Chennai 44 India Govt Coll Engn Dharmapuri Dept EEE Dharmapuri India SA Engn Coll Dept ECE Chennai 77 India Govt Coll Engn Dharmapuri Dept ECE Dharmapuri 636704 India

Wireless networks are being forced to handle a greater amount of data due to a variety of circumstances, and this trend is progressing at a rapid rate. The denial of service (DoS) attacks have the highest rate of growth target the expanding computational network infrastructures all over the globe. As a consequence of this, the objective of this study is to come up with an innovative model for the identification of DoS attacks on 5 G networks. The model will go through two phases: the first will be feature extraction, and the second will be attack detection. In order to successfully carry out the detection, classifiers that have long short-term memories (LSTM) are utilised. The whale optimisation algorithm (WOA) model works to optimise the weight of the LSTM. The detected output of a hybrid model that has been trained appropriately provides the accuracy rate of 96.5%

关键词： 5G deep learning dos attacks denial-of-service whale optimisation algorithm WOA

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WOATCA: A secure and energy aware scheme based on whale optimisation in clustered wireless sensor networks

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IET COMMUNICATIONS 2020年第8期14卷 1199-1208页

作者： Sharma, Richa Vashisht, Vasudha Singh, Umang Amity Univ Amity Inst Informat Technol Noida India Amity Univ Amity Sch Engn & Technol Noida India Inst Technol & Sci Ghaziabad India

Clustering is an important method for extending the network life of each node in wireless sensor networks. Owing to the untrustworthy environment, these nodes unintentionally sacrifice their energy and lost their life which is a major challenge and requires appropriate attention for smooth communication. Thus energy efficiency and network life period both are major concerns in critical and sensitive real-life applications such as military applications, landslide monitoring etc. This study reviews and analyses existing trust-based clustering routing protocols for a complete understanding of research contribution to date made by researchers of this domain. In addition, a novel trust-based energy efficient protocol based on a whale optimisation algorithm named WOATCA has been presented. This algorithm focuses on the selection of trust worthy nodes as cluster heads (CHs) by considering five parameters namely (i) node's residual energy, (ii) number of packets forwarded by a node, (iii) average cluster distance, (iv) transmission delay, and (v) node density. Simulation results and analysis demonstrate that WOATCA outperformed existing protocols by showing a percentage decrease of 87.85, 64.58 and 61.36% in the selection of compromised nodes as CH in comparison with existing trust-based schemes such as Adoptive LEACH Mobile (ALM), Topology Control algorithm for node mobility (TCM), Q12 and secure CH selection protocols, respectively.

关键词： wireless sensor networks telecommunication security protocols pattern clustering routing protocols research contribution trust-based energy efficient protocol whale optimisation algorithm WOATCA trust parameter routing protocols trust-based clustering military applications real-life applications energy efficiency smooth communication appropriate attention untrustworthy environment network life clustered wireless sensor networks secure energy secure CH selection protocols existing trust-based schemes compromised nodes node density average cluster distance parameters node trust worthy environment cluster heads

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Performance analysis of doubly-fed induction generator using PID controller optimised by whale optimisation

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INTERNATIONAL JOURNAL OF ENGINEERING SYSTEMS MODELLING AND SIMULATION 2024年第4期15卷 199-212页

作者： Kashiv, Ashutosh Verma, H. K. SGSITS Dept Elect Engn Indore 452010 Madhya Pradesh India

The large-scale installation of wind turbines equipped with a doubly powered induction generator (DFIG) has been promoting the carrying out of several studies related to potential solutions for their integration into the electrical grid. In this paper, a control technique is presented that allows to regulate the active and reactive powers of DFIG in a stable and independent way. Its feasibility is supported by simulation results of models developed using MATLAB/Simulink software. To optimise the gains of the proportional, integral, derivative (PID) controller, a metaheuristic optimisation methodology called whale optimisation is used, where the operation of the system will be considered in the design stage to increase the robustness of the control. The behaviour of the control loops is evaluated after the occurrence of three-phase short circuits in the distribution network.

关键词： doubly-fed induction generator DFIG GSC PID rotor-side controller RSC wind turbine whale optimisation algorithm WOA

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Solution to Unit Commitment Using Lagrange Relaxation with whale Optimization Method

Solution to Unit Commitment Using Lagrange Relaxation with W...

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IEEE International Conference on Innovations in Power and Advanced Computing Technologies

作者： Saranya, S. Saravanan, B. VIT Univ Sch Elect Engn Vellore Tamil Nadu India

ISBN: (纸本)9781538681909

The unit commitment is an optimization problem which aims at optimal scheduling of generators subjected to different constraints such as power balance, minimum uptime and down time etc. A novel Lagrange Relaxation with whale optimization algorithm (LR-WOA) has been developed for solving unit commitment problem. In order to prove the superiority over other conventional methods, IEEE 10 unit 24 hour system is taken and solved using LR- WOA optimization algorithm and compared with other conventional algorithms.

关键词： Lagrange Relaxation Minimum up time Minimum down time whale optimisation algorithm Unit Commitment Power balance constraint

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Protein Folding Simulation using Combinatorial whale Optimization algorithm 21

Protein Folding Simulation using Combinatorial Whale Optimiz...

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21st International Symposium on Symbolic and Numeric algorithms for Scientific Computing (SYNASC)

作者： Sima, Ioan Parv, Bazil Babes Bolyai Univ Fac Math & Comp Sci Cluj Napoca Romania

ISBN: (纸本)9781728157245

The whale Optimization algorithm (WOA) is a novel nature-inspired algorithm, being originally dedicated to continuous function optimization. This paper modifies it to address combinatorial, i.e. discrete function optimization;the new algorithm is called Combinatorial whale Optimization algorithm (cWOA). cWOA was applied to protein folding problem on the 2D HP Model, which is a well known combinatorial optimization problem. The results are encouraging for further development and expansion of experiments to 3D HP or other models.

关键词： Protein Folding Simulation HP 2D Model whale optimisation algorithm Combinatorial whale optimisation algorithm

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Optimised random forest for predicting bed expansion and pressure drop in media filter backwashing

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Biosystems Engineering 2025年 256卷

作者： García-Nieto, P.J. García-Gonzalo, E. Graciano-Uribe, J. Arbat, G. Duran-Ros, M. Pujol, T. Puig-Bargués, J. Department of Mathematics Faculty of Sciences University of Oviedo Oviedo 33007 Spain Department of Chemical and Agricultural Engineering and Technology University of Girona Catalonia Girona 17003 Spain Department of Mechanical Engineering and Industrial Construction University of Girona C/ Universitat de Girona 4 Catalonia Girona 17003 Spain

In drip irrigation systems, media filters are frequently used to avoid emitter clogging, but periodic backwashing is required to restore media filtration capacity. Backwashing reverses the flow, fluidises the media, and expels trapped particles, being bed expansion (BE) and pressure drop (PD) the key parameters for assessing backwashing hydraulic performance. Available equations and models, however, often fail to reliably predict these parameters under diverse operational conditions. This study introduces a machine learning-based model utilising Random Forest Regression (RFR) and the whale optimisation algorithm (WOA) to predict both BE and PD from a dataset of 705 backwashing runs carried out in the laboratory. For comparison, Lasso, Elastic-net, and Ridge regression models were also implemented with the WOA optimiser. The RFR model was tuned to enhance accuracy by identifying key operational inputs: filter medium type (three categories: silica sand 0.75–0.85 mm, microspheres 0.63–0.75 mm, and silica sand 0.63–0.75 mm), underdrain type (four categories), filter bed height (200 or 300 mm), and superficial velocity. Results showed the WOA/RFR model not only ranked input variable importance but also achieved superior predictive accuracy, with coefficients of determination of 0.9771 and 0.9957 for BE and PD, respectively. The WOA/RFR model consistently outperformed Lasso, Elastic-net, and Ridge models, but demonstrated robust alignment with experimental data. This study presents a novel and optimised approach for predicting bed expansion and pressure drop, enhancing the reliability of media filter backwashing performance assessments in irrigation systems. © 2025 The Authors

关键词： Elastic-net Lasso Microirrigation Random forest regression Ridge whale optimisation algorithm

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Efficiency of different optimisation approaches for optimal parameters of the CSM for investigating the onset field due to corona point

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第9期14卷 1751-1761页

作者： Talaat, M. Tayseer, M. El-Zein, A. Zagazig Univ Elect Power & Machines Dept Fac Engn Zagazig Egypt Shaqra Univ Elect Dept Coll Engn Dawadmi Ar Riyadh Saudi Arabia

Corona discharges require high and non-uniform field to take place. In this research, the difficulty of suggesting the required arrangement of optimum fictitious charges, their positions and values inside the conductor in charge simulation method (CSM) is solved using genetic algorithm, satin bowerbird optimisation and whale optimisation algorithm. A hybrid model based on combining CSM with proposed optimisation techniques is provided to simulate and calculate the non-uniform electric field that is created by a hyperbolic needle-to-plane configuration expressed in a dielectric medium. The accuracy of electric-field simulation results is investigated by estimating per cent potential error and the maximum deviation angle of the normal field vector at the conductor surface for each proposed optimisation algorithm, taking into account the assessment of the efficiency of all proposed optimisation algorithms. To investigate the validity and accuracy of the proposed model, a comparison is held with available previous simulation and experimental data.

关键词： electric fields genetic algorithms corona discharges (electric) optimal parameters CSM onset field corona point corona discharges nonuniform field optimum fictitious charges charge simulation method genetic algorithm satin bowerbird optimisation whale optimisation algorithm hybrid model nonuniform electric field hyperbolic needle-to-plane configuration electric-field simulation results normal field vector conductor surface

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Two-fold calibration approach for microscopic traffic simulation models

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IET INTELLIGENT TRANSPORT SYSTEMS 2019年第10期13卷 1507-1517页

作者： Karimi, Mohammad Alecsandru, Ciprian Concordia Univ Dept Bldg Civil & Environm Engn 1455 Maisonneuve Blvd W Montreal PQ H3G 1M8 Canada

Calibrating the microsimulation traffic models can be defined as a black-box optimisation problem with some non-concave objective functions. In this regard, the stochastic optimisation algorithms are suitable choices to explore the search space and prevent getting stuck in local optimums. However, considering only the traffic attributes-related objectives may fail to calibrate the model in terms of safety. Therefore, by defining two different objectives, a two-fold calibration approach is proposed such that the simulation model reproduces the real-world transportation network more accurately, both in terms of safety and operation. Moreover, the performance of two different approaches to solve this multi-objective optimisation problem are evaluated. It is shown that by aggregating the objectives in one single formula (i.e. a priori methods), the information exchange among solutions is not captured, which may lead to non-optimal solutions. While this limitation is overcome by a posteriori methods since different objectives can be optimised separately and simultaneously. In this regard, the performance of posteriori-based multi-objective particle swarm optimisation (MOPSO) algorithm in calibrating VISSIM is compared with some priori-based optimisation algorithms (e.g. PSO, genetic algorithm, and whale optimisation algorithm). The results show that posteriori-based MOPSO leads to a more accurate solution set in terms of both objectives.

关键词： genetic algorithms traffic engineering computing particle swarm optimisation road traffic control two-fold calibration approach microscopic traffic simulation models microsimulation traffic models black-box optimisation problem nonconcave objective functions stochastic optimisation algorithms search space local optimums simulation model real-world transportation network nonoptimal solutions posteriori-based multiobjective particle swarm optimisation algorithm whale optimisation algorithm traffic attribute-related objectives MOPSO algorithm VISSIM

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