Biogeography-based Optimization Algorithm Research Articles

Smart infrastructure design: Machine learning solutions for securing modern cities

In the realm of securing smart cities against emerging cyber threats, this research encompasses three distinct yet interconnected initiatives. First, a pioneering data architecture design leveraging CycleGAN is proposed to counteract False Data Injection Attacks (FDIA). By cyclically transforming data distributions, CycleGAN ensures the integrity and reliability of smart city information, fortifying against malicious manipulations. Second, an innovative Internet of Things (IoT) concept is introduced, aiming to enhance real-time monitoring and context-aware threat detection within Intrusion Detection Systems (IDS). Harnessing the wealth of data generated by IoT devices, this concept provides a comprehensive understanding of network activities, fostering adaptive responses to emerging threats. Lastly, the research delves into the development of a novel IDS hyperparameter adjusting system. This system integrates the strengths of Biogeography-Based Optimization (BBO) and Whale Optimization Algorithm (WOA) to fine-tune IDS configuration parameters. Drawing inspiration from biogeographical principles and collaborative whale behavior, the hybrid optimization system balances exploration and exploitation, adapting the IDS to diverse network environments. Together, these initiatives represent a holistic approach to fortifying smart cities through cutting-edge data architecture, IoT-driven threat detection, and optimized IDS configurations, contributing to the resilience and cybersecurity of modern urban landscapes.

Prediction of CO2 emission for the central European countries through five metaheuristic optimization techniques helping multilayer perceptron

One of the most significant issues in urban design is energy-related CO2 emissions, which are rising quickly as cities expand. The GDP of the Central European countries (from 1990 to 2016) based on several energy sources, such as coal, oil, natural gas, and renewable energy, are used as inputs in this study. To develop a reliable predictive network considering the problem complexity, multilayer perceptron (MLP) is combined with several nature-inspired optimization algorithms, namely, black hole algorithm (BHA), future search algorithm (FSA), backtracking search algorithm (BSA), biogeography-based optimization (BBO), and shuffled complex evolution (SCE). By applying the approaches mentioned above to the synthesis of the MLP, the recommended BBO, BHA, BSA, FSA, and SCE ensembles are obtained. A series of parametric studies are performed to improve the effectiveness of the employed models. It is found that, by combining the BBO, BHA, BSA, FSA, and SCE algorithms, the MLP's accuracy is increased. The result from this parametric analysis showed that SCE and BBO perform better than the other three algorithms as the CO2 emission was computed with the highest level of accuracy using R2 = 0.9999 and 0.9998, RMSE = 1.6781 and 2.0539 for SCE, and R2 = 0.9999 and 0.9998, RMSE = 1.8689 and 2.3833 for BBO.

Proposing a procedure for multi-objective optimization of cascade thermoelectric coolers to achieve maximum cooling capacity and COP

Abstract A novel approach is proposed for optimal designing two-stage thermoelectric coolers using the biogeography-based optimization algorithm. For the comprehensiveness of the research, both types of two-stage coolers, including electrically serial and separated stages, have been investigated. Using the proposed procedure, first by selecting the most important performance parameters of the cooler, i.e. COP and the cooling capacity as objective functions, the efficiency and the validity of the approach are proved by comparing the results with results of the previous researchers. In all the examined cases, the proposed approach has led to the enhancing of the cooler performance. The results of single-objective optimization showed that using the proposed method, the cooling capacity and COP can be improved up to 17 % and 8.5 % compared to the previous designs, respectively. In the following, multi-objective optimization of thermoelectric coolers is conducted, taking into account both the objectives of the COP and the cooling capacity. Finally, Pareto optimal solutions and the exergetic efficiency of the optimized cooler were calculated for different values of the weight factor of the multi-objective function. The results of multi-objective optimization showed that by increasing the COP weight factor in the objective function, the exergetic efficiency of the refrigeration system enhances.

Multi-skilled resource-constrained multi-project scheduling problem with dexterity improvement of workforce

In this study, a mixed-integer mathematical model has been presented for the Multi-Skilled Resource-Constrained Multi-Project Scheduling Problem (MSRCMPSP), where the dexterity of workers can be improved via two ways: (1) Cooperating with more proficient co-workers, and (2) Practicing the possessed skills. The model aims to determine the start and finish times of activities considering the dexterity improvement of workforces. This research presents a Modified discrete variant of the Biogeography-Based Optimization (MBBO) algorithm to solve the model and to minimize the total required duration to complete all projects. The MBBO embraces two novel migration procedures, a new mutation process, and a new habitat selection operator. On several test instances and on a case study, the efficacy of the MBBO has been put to examination in comparison to four other meta-heuristics. The MBBO has overpowered other methodologies in terms of most of the assessment criteria. The obtained results have revealed that the dexterity improvement of workers significantly improves the total required duration to complete all projects. This research and its outputs can be helpful for the researchers who work on project scheduling models. Moreover, this study can inspire future project scheduling formulations, where the dexterity of human resources is affected by the learning phenomenon.

Optimization of Load Distribution Method for Hydropower Units Based on Output Fluctuation Constraint and Double-Layer Nested Model

During the load distribution of hydropower units, the frequent crossing of vibration zones as well as large output fluctuations affect the stability of the power station. A multi-objective double-layer intelligent nesting model that considers the constraint of the output fluctuation of units is proposed to address these problems. The nonlinear constraint unit commitment optimization model layer is built based on outer dynamic programming, and the load distribution optimization model layer is constructed based on the improved biogeography-based optimization algorithm. Simultaneously, the unit output fluctuation constraint is established based on whether the unit combination changes in order to limit the unit output fluctuation. The results of this model indicate that compared with traditional load allocation models, the application of the method proposed in this paper can reduce the fluctuation range of unit output by 85.01%. In addition, except for the inevitable vibration zone crossings during startup and shutdown processes, the unit does not cross the vibration zone during operation, which greatly improves the unit’s vibration isolation and optimization capabilities. The multi-objective double-layer intelligent nested model proposed in this paper has significant advantages in the field of load allocation for hydropower units. It effectively improves the stability and reliability of unit operation, and this method can be applied to practical load allocation processes. It is of great significance for the research on load allocation optimization of hydropower units.

Tunnel boring machine performance assessment and prediction applying hybrid artificial intelligence

A machine for tunnel boring machine (TBM) is recognized as productive equipment for tunnel construction. A dependable and precise tunnel boring machine’s performance (such as penetration rate (ROP)) prediction could reduce the cost and help choose the suitable construction method. Hence, this research develops new integrated artificial intelligence methods, i.e., biogeography-based multilayer perceptron neural network (BMLP) and biogeography-based support vector regression (BSVR), to forecast TBMPR. Using the biogeography-based optimization (BBO) algorithm aims to improve the developed model’s performance by determining the optimized neuron number of hidden layers for MLP models and the ideal values of the essential variables of SVR method. The results show that advanced methods can productively make a nonlinear relation among the ROP and its forecasters to obtain a satisfying forecast. Amongst the BMLP models with several hidden substrates, BM5L with five hidden substrates could attain the total ranking score (TRS) greatest rate, with root mean squared error (RMSE) and coefficient of determination (R2) equal to 0.017 and 0.9969. Simultaneously, the BSVR was the supreme model because of the fewer RMSE (0.00497 m/hr) and a larger R2 (0.999) compared with BMLP models. Overall, the acquired TRSs show that the BSVR outperforms the BMLP in terms of performance. As a consequence, the BSVR model may have been chosen as the suggested model if it had been able to accurately forecast the observed value even better than BM5L.

A regional biogeography-based optimization algorithm for the distributed assembly permutation flow-shop scheduling problem with fuzzy processing time

For solving the distributed assembly flow shop scheduling problem with fuzzy processing time (FDAPFSP), a regional biogeography-based optimization algorithm (RBBO) is proposed to minimize the maximum fuzzy completion time. The mathematical model is provided. In RBBO, all habitats are divided into regions based on the habitat suitability index, and the habitats of each region are subject to cross-regional migration and replacement procedures. A critical factory optimization strategy is developed to enhance local search capability. Taguchi method is used to determine the parameters of RBBO. In ten FDAPFSP instances, comparative testing of RBBO algorithm with various heuristic and swarm intelligence algorithms are conducted. The computation results show that in ten FDAPFSP cases, the proposed RBBO outperforms other algorithms in nine out of ten FDAPFSP cases.

A Comprehensive Multi-Strategy Enhanced Biogeography-Based Optimization Algorithm for High-Dimensional Optimization and Engineering Design Problems

The biogeography-based optimization (BBO) algorithm is known for its simplicity and low computational overhead, but it often struggles with falling into local optima and slow convergence speed. Against this background, this work presents a multi-strategy enhanced BBO variant, named MSBBO. Firstly, the example chasing strategy is proposed to eliminate the destruction of the inferior solutions to superior solutions. Secondly, the heuristic crossover strategy is designed to enhance the search ability of the population. Finally, the prey search–attack strategy is used to balance the exploration and exploitation. To verify the performance of MSBBO, we compare it with standard BBO, seven BBO variants (PRBBO, BBOSB, HGBBO, FABBO, BLEHO, MPBBO and BBOIMAM) and seven meta-heuristic algorithms (GWO, WOA, SSA, ChOA, MPA, GJO and BWO) on multiple dimensions of 24 benchmark functions. It concludes that MSBBO significantly outperforms all competitors both on convergence accuracy, speed and stability, and MSBBO basically converges to the same results on 10,000 dimensions as on 1000 dimensions. Further, MSBBO is applied to six real-world engineering design problems. The experimental results show that our work is still more competitive than other latest optimization techniques (COA, EDO, OMA, SHO and SCSO) on constrained optimization problems.

Thermal field simulation and material parameter optimization for spaceborne annular truss antennas

The operational efficacy of large spaceborne annular truss antennas in orbit is significantly influenced by solar irradiation and alternating Earth shadow. This antenna system periodically encounters diverse extreme environments that impact the precision of the antenna surface performance. Consequently, this study presents an optimized thermal control design and conducts temperature field simulation calculations for such antennas. Initial efforts are directed toward analyzing the variables influencing the antenna structure’s temperature, with specific attention paid to the distinctive compositional characteristics of high-precision antennas. As a subsequent step, orthogonal tests are implemented, facilitating the development of an antenna thermal analysis model. This model assists in the identification of principal variables influencing the antenna’s temperature field. Finally, the antenna’s optimal thermal design is drawn upon the biogeography-based optimization (BBO) algorithm, enabling the derivation of ideal material parameters for the thermal design of the antenna. This methodology offers theoretical guidance for future thermal control design of large spaceborne annular truss antennas.

Optimized PI tuning of DG-Integrated Shunt Active Power Filter Using Biogeography-Based Optimization Algorithm

Optimized PI tuning of DG-Integrated Shunt Active Power Filter Using Biogeography-Based Optimization Algorithm

A comparative approach for the optimal design of steel structures using biogeography-based optimization (BBO) algorithm and genetic algorithm (GA)

Structural optimization is one of the key concerns of civil engineering designers, but from a mathematical point of view, this optimization problem is highly complex and complicated due to a large number of non-linear design constraints and the iterative procedure of structural analysis. Introducing optimization algorithms such as biogeography-Based Optimization (BBO) and genetic algorithms (GA) into applications can help the user to optimize the cost of the structure to be adopted more quickly and with fewer errors in the preliminary phase of the design study. The aim of this research is to carry out a comparative approach to structure weight minimization using biogeography-Based Optimization (BBO) algorithm and genetic algorithms (GA), examining the influence of the number of populations and the number of iterations in the final results. In this study, both algorithms gave reliable results, but a comparison of the results obtained by the two methods reveals that the biogeography-Based Optimization algorithm (BBO) can be successfully used for the optimization of steel structures while ensuring verification of the strength, serviceability and stability criteria defined by Eurocode 3 (Union, 2006), as it has certain advantages in detecting the global minimum over genetic algorithms (GA). It is capable of finding solutions that are lighter, stiffer and have lower deflection than the original designs.

A Comparative Study of Metaheuristic Optimization Algorithms for Solving Engineering Design Problems

Metaheuristic optimization algorithms (Nature-Inspired Optimization Algorithms) are a class of algorithms that mimic the behavior of natural systems such as evolution process, swarm intelligence, human activity and physical phenomena to find the optimal solution. Since the introduction of meta-heuristic optimization algorithms, they have shown their profound impact in solving the high-scale and non-differentiable engineering problems. This paper presents a comparative study of the most widely used nature-inspired optimization algorithms for solving engineering classical design problems, which are considered challenging. The teen metaheuristic algorithms employed in this study are, namely, Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Biogeography Based Optimization Algorithm (BBO), Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES), Cuckoo Search algorithm (CS), Differential Evolution (DE), Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO). The efficiency of these algorithms is evaluated on teen popular engineering classical design problems using the solution quality and convergence analysis, which verify the applicability of these algorithms to engineering classical constrained design problems. The experimental results demonstrated that all the algorithms provide a competitive solution.

A biogeography-based optimization algorithm with modified migration operator for large-scale distributed scheduling with transportation time

To expand production scale and enhance enterprise competitiveness, modern supply chains and manufacturing systems have shifted from the single factory production to the multi-factory production network with the characteristics of large scale, diversification of varieties, redundant production factories, flexible production processes, and high-value products. This production mode is called distributed scheduling. The transportation time caused by geographically distributed factories in Distributed Scheduling Problem (DSP) with a series-parallel network plays an important role in the scheduling scheme. To minimize the global makespan over all factories, a two-segment representation has been taken as an encoding scheme while the decoding procedure applies the First Come First Served (FCFS) heuristic rule. A Biogeography-Based Optimization with Modified Migration Operator (BBO-MMO) is then proposed to address the heterogeneous DSP with transportation time between factories at adjacent processing stages, which improves the migration operator by modifying the immigration rate according to processing time and transportation time. A cosine migration model has been adopted to enhance the ability of BBO-MMO to break through local optimum. BBO-MMO is compared with the Improved Memetic Algorithm (IMA), and several groups of examples with various parameters have been generated to test the performance of BBO-MMO. The results show that the proposed BBO-MMO can effectively solve the large-scale heterogeneous DSP with transportation time.

A novel neural-evolutionary framework for predicting weight on the bit in drilling operations

This study compares the performance of artificial neural networks (ANN) trained by grey wolf optimization (GWO), biogeography-based optimization (BBO), and Levenberg–Marquardt (LM) to estimate the weight on bit (WOB). To this end, a dataset consisting of drilling depth, drill string rotational speed, rate of penetration, and volumetric flow rate as input variables and the WOB as a response is used to develop and validate the intelligent tools. The relevance test is applied to sort the strength of WOB dependency on the considered features. It was observed that the WOB has the highest linear correlation with the drilling depth and drill string rotational speed. After dividing the databank into the training and testing (4:1) parts, the proposed LM-ANN, GWO-ANN, and BBO-ANN ensembles are constructed. A sensitivity analysis is then carried out to find the most powerful structure of the models. Each model performs to reveal the relationship between the WOB and the mentioned independent factors. The performance of the models is finally evaluated by mean square error (MSE) and mean absolute error criteria. The results showed that both GWO and BBO algorithms effectively help the ANN to achieve a more accurate prediction of the WOB. Accordingly, the training MSEs decreased by 14.62% and 24.90%, respectively, by applying the GWO and BBO evolutionary algorithms. Meanwhile, these values were obtained as around 9.86% and 9.41% for the prediction error of the ANN in the testing phase. It was also deduced that the BBO performs more efficiently than the other technique. The effect of input variables dimension on the accuracy and training time of the BBO-ANN clarified that the most accurate WOB predictions are achieved when the model constructs with all four input variables instead of utilizing either three or two of them with the highest linear correlation. It was also observed that the training stage of the BBO-ANN model with four input variables needs a little more computational time than its training with either two or three variables. Finally, the accuracy of the BBO-ANN model for the WOB prediction has been compared with the multiple linear regression, support vector regression, adaptive neuro-fuzzy inference systems, and group method of data handling. The statistical accuracy analysis confirmed that the BBO-ANN is more accurate than the other checked techniques.

State of Charge Estimation for Power Battery Using Improved Extended Kalman Filter Method Based on Neural Network

In order to enhance the accuracy of the traditional extended Kalman filter (EKF) algorithm in the estimation of the state of charge (SoC) of power batteries, we first derived the state space equation and measurement equation of lithium power batteries based on the Thevenin battery model and the modified Ampere-Hour integralalgorithm. Then, the basic principles of EKF, backpropagation neural networks (BPNNs), and a biogeography-based optimization (BBO) algorithm were analyzed, and the arc curve mobility model was used to improve the global search ability of the BBO algorithm. By combining these three algorithms, this paper proposes a BP neural network method based on the BBO algorithm. This method uses the BBO algorithm to optimize the incipient weight and threshold of the BP neural network and uses this improved neural network to modify the estimated value of the extended Kalman filter algorithm (BBOBP-EKF). Finally, the BBOBP-EKF algorithm, the extended Kalman filter algorithm based on the BP neural network (BP-EKF), and the EKF algorithm are used to estimate the error value of the SOC of a power battery, and according to the experimental data, it was confirmed that the proposed BBOBP-EKF algorithm has been improved compared to other algorithms with respect to each error index term, in which the maximum error is 1% less than that of the BP-EKF algorithm and 2.4% less than that of the EKF algorithm, the minimum error is also the smallest, and the estimation accuracy is improved compared to the traditional algorithms.

Predicting friction capacity of driven piles using new combinations of neural networks and metaheuristic optimization algorithms

Friction capacity is a principal characteristic in designing driven piles. Considering the complexities in analyzing the behavior of piles, many studies have recommended the use of machine learning for this purpose. However, the used methodologies need to be updated and improved with respect to recent computational advances such as the development of optimization algorithms. In this work, three metaheuristic algorithms, namely equilibrium optimizer (EO), biogeography-based optimization (BBO), and salp swarm algorithm (SSA) are deployed to optimize an artificial neural network (ANN) for predicting pile friction capacity based on pile geometry, effective stress, and shear strength. The findings indicate the suitability of the proposed algorithms. More specifically, in the training phase, the ANN supervised by SSA yielded the most accurate results, whereas in the testing phase, the BBO-ANN outperformed the two other models. The calculated mean absolute error, Pearson correlation coefficient, and root mean square error for the models are as follows: 6.0740, 0.9385, and 7.0678 for the EO-ANN, 6.1450, 0.9440, and 6.7343 for the BBO-ANN, and 5.9684, 0.9395, and 7.1322 for the SSA-ANN. It is shown that both SSA-ANN and BBO-ANN can serve as efficient tools for the reliable design of driven piles, providing efficient computational intelligence alternatives to traditional design methods.

An optimized backpropagation neural network models for the prediction of nanomaterials concentration for purification industrial wastewater

In this paper, an optimized backpropagation neural network (BPNN) prediction models have been proposed to estimate the concentration of titanium dioxide (TiO2) nanomaterials required for the removal of Acid Black 24 dye, a type of textile dye, from industrial wastewater. The research gap addressed in this study lies in the investigation of various optimization algorithms for BPNN models and their profound impact on the accuracy of nanomaterial concentration predictions, thus contributing to the advancement of nanotechnology-based wastewater treatment research and its practical applications in industrial settings. Although several studies have predicted the amount of nanomaterial needed to degrade the dyes in industrial wastewater using BPNN, few have investigated the effects of optimized BPNN and how greatly the amount of degradation can vary. Therefore, six different optimization algorithms, including genetic algorithms (GAs), ant colony optimization (ACO), particle swarm optimization (PSO), evaluation strategy (ES), biogeography-based optimization (BBO), and population-based incremental learning algorithm (PBIL), are applied to optimize and evaluate the BPNN models. Experimental data obtained from chemical laboratory experiments are used to train and test the models, with different experiments conducted for various factors like TiO2, H2SO4, NaIO4, K2S2O8, dye, and silver nitrate. The performance of the optimized BPNN models is assessed using metrics such as mean squared error (MSE), root means squared error (RMSE), and R-squared (R2). The results demonstrate the effectiveness of the proposed models, with ACO yielding the best outcomes across all models. The R2 values for the TiO2, H2SO4, NaIO4, and K2S2O8 models after optimization indicate perfect predictions, whereas the R2 values before optimization ranged from 0.888 to 0.984. PSO performs well for most models except for K2S2O8, GA improves most models except for the silver nitrate model, ES enhances only the Dye model, and PBIL does not affect the TiO2 model. Overall, the optimized BPNN models exhibit impressive accuracy in estimating the TiO2 concentration required for removing Acid Black 24 dye from industrial wastewater.

Performance Enhancement of Elephant Herding Optimization Algorithm Using Modified Update Operators

This research paper presents a modified version of the Elephant Herding Optimization (EHO) algorithm, referred to as the Modified Elephant Herding Optimization (MEHO) algorithm, to enhance its global performance. The focus of this study lies in improving the balance between exploration and exploitation within the algorithm through the modification of two key operators: the matriarch updating operator and the separation updating operator. By reframing the equations governing these operators, the proposed modifications aim to enhance the algorithm’s ability to discover optimal global solutions. The MEHO algorithm is implemented in the MATLAB environment, utilizing MATLAB R2019a. To assess its efficacy, the algorithm is subjected to rigorous testing on various standard benchmark functions. Comparative evaluations are conducted against the original EHO algorithm, as well as other established optimization algorithms, namely the Improved Elephant Herding Optimization (IEHO) algorithm, Particle Swarm Optimization (PSO) algorithm, and Biogeography-Based Optimization (BBO) algorithm. The evaluation metrics primarily focus on the algorithms’ capacity to produce the best global solution for the tested functions. The proposed MEHO algorithm outperformed the other algorithms on 75% of the tested functions, and 62.5% under two specific test scenarios. The findings highlight the effectiveness of the proposed modification in enhancing the global performance of the Elephant Herding Optimization algorithm. Overall, this work contributes to the field of optimization algorithms by presenting a refined version of the EHO algorithm that exhibits improved global search capabilities.

A Hybrid Algorithm for Multi-Objective Optimization—Combining a Biogeography-Based Optimization and Symbiotic Organisms Search

To solve the multi-objective, flexible job-shop scheduling problem, the biogeography-based optimization (BBO) algorithm can easily fall into premature convergence, local optimum and destroy the optimal solution. Furthermore, the symbiotic organisms search (SOS) strategy can be introduced, which integrates the mutualism strategy and commensalism strategy to propose a new migration operator. To address the problem that the optimal solution is easily destroyed, a parasitic natural enemy insect mechanism is introduced, and predator mutation and parasitic mutation strategies with symmetry are defined, which can be guided according to the iterative characteristics of the population. By comparing with eight multi-objective benchmark test functions with four multi-objective algorithms, the results show that the algorithm outperforms other comparative algorithms in terms of the convergence of the solution set and the uniformity of distribution. Finally, the algorithm is applied to multi-objective, flexible job-shop scheduling (FJSP) to test its practical application value, and it is shown through experiments that the algorithm is effective in solving the multi-objective FJSP problem.

Development of novel optimized deep learning algorithms for wildfire modeling: A case study of Maui, Hawai‘i

To address the growing global concern regarding increased wildfire occurrences and their widespread socio-ecological impacts, cost-effective and practical approaches must be urgently identified to accurately predict the probability of wildfire incidents. The objective of this study was to develop deep learning models to estimate the likelihood of wildfire incidents and compare the predictive capability of the methods. To this end, the group method of data handling (GMDH) and convolutional neural network (CNN) algorithms were coupled with the biogeography-based optimization (BBO) and ant colony optimization (ACO) algorithms to enhance the predictive performance of the models. Overall, 1745 historical wildfires were identified in the island of Maui, Hawai‘i. Among them, 1221 events (70%) were randomly selected to generate the models, and the remaining 524 (30%) were used for validation. The frequency ratio, information gain ratio (IGR), and variance inflation factor methods were used to select the optimal number of predictor variables for model development. 13 influencing factors: elevation, aspect, slope, plan curvature, slope length, valley depth, topographic wetness index, mean annual wind speed, mean annual air temperature, mean monthly rainfall, distance to the road, distance to the river, and normalized difference vegetation index were selected to generate the proposed models and detect fire-prone areas. The IGR values indicated that the key parameters for predicting wildfire susceptibility were the distance to the road, mean annual air temperature, elevation, and slope. Finally, the area under the receiver operating characteristic curve (AUROC) was computed to verify the reliability and accuracy of the wildfire susceptibility maps. Both the optimization algorithms enhanced the performances of the GMDH and CNN models. The ACO most notably enhanced the CNN performance compared with the models (AUROCTraining=0.889 and AUROCTesting=0.885). The findings demonstrated the potential of coupled models in overcoming the limitations of individual models for mapping fire-susceptible areas and analyzing the multifactorial aspects that lead to wildfire occurrence. Overall, the proposed frameworks can be used to predict the spatio-temporal patterns of wildfire occurrence, which are of significance for land management, wildfire prevention, and mitigation of wildfire consequences.