Heuristic Search Algorithm Research Articles

Application of Heuristic Search Algorithm to Design Automatic Generation Control in Power System

Application of Heuristic Search Algorithm to Design Automatic Generation Control in Power System

A second-order dynamic and static ship path planning model based on reinforcement learning and heuristic search algorithms

Ship path planning plays an important role in the intelligent decision-making system which can provide important navigation information for ship and coordinate with other ships via wireless networks. However, existing methods still suffer from slow path planning and low security problems. In this paper, we propose a second-order ship path planning model, which consists of two main steps, i.e., first-order static global path planning and second-order dynamic local path planning. Specifically, we first create a raster map using ArcGIS. Second, the global path planning is performed on the raster map based on the Dyna-Sarsa(lambda) model, which integrates the eligibility trace and the Dyna framework on the Sarsa algorithm. Particularly, the eligibility trace has a short-term memory for the trajectory, which can improve the convergence speed of the model. Meanwhile, the Dyna framework obtains simulation experience through simulation training, which can further improve the convergence speed of the model. Then, the improved ship trajectory prediction model based on stacked bidirectional gated recurrent unit is used to identify the risk of ship collision and switch the path planning from the first order to the second order. Finally, the second-order dynamic local path planning is presented based on the FCC-A* algorithm, where the cost function of the traditional path planning A* algorithm is rewritten using the fuzzy collision cost membership function (fuzzy collision cost, FCC) to reduce the collision risk of ships. The proposed model is evaluated on the Baltic Sea geographic information and ship trajectory datasets. The experimental results show that the eligibility trace and the Dyna learning framework in the proposed model can effectively improve the planning efficiency of the ship’s global path planning, and the collision risk membership function can effectively reduce the number of collisions in A* local path planning and thus improve the navigation safety of encountering ships.

A dynamic algorithm for trust inference based on double DQN in the internet of things

The development of the Internet of Things (IoT) has brought great convenience to people. However, some information security problems such as privacy leakage are caused by communicating with risky users. It is a challenge to choose reliable users with which to interact in the IoT. Therefore, trust plays a crucial role in the IoT because trust may avoid some risks. Agents usually choose reliable users with high trust to maximize their own interests based on reinforcement learning. However, trust propagation is time-consuming, and trust changes with the interaction process in social networks. To track the dynamic changes in trust values, a dynamic trust inference algorithm named Dynamic Double DQN Trust (Dy-DDQNTrust) is proposed to predict the indirect trust values of two users without direct contact with each other. The proposed algorithm simulates the interactions among users by double DQN. Firstly, CurrentNet and TargetNet networks are used to select users for interaction. The users with high trust are chosen to interact in future iterations. Secondly, the trust value is updated dynamically until a reliable trust path is found according to the result of the interaction. Finally, the trust value between indirect users is inferred by aggregating the opinions from multiple users through a Modified Collaborative Filtering Average-based Similarity (SMCFAvg) aggregation strategy. Experiments are carried out on the FilmTrust and the Epinions datasets. Compared with TidalTrust, MoleTrust, DDQNTrust, DyTrust and Dynamic Weighted Heuristic trust path Search algorithm (DWHS), our dynamic trust inference algorithm has higher prediction accuracy and better scalability.

Heuristic algorithms for the special knapsack packing problem with defects arising in aircraft arrangement

This paper addresses a special knapsack packing problem in which identical pieces are packed into an irregular board with unusable regions to maximize the number of pieces. This problem arises from a real-world application in which the aircraft is placed on the carrier’s deck to maximize the number of aircraft. We propose the support polygon concept to solve one of the challenges of the problem, i.e., ensure that the support points inside the piece’s interior are located within the board when the piece protrudes from the board. Many strategies for determining the piece’s location and selecting the rotation angle of the piece are proposed and compared. Based on these techniques, we propose a deterministic heuristic algorithm to solve the problem. In addition, we develop an improved version of the algorithm, called the recursive search heuristic algorithm, to get higher-quality solutions. Many experiments were conducted to test the performance of different combinations of strategies and the proposed algorithms. The computational results show that the proposed algorithms have a strong capacity for obtaining good solutions, and updating many of the best results in the benchmark instances provided by the literature.

A Low-Data-Dependence Assessment Method of Industrial Process Interruption Probability Due to Voltage Sag

Information about the interruption probability of sensitive industrial processes is important for devising mitigation strategies against voltage sags. However, the existing method for assessing interruption probability is less practical, owing to the difficulty in collecting technical information and data from industrial users. This study proposes a low-data-dependence assessment method for the interruption probability of an industrial process. First, it establishes a general assessment model for the tripping probability of sensitive equipment. Second, it proposes a parameter estimation method to determine the probability density function of the voltage tolerance curve of sensitive equipment in the uncertainty region, according to the equipment trip information recorded by the user. This improves the accuracy of the assessment. Third, this study establishes the logic function expression form of the industrial process structure to describe the relationship between the process interruption probability and the equipment tripping probability. Finally, it proposes a solution method for the process interruption probability based on a heuristic search algorithm, which overcomes the problem of technological process information acquisition, such as the logical relationship among various equipment in the industrial process. The rationality and applicability of the proposed method are verified through a case study.

Personalized Route Recommendation With Neural Network Enhanced Search Algorithm

In this work, we study an important task in location-based services, namely <i>Personalized Route Recommendation (PRR)</i> . Given a road network, the PRR task aims to generate user-specific route suggestions for replying to users’ route queries. A classic approach is to adapt search algorithms to construct pathfinding-like solutions. These methods typically focus on reducing search space with suitable heuristic strategies. For these search algorithms, heuristic strategies are often handcrafted, which are not flexible to work in complicated task settings. In addition, it is difficult to utilize useful context information in the search procedure. To develop a more principled solution to the PRR task, we propose to improve search algorithms with neural networks for solving the PRR task based on the widely used <inline-formula><tex-math notation="LaTeX">$A^{*}$</tex-math></inline-formula> algorithm. The main idea of our solution is to automatically learn the cost functions in <inline-formula><tex-math notation="LaTeX">$A^{*}$</tex-math></inline-formula> algorithms, which is the key of heuristic search algorithms. Our model consists of two main components. First, we employ attention-based Recurrent Neural Networks (RNN) to model the cost from the source to the candidate location by incorporating useful context information. Instead of learning a single cost value, the RNN component is able to learn a time-varying vectorized representation for the moving state of a user. Second, we propose to use an estimation network for predicting the cost from a candidate location to the destination. For capturing structural characteristics, the estimation network is built on top of position-aware graph attention networks. The two components are integrated in a principled way for deriving a more accurate cost of a candidate location for the <inline-formula><tex-math notation="LaTeX">$A^{*}$</tex-math></inline-formula> algorithm. Extensive experiment results on three real-world datasets have shown the effectiveness and robustness of the proposed model.

Exploring the Behavior of Bioelectric Circuits Using Evolution Heuristic Search

Characteristic spatial differences of cellular resting potential across tissues have been shown to act as instructive bioelectric prepatterns regulating embryonic and regenerative morphogenesis, as well as cancer suppression. Indeed, modulation of bioelectric patterns via specific ion channel-targeting drugs, channel misexpression, or optogenetics has been used to control growth and form in vitro, showing promise in regenerative medicine and synthetic bioengineering. Repair of defects, injury, and transformation requires quantitative understanding of bioelectric dynamics within tissues so that these can be modulated toward desired outcomes in organ patterning or the creation of entirely novel synthetic constructs. The major gap in the discovery of interventions for rational control of organ-level outcomes is the inability to predict large-scale bioelectric patterns—their emergence from symmetry breaking (given a set of channels expressed on the tissue) and their change as a function of time under specific bioelectrical interventions. It is thus essential to develop machine learning and other computational tools to help human scientists identify bioelectric states with desirable properties. In this study, we tested the ability of a heuristic search algorithm to explore the parameter space of bioelectrical circuits by adjusting the parameters of simulated cells. We show that while bioelectrical space is not easy to search, it does contain parameter sets that encode rich and interesting patterning behaviors. We demonstrate proof of principle of using a computational search platform to identify circuits with desired properties, as a first step toward the design of machine learning tools for improved bioelectric control of growth and form.

A Joint Data Association Method for Laser-SLAM of Unmanned Delivery Vehicle Based on Heuristic Search Algorithm

In Laser-SLAM system of unmanned delivery vehicle, there are two kinds of association methods applied to solve the data association problem. Compared with the method of independent association for a single measurement and a single feature, the methods of batch association of measurements and features can provide more accurate association results in the state estimation stage of SLAM. In order to obtain a better association solution, a joint data association method based on heuristic search algorithm (HSA-JDA) is proposed to improve the robustness and accuracy of data association. In HSA-JDA, according to the joint maximum likelihood criterion, the data association problem is evolved into a combinatorial optimization problem of how to determine the optimal association set. A heuristic search algorithm that is an optimized artificial fish swarm algorithm by using adaptive step size and adding fish swarm jumping behavior is applied to search the optimal association solution. Experimental results show that HSA-JDA method ensures high association accuracy and then improves the robustness and accuracy of the whole state estimation results of SLAM. It can be used in the Laser-SLAM system based on Kalman filter to provide reliable association results for improving the accuracy of SLAM estimation results for unmanned delivery vehicle.

Machine Learning Allows the Identification of New Co-Mutational Patterns with Prognostic Implications in NPM1 Mutated AML - Results of the European Harmony Alliance

Background: Mutations of NPM1 are generally considered as a favorable prognostic marker in acute myeloid leukemia (AML), although patients present with distinct co-mutations that might influence outcomes. The recently updated European LeukemiaNet classification (ELN2022) does now group NPM1mut AML patients with FLT3-ITD (regardless of the allelic ratio) as intermediate prognosis, while NPM1mut without FLT3-ITD is part of the favorable group. However, the mutational landscape of NPM1mut AML is more complex and analysis of a large cohort of patients might allow further dissection to identify less frequent co-mutational patterns of prognostic and predictive importance. Aims: To identify clinically significant co-mutational patterns in NPM1mut AML to further refine risk stratification models for this subset of patients. Methods: From the HARMONY Alliance database, a total of 1093 intensively treated NPM1mut patients were selected. Patients treated with targeted therapies (e.g. FLT3 or IDH1/2 inhibitors) were not included and NPM1 MRD information was not available at this stage of the analysis. A machine learning algorithm was developed in order to identify combinations of up to 4 co-mutated genes with potential impact on overall survival (OS). A heuristic search algorithm was implemented and bootstrap sampling was applied to estimate the impact of all possible gene combinations on OS. In addition, a global dashboard was developed where it was possible to compare mutational combinations with Kaplan Meier and Cox regression models. Finally, clinically significant co-mutational patterns were summarized in a novel risk stratification model for NPM1mut AML, which was validated using a publicly available external patient cohort (Awada et al, Blood, 2021). Results: The study population of 1093 NPM1mut AML patients included 57% females and median age was 53 years. Regarding ELN2022 classification, 57% of patients were classified into the favorable, 42% into intermediate and only 1% into adverse risk groups. The most frequent co-mutations were DNMT3A (54%) and FLT3-ITD (42%), followed by NRAS, FLT3-TKD and TET2 (20% each). Mutations on IDH1 (13%) and IDH2 (15%) showed a similar behavior for all the analyses performed, so for simplification purposes we will refer to IDHmut when any of them was mutated and to IDHwt when both were wildtype. The triple combination of NPM1mut + FLT3-ITD + DNMT3Amut identified a subgroup of patients with adverse prognosis (2-year OS of 33%), similar to patients with TP53mut. Of note, not all FLT3-ITD patients carried an intermediate or adverse prognosis, as we were able to identify a subgroup (FLT3-ITD + IDHmut + DNMT3Awt) with excellent prognosis (2-year OS of 80%), which represented 4% of NPM1mut AML. However, in the absence of FLT3-ITD and the presence of DNMT3Amut, the addition of IDHmut decreased OS towards the intermediate risk (2-year OS 59%). Notably, not all DNMT3Amut patients carried an intermediate or adverse prognosis. In the absence of FLT3-ITD and with IDHwt, mutations on either NRAS, KRAS, PTPN11 or RAD21 revealed a subgroup with favorable prognosis even when DNMT3A was mutated (2-year OS 80%) and this group represented 11% of NPM1mut AML. This information is summarized in a 4-category risk stratification model (figure 1). The revised NPM1mut favorable group presented with a 3-year OS of 78%, which was 63%, 48% and 29% for intermediate-1, intermediate-2 and adverse risk groups respectively (p<0.001). Regarding 3-year relapse free survival (RFS), it was 71%, 59%, 39% and 26% accordingly (p<0.001). In the validation cohort, 3-year OS of was 73% for NPM1mut favorable group, being 59%, 40% and 22% for intermediate-1, intermediate-2 and adverse groups respectively (p<0.001). Multivariate OS analysis in NPM1mut AML identified the following independent prognostic factors: revised NPM1mut model (taking favorable group as reference, HR 1.77 for intermediate-1, HR 2.92 for intermediate-2 and HR 5.13 for adverse group, p<0.001); secondary or therapy-related AML (HR 1.93, p<0.001), age >60 years (HR 1.84, p<0.001), WBC at diagnosis >100x103/uL (HR 1.59, p<0.001), and male gender (HR 1.21, p=0.04). Conclusion/summary: Analysis of large NPM1mut AML cohorts allows the identification of clinically significant co-mutational patterns. We propose a new genetic stratification model for NPM1mut AML that identifies 4 groups with different OS and RFS. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Multi-equipment coordinated scheduling strategy of U-shaped automated container terminal considering energy consumption

In order to meet the green development trend of today's ports and accelerate the formation of green and low-carbon transportation modes. In this paper, the coordinated scheduling efficiency of quay crane, automatic double cantilever, intelligent guided transport vehicle and external truck in the multi-point loading and unloading mode of U-shaped automated container terminal is optimized to reduce the overall operation energy consumption of the terminal. A decision tree learning based on heuristic Monte Carlo tree search algorithm with the objectives of minimizing road conflicts and minimizing the total task energy consumption is proposed to solve the mixed integer planning model. The proposed regional coordinated scheduling strategy and flexible neighborhood search strategy effectively reduces operational conflicts and improves the optimal container scheduling sequence in the yard. The proposed predictive-based pruning strategy and other pruning strategy effectively improves the operational efficiency. This paper conducted experiments comparing the algorithms on a large scale and a small scale scheduling problems, and the total energy consumption value gap rate was reduced by 0.03%-14.15%. The results show that the proposed algorithm and model can not only effectively handle the conflicts between transportation equipment, but also further improve the transportation efficiency and reduce energy consumption.

UAV Formation Transformation Based on Two-Stage Path Planning Method

With the maturity of unmanned aerial vehicle (UAV) technology, the application of UAV in various fields is more in-depth. A method to quickly and accurately solve the flight paths of a large number of UAVs from one formation to another is crucial for application scenarios that require UAVs to perform formation transformation tasks. In this paper, the path planning of UAV swarm formation transformation is divided into two stages: rough planning and fine planning. In the rough planning stage, a 0-1 variable planning model is established to match the start and end points of the UAV flight path, and the model is solved using a Strengthen Elitist GA Algorithm to quickly obtain high-quality feasible solutions. In the fine planning stage, four models including behavioral mode, obstacle avoidance criterion, optimal waypoint selection and target exchange are established. On this basis, a heuristic fixed-step space search algorithm is designed and implemented to plan the path finely, and the path planning for the UAV formation transformation in two scenarios is finally completed.

A Novel Spatiotemporal Data Low-Rank Imputation Approach for Traffic Sensor Network

The Internet of Things (IoT) has enormous potential to transform the transport industry by improving passenger experiences, safety, and efficiency. However, the collected spatiotemporal data by traffic sensor network often suffer from missing values (MVs), which affect the overall performance of the system. As a result, accurate recovery of MVs is essential for the successful application of IoT in transportation. In this article, we propose a novel MVs imputation model by integrating low-rank tensor completion (LRTC) and sparse self-representation into a unified framework. In doing so, the global multidimensional correlation, as well as sample self-similarity, can be well leveraged for imputation. In order to solve the proposed model, an elaborate solution algorithm is developed, following the principle of alternating direction method of multipliers (ADMMs). Importantly, each step in ADMM can be implemented efficiently by analyzing the problem structure. Moreover, in order to select proper parameters for the model, an improved harmony search heuristic algorithm based on dual harmonies generation strategy is developed, thus sufficiently considering the information contained in current harmony memory. The experiments on two real-world traffic data are carried out to evaluate the proposed approach. The results verify that in comparison with the classic matrix/tensor completion and other competing algorithms, our method significantly improves the imputation performance.

Simple and efficient bi-objective search algorithms via fast dominance checks

Many interesting search problems can be formulated as bi-objective search problems, that is, search problems where two kinds of costs have to be minimized, for example, travel distance and time for transportation problems. Instead of looking for a single optimal path, we compute a Pareto-optimal frontier in bi-objective search, which is a set of paths in which no two paths dominate each other. Bi-objective search algorithms perform dominance checks each time a new path is discovered. Thus, the efficiency of these checks is key to performance. In this article, we propose algorithms for two kinds of bi-objective search problems. First, we consider the problem of computing the Pareto-optimal frontier of the paths that connect a given start state with a given goal state. We propose Bi-Objective A* (BOA*), a heuristic search algorithm based on A*, for this problem. Second, we consider the problem of computing one Pareto-optimal frontier for each state s of the search graph, which contains the paths that connect a given start state with s. We propose Bi-Objective Dijkstra (BOD), which is based on BOA*, for this problem. A common feature of BOA* and BOD is that all dominance checks are performed in constant time, unlike the dominance checks of previous algorithms. We show in our experimental evaluation that both BOA* and BOD are substantially faster than state-of-the-art bi-objective search algorithms.

Accurate Reliability Boundary Evaluation of Approximate Arithmetic Circuit

Approximate arithmetic circuit (AAC) has emerged as a promising high-performance and energy-efficient circuit paradigm, which can be used in many applications with inherent error tolerance. To guarantee the usability of AACs and the availability of resilient applications, it is necessary to analyze the reliability of AACs. Most current literature focus on the error characteristics of AACs and few methods can be applied to estimate the reliability of AACs. These methods mostly have exponential time complexities and evaluate the average reliability assuming the input combinations are equally likely. In reality, the primary input (PI) signals can be given with any probability from 0 to 1. In this article, we assume that the PIs have random signal probabilities and propose approaches to reliability boundary estimation for AACs. First, we propose a new efficient and accurate method to evaluate the reliability of AACs. The method mainly calculates the AAC reliability for an input vector set, and furthermore, during the calculation, the correlation problem is considered to increase accuracy. Then, based upon the proposed AAC reliability evaluation method, we present the approaches to finding the reliability boundary. Randomly given signal probabilities of every PI, two heuristic search algorithms are utilized to find the lowest reliability. A comparison of the results on three series of AACs and the circuits in the EvoApprox8b library confirms that the proposed reliability evaluation method is more accurate and efficient than the previous method. Further experiments verify the plausibility of the calculated reliability boundary of AACs.

Numerical Simulation and Assessment of Meta Heuristic Optimization Based Multi Objective Dynamic Job Shop Scheduling System

In today's world of manufacturing, cost reduction becomes one of the most important issues. A successful business needs to reduce its cost to be competitive. The programming of the machine is playing an important role in production planning and control as a tool to help manufacturers reduce their costs maximizing the use of their resources. The programming problem is not only limited to the programming of the machine, but also covers many other areas such such as computer and information technology and communication. From the definition, programming is an art that involves allocating, planning the allocation and utilization of resources to achieve a goal. The aim of the program is complete tasks in a reasonable amount of time. This reasonableness is a performance measure of how well the resources are allocated to tasks. Time or time-dependent functions are always it used as performance measures. The objectives of this research are to develop Intelligent Search Heuristic algorithms (ISHA) for equal and variable size sub lot for m machine flow shop problems, to Implement Particle Swarm Optimization algorithm (PSO) in matlab, to develop PSO based Optimization program for efficient job shop scheduling problem. The work also address solution to observe and verify results of PSO based Job Shop Scheduling with help of graft chart.

Implementation of heuristic search algorithms in the calibration of a river hydraulic model

In this contribution, two heuristic search algorithms, (1) particle swarm optimization (PSO) and (2) genetic algorithm (GA) are used to calibrate the iRIC, a two-dimensional hydraulic model for a test case on the Green River in Utah. The goal of the search algorithms was to find the combination of roughness values that minimized model error compared to the measured water surface elevation. An artificial neural network (ANN), which was trained based on the simulated water surface profiles obtained from iRIC, was used to measure the fitness of each solution. Implementation of PSO and GA both improved the accuracy of the calibration by 34 and 30 percent respectively relative to the trial and error. This research demonstrates that the implementation of heuristic search models provides an objective methodology for the calibration of hydraulic models with improved performance when compared to models calibrated via trial and error.

Truck–drone hybrid routing problem with time-dependent road travel time

Combining trucks and drones in package delivery provides a promising venue for a future logistics system that is more efficient and sustainable than the existing one. However, how to coordinate trucks and drones, particularly under uncertain traffic conditions (thus, travel time), remains a critical question in this field. To address this challenge, this study proposes and solves a truck–drone hybrid routing problem with time-dependent road travel time (TDHRP-TDRTT) to address the truck–drone cooperation issue. TDHRP-TDRTT is formulated as a cost minimization problem with constraints associated with logistics demand and supply. An iterative local search heuristic algorithm based on intra-pair and inter-pair customer exchanges and link re-optimization is developed to solve TDHRP-TDRTT. Our results on small-scale and benchmark instances show that the proposed algorithm has better computational performance than CPLEX solver, the adaptive large neighborhood search, hybrid genetic-sweep algorithm, and variable neighborhood search. A case study using traffic data from Chongqing, China shows that the truck–drone solution improves the timeliness of delivery, undertakes sensitivity analysis considering four road congestion states, significantly reduces trucking mileage, and facilitates overcoming terrain limitations. Therefore, the proposed model and algorithm are of practical significance in reducing operating cost, improving transportation efficiency, and facilitating a smart and sustainable urban logistics distribution system.

Application of Artificial Intelligence Computer Intelligent Heuristic Search Algorithm

In order to transform three-dimensional space path planning into two-dimensional plane path planning problem and greatly reduce the search time, an intelligent heuristic search algorithm based on artificial intelligence is proposed. The heuristic search algorithm is analyzed and introduced, and A is chosen. A two-dimensional spatial environment model of picking robot path planning is investigated, and a spatial model of picking robot path planning is established by raster method. Then, considering the whole day operation time, the whole day operation time is divided into several periods. With the help of heuristic search algorithm, the most reasonable operation time interval of each period is found, so as to provide reliable reference for the decision-making organization of urban rail transit operation on how to arrange the train rationally. The experimental results show that the improved A ∗ algorithm can significantly improve the moving path of the picking robot and make the planned path smoother, which confirms the feasibility and superiority of the improved algorithm. The operation decision of urban rail transit is obtained through experiments. After 114 iterations of the heuristic search algorithm, the optimal value is 6.83353635 e -001, while the average optimal value is 6.83551939 e -001. After 231 iterations of particle swarm optimization algorithm, the optimal value is 6.83650785 e -001. The average optimal value is 6.83745935 e -001. After 789 iterations, the genetic algorithm obtains the optimal value of 6.83921100 e -001, and the average optimal value is 6.84410765 e -001. Through the comparative analysis, it can be seen that compared with the other two optimization algorithms, the heuristic search algorithm is significantly better than the other two optimization algorithms, both in terms of the optimal value and the number of optimization iterations. The results show that the heuristic search algorithm is a fast, accurate, and reliable optimization method to solve the problem of accurate scheduling of urban rail transit departure interval. It is proved that the intelligent heuristic search algorithm of artificial intelligence computer can realize the path planning effectively.

A prediction model of wall shear stress for ultra-high-pressure water-jet nozzle based on hybrid BP neural network

ABSTRACT Two hybrid back-propagation neural network (BPNN) models optimized by two heuristic search algorithms, namely genetic algorithm (GA-BP) and particle swarm optimization (PSO-BP), are proposed in this paper to predict radial maximum wall shear stress instead of traditional computational fluid dynamics (CFD) methods. The two proposed models are trained and validated using a database of 150 radial maximum wall-shear-stress values obtained via CFD simulations. The best fit model is identified from various BPNN models based on the Akaike information criterion (AIC) and the Bayesian information criterion (BIC), balancing the trade-off between goodness-of-fit and model complexity. The model performance is evaluated by MAE, RMSE, regression coefficient (R), and Nash-Sutcliffe efficiency (NSE). The best fit model is a three-layered BPNN model consisting of a 4:4:1 topology. In almost all evaluation indicators, the two hybrid BPNN methods outperform three existing algorithms, namely classical BPNN, random forest (RF), and gradient boosting decision tree (GBDT). Both PSO-BP and GA-BP can provide a more precise assessment of radial maximum wall shear stress, with maximum errors being 5.81% and 8.24% respectively. The proposed PSO-BP prediction model is promising and has great feasibility in predicting the radial maximum wall shear stress of UHP water-jet nozzle in engineering applications.

Taşkın Duyarlılık Haritalarının Oluşturulmasında Kullanılan Yöntemler

In recent years, flood susceptibility mapping has an important place among the studies carried out to take precautions against floods and mitigate the damages and possible negative effects caused by floods. In this context, flood susceptibility analysis, especially on a regional scale, has been the subject of research by many researchers. In this study, the methods used in flood susceptibility mapping were investigated. 155 studies on flood susceptibility published between 2014 and 2022 were evaluated. In general, the methods used in the determination and evaluation of flood susceptibility are multi-criteria decision making (MCDM) methods, physically based hydrological models, statistical methods and various soft computing methods. Although the use rate of traditional statistical methods and multi-criteria decision making methods is already high among researchers, the methods used in flood susceptibility analysis have evolved over the years from traditional human judgments to statistical methods based on big data and machine learning methods. In the reviewed studies, it has been observed that machine learning, fuzzy logic, metaheuristic optimization algorithms and heuristic search algorithms, which are soft computing methods, have been widely used in the flood susceptibility mapping in recent years.