Design Analysis and Implementation of Stock Market Forecasting System using Improved Soft Computing Technique

In this paper, a stock market prediction model was created utilizing artificial neural networks. Many people nowadays are attempting to predict future trends in bonds, currencies, equities, and stock markets. It is quite challenging for a capitalist and an industry to forecast changes in stock market prices. Due to the numerous economic, political, and psychological aspects at play, forecasting future value changes on the stock markets is quite challenging. In addition, stock market forecasting is a difficult endeavor because it relies on a wide range of known and unknown variables. Many approaches, including technical analysis, fundamental analysis, time series analysis, and statistical analysis are used to attempt to predict the share price; however, none of these methods has been demonstrated to be a consistently effective prediction tool. Artificial neural networks (ANNs), a subfield of artificial intelligence, are one of the most modern and promising methods for resolving financial issues, such as categorizing corporate bonds and anticipating stock market indexes and bankruptcy (AI). Artificial neural networks (ANN) are a prominent technology used to forecast the future of the stock market. In order to understand financial time series, it is often essential to extract relevant information from enormous data sets using artificial neural networks. An outcome prediction neural network with three layers is trained using the back propagation method. Analysis shows that ANN outperforms every other prediction technique now available to academics in terms of stock market price predictions. It is concluded that ANN is a useful technique for predicting stock market movements globally

Design and Modeling of Stock Market Forecasting Using Hybrid Optimization Techniques

In this paper, an artificial neural network-based stock market prediction model was developed. Today, a lot of individuals are making predictions about the direction of the bond, currency, equity, and stock markets. Forecasting fluctuations in stock market values is quite difficult for businesspeople and industries. Forecasting future value changes on the stock markets is exceedingly difficult since there are so many different economic, political, and psychological factors at play. Stock market forecasting is also a difficult endeavour since it depends on so many various known and unknown variables. There are several ways used to try to anticipate the share price, including technical analysis, fundamental analysis, time series analysis, and statistical analysis; however, none of these approaches has been shown to be a consistently reliable prediction tool. We built three alternative Adaptive Neuro-Fuzzy Inference System (ANFIS) models to compare the outcomes. The average of the tuned models is used to create an ensemble model. Although comparable applications have been attempted in the literature, the data set is extremely difficult to work with because it only contains sharp peaks and falls with no seasonality. In this study, fuzzy c-means clustering, subtractive clustering, and grid partitioning are all used. The experiments we ran were designed to assess the effectiveness of various construction techniques used to our ANFIS models. When evaluating the outcomes, the metrics of R-squared and mean standard error are mostly taken into consideration. In the experiments, R-squared values of over.90 are attained

Designing a Novel Model for Stock Price Prediction Using an Integrated Multi-Stage Structure: The Case of the Bombay Stock Exchange

Stock price prediction is considered a strategic and challenging issue in the stock markets. Considering the complexity of stock market data and price fluctuations, the improvement of effective approaches for stock price prediction is a crucial and essential task. Therefore, in this study, a new model based on “Adaptive Neuro-Fuzzy Inference System (ANFIS), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA)” is employed to predict stock price accurately. ANFIS has been utilized to predict stock price trends more precisely. PSO executes towards developing the vector, and GA has been utilized to adjust the decision vectors employing genetic operators. The stock price data of top companies of the Bombay Stock Exchange (BSE) from 2010 to 2020 are employed to analyze the model functionality. Experimental outcomes demonstrated that the average functionality of our model (77.62%) was achieved noticeably better than other methods. The findings verified that the ANFIS-PSO-GA model is an efficient tool in stock price prediction which can be applied in the different financial markets, especially the stock market

A survey on financial applications of metaheuristics

Modern heuristics or metaheuristics are optimization algorithms that have been increasingly used during the last decades to support complex decision-making in a number of fields, such as logistics and transportation, telecommunication networks, bioinformatics, finance, and the like. The continuous increase in computing power, together with advancements in metaheuristics frameworks and parallelization strategies, are empowering these types of algorithms as one of the best alternatives to solve rich and real-life combinatorial optimization problems that arise in a number of financial and banking activities. This article reviews some of the works related to the use of metaheuristics in solving both classical and emergent problems in the finance arena. A non-exhaustive list of examples includes rich portfolio optimization, index tracking, enhanced indexation, credit risk, stock investments, financial project scheduling, option pricing, feature selection, bankruptcy and financial distress prediction, and credit risk assessment. This article also discusses some open opportunities for researchers in the field, and forecast the evolution of metaheuristics to include real-life uncertainty conditions into the optimization problems being considered.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (TRA2013-48180-C3-P, TRA2015-71883-REDT), FEDER, and the Universitat Jaume I mobility program (E-2015-36)

A comprehensive comparison of the performance of metaheuristic algorithms in neural network training for nonlinear system identification

Many problems in daily life exhibit nonlinear behavior. Therefore, it is important to solve nonlinear problems. These problems are complex and difficult due to their nonlinear nature. It is seen in the literature that different artificial intelligence techniques are used to solve these problems. One of the most important of these techniques is artificial neural networks. Obtaining successful results with an artificial neural network depends on its training process. In other words, it should be trained with a good training algorithm. Especially, metaheuristic algorithms are frequently used in artificial neural network training due to their advantages. In this study, for the first time, the performance of sixteen metaheuristic algorithms in artificial neural network training for the identification of nonlinear systems is analyzed. It is aimed to determine the most effective metaheuristic neural network training algorithms. The metaheuristic algorithms are examined in terms of solution quality and convergence speed. In the applications, six nonlinear systems are used. The mean-squared error (MSE) is utilized as the error metric. The best mean training error values obtained for six nonlinear systems were 3.5×10−4, 4.7×10−4, 5.6×10−5, 4.8×10−4, 5.2×10−4, and 2.4×10−3, respectively. In addition, the best mean test error values found for all systems were successful. When the results were examined, it was observed that biogeography-based optimization, moth–flame optimization, the artificial bee colony algorithm, teaching–learning-based optimization, and the multi-verse optimizer were generally more effective than other metaheuristic algorithms in the identification of nonlinear systems

A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem

[EN] The counterfort retaining wall is one of the most frequent structures used in civil engineering. In this structure, optimization of cost and CO2 emissions are important. The first is relevant in the competitiveness and efficiency of the company, the second in environmental impact. From the point of view of computational complexity, the problem is challenging due to the large number of possible combinations in the solution space. In this article, a k-means cuckoo search hybrid algorithm is proposed where the cuckoo search metaheuristic is used as an optimization mechanism in continuous spaces and the unsupervised k-means learning technique to discretize the solutions. A random operator is designed to determine the contribution of the k-means operator in the optimization process. The best values, the averages, and the interquartile ranges of the obtained distributions are compared. The hybrid algorithm was later compared to a version of harmony search that also solved the problem. The results show that the k-mean operator contributes significantly to the quality of the solutions and that our algorithm is highly competitive, surpassing the results obtained by harmony search.The first author was supported by the Grant CONICYT/FONDECYT/INICIACION/11180056, the other two authors were supported by the Spanish Ministry of Economy and Competitiveness, along with FEDER funding (Project: BIA2017-85098-R).García, J.; Yepes, V.; Martí Albiñana, JV. (2020). A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem. Mathematics. 8(4):1-22. https://doi.org/10.3390/math8040555S12284García, J., Altimiras, F., Peña, A., Astorga, G., & Peredo, O. (2018). A Binary Cuckoo Search Big Data Algorithm Applied to Large-Scale Crew Scheduling Problems. Complexity, 2018, 1-15. doi:10.1155/2018/8395193García, J., Moraga, P., Valenzuela, M., Crawford, B., Soto, R., Pinto, H., … Astorga, G. (2019). A Db-Scan Binarization Algorithm Applied to Matrix Covering Problems. Computational Intelligence and Neuroscience, 2019, 1-16. doi:10.1155/2019/3238574Al-Madi, N., Faris, H., & Mirjalili, S. (2019). Binary multi-verse optimization algorithm for global optimization and discrete problems. International Journal of Machine Learning and Cybernetics, 10(12), 3445-3465. doi:10.1007/s13042-019-00931-8Kim, M., & Chae, J. (2019). Monarch Butterfly Optimization for Facility Layout Design Based on a Single Loop Material Handling Path. Mathematics, 7(2), 154. doi:10.3390/math7020154García, J., Crawford, B., Soto, R., & Astorga, G. (2019). A clustering algorithm applied to the binarization of Swarm intelligence continuous metaheuristics. Swarm and Evolutionary Computation, 44, 646-664. doi:10.1016/j.swevo.2018.08.006García, J., Lalla-Ruiz, E., Voß, S., & Droguett, E. L. (2020). Enhancing a machine learning binarization framework by perturbation operators: analysis on the multidimensional knapsack problem. International Journal of Machine Learning and Cybernetics, 11(9), 1951-1970. doi:10.1007/s13042-020-01085-8García, J., Moraga, P., Valenzuela, M., & Pinto, H. (2020). A db-Scan Hybrid Algorithm: An Application to the Multidimensional Knapsack Problem. Mathematics, 8(4), 507. doi:10.3390/math8040507Saeheaw, T., & Charoenchai, N. (2018). A comparative study among different parallel hybrid artificial intelligent approaches to solve the capacitated vehicle routing problem. International Journal of Bio-Inspired Computation, 11(3), 171. doi:10.1504/ijbic.2018.091704Valdez, F., Castillo, O., Jain, A., & Jana, D. K. (2019). Nature-Inspired Optimization Algorithms for Neuro-Fuzzy Models in Real-World Control and Robotics Applications. Computational Intelligence and Neuroscience, 2019, 1-2. doi:10.1155/2019/9128451Penadés-Plà, V., García-Segura, T., & Yepes, V. (2020). Robust Design Optimization for Low-Cost Concrete Box-Girder Bridge. Mathematics, 8(3), 398. doi:10.3390/math8030398García-Segura, T., Yepes, V., Frangopol, D. M., & Yang, D. Y. (2017). Lifetime reliability-based optimization of post-tensioned box-girder bridges. Engineering Structures, 145, 381-391. doi:10.1016/j.engstruct.2017.05.013Yepes, V., Martí, J. V., & García, J. (2020). Black Hole Algorithm for Sustainable Design of Counterfort Retaining Walls. Sustainability, 12(7), 2767. doi:10.3390/su12072767Marti-Vargas, J. R., Ferri, F. J., & Yepes, V. (2013). Prediction of the transfer length of prestressing strands with neural networks. Computers and Concrete, 12(2), 187-209. doi:10.12989/cac.2013.12.2.187Fu, W., Tan, J., Zhang, X., Chen, T., & Wang, K. (2019). Blind Parameter Identification of MAR Model and Mutation Hybrid GWO-SCA Optimized SVM for Fault Diagnosis of Rotating Machinery. Complexity, 2019, 1-17. doi:10.1155/2019/3264969Sierra, L. A., Yepes, V., García-Segura, T., & Pellicer, E. (2018). Bayesian network method for decision-making about the social sustainability of infrastructure projects. Journal of Cleaner Production, 176, 521-534. doi:10.1016/j.jclepro.2017.12.140Crawford, B., Soto, R., Astorga, G., García, J., Castro, C., & Paredes, F. (2017). Putting Continuous Metaheuristics to Work in Binary Search Spaces. Complexity, 2017, 1-19. doi:10.1155/2017/8404231Hatamlou, A. (2013). Black hole: A new heuristic optimization approach for data clustering. Information Sciences, 222, 175-184. doi:10.1016/j.ins.2012.08.023Pan, W.-T. (2012). A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example. Knowledge-Based Systems, 26, 69-74. doi:10.1016/j.knosys.2011.07.001Rashedi, E., Nezamabadi-pour, H., & Saryazdi, S. (2009). GSA: A Gravitational Search Algorithm. Information Sciences, 179(13), 2232-2248. doi:10.1016/j.ins.2009.03.004Calvet, L., Armas, J. de, Masip, D., & Juan, A. A. (2017). Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs. Open Mathematics, 15(1), 261-280. doi:10.1515/math-2017-0029Talbi, E.-G. (2015). Combining metaheuristics with mathematical programming, constraint programming and machine learning. Annals of Operations Research, 240(1), 171-215. doi:10.1007/s10479-015-2034-yJuan, A. A., Faulin, J., Grasman, S. E., Rabe, M., & Figueira, G. (2015). A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems. Operations Research Perspectives, 2, 62-72. doi:10.1016/j.orp.2015.03.001Chou, J.-S., & Nguyen, T.-K. (2018). Forward Forecast of Stock Price Using Sliding-Window Metaheuristic-Optimized Machine-Learning Regression. IEEE Transactions on Industrial Informatics, 14(7), 3132-3142. doi:10.1109/tii.2018.2794389Sayed, G. I., Tharwat, A., & Hassanien, A. E. (2018). Chaotic dragonfly algorithm: an improved metaheuristic algorithm for feature selection. Applied Intelligence, 49(1), 188-205. doi:10.1007/s10489-018-1261-8De León, A. D., Lalla-Ruiz, E., Melián-Batista, B., & Marcos Moreno-Vega, J. (2017). A Machine Learning-based system for berth scheduling at bulk terminals. Expert Systems with Applications, 87, 170-182. doi:10.1016/j.eswa.2017.06.010García, J., Crawford, B., Soto, R., Castro, C., & Paredes, F. (2017). A k-means binarization framework applied to multidimensional knapsack problem. Applied Intelligence, 48(2), 357-380. doi:10.1007/s10489-017-0972-6Molina-Moreno, F., Martí, J. V., & Yepes, V. (2017). Carbon embodied optimization for buttressed earth-retaining walls: Implications for low-carbon conceptual designs. Journal of Cleaner Production, 164, 872-884. doi:10.1016/j.jclepro.2017.06.246Asta, S., Özcan, E., & Curtois, T. (2016). A tensor based hyper-heuristic for nurse rostering. Knowledge-Based Systems, 98, 185-199. doi:10.1016/j.knosys.2016.01.031Martin, S., Ouelhadj, D., Beullens, P., Ozcan, E., Juan, A. A., & Burke, E. K. (2016). A multi-agent based cooperative approach to scheduling and routing. European Journal of Operational Research, 254(1), 169-178. doi:10.1016/j.ejor.2016.02.045Ghazali, R., Deris, M. M., Nawi, N. M., & Abawajy, J. H. (Eds.). (2018). Recent Advances on Soft Computing and Data Mining. Advances in Intelligent Systems and Computing. doi:10.1007/978-3-319-72550-5Veček, N., Mernik, M., Filipič, B., & Črepinšek, M. (2016). Parameter tuning with Chess Rating System (CRS-Tuning) for meta-heuristic algorithms. Information Sciences, 372, 446-469. doi:10.1016/j.ins.2016.08.066Ries, J., & Beullens, P. (2015). A semi-automated design of instance-based fuzzy parameter tuning for metaheuristics based on decision tree induction. Journal of the Operational Research Society, 66(5), 782-793. doi:10.1057/jors.2014.46Yalcinoz, T., & Altun, H. (2001). Power economic dispatch using a hybrid genetic algorithm. IEEE Power Engineering Review, 21(3), 59-60. doi:10.1109/39.911360Kaur, H., Virmani, J., Kriti, & Thakur, S. (2019). A genetic algorithm-based metaheuristic approach to customize a computer-aided classification system for enhanced screen film mammograms. U-Healthcare Monitoring Systems, 217-259. doi:10.1016/b978-0-12-815370-3.00010-4Faris, H., Hassonah, M. A., Al-Zoubi, A. M., Mirjalili, S., & Aljarah, I. (2017). A multi-verse optimizer approach for feature selection and optimizing SVM parameters based on a robust system architecture. Neural Computing and Applications, 30(8), 2355-2369. doi:10.1007/s00521-016-2818-2Faris, H., Aljarah, I., & Mirjalili, S. (2017). Improved monarch butterfly optimization for unconstrained global search and neural network training. Applied Intelligence, 48(2), 445-464. doi:10.1007/s10489-017-0967-3Chou, J.-S., & Thedja, J. P. P. (2016). Metaheuristic optimization within machine learning-based classification system for early warnings related to geotechnical problems. Automation in Construction, 68, 65-80. doi:10.1016/j.autcon.2016.03.015Pham, A.-D., Hoang, N.-D., & Nguyen, Q.-T. (2016). Predicting Compressive Strength of High-Performance Concrete Using Metaheuristic-Optimized Least Squares Support Vector Regression. Journal of Computing in Civil Engineering, 30(3), 06015002. doi:10.1061/(asce)cp.1943-5487.0000506Göçken, M., Özçalıcı, M., Boru, A., & Dosdoğru, A. T. (2016). Integrating metaheuristics and Artificial Neural Networks for improved stock price prediction. Expert Systems with Applications, 44, 320-331. doi:10.1016/j.eswa.2015.09.029Chou, J.-S., & Pham, A.-D. (2017). Nature-inspired metaheuristic optimization in least squares support vector regression for obtaining bridge scour information. Information Sciences, 399, 64-80. doi:10.1016/j.ins.2017.02.051Kuo, R. J., Lin, T. C., Zulvia, F. E., & Tsai, C. Y. (2018). A hybrid metaheuristic and kernel intuitionistic fuzzy c-means algorithm for cluster analysis. Applied Soft Computing, 67, 299-308. doi:10.1016/j.asoc.2018.02.039Singh Mann, P., & Singh, S. (2017). Energy efficient clustering protocol based on improved metaheuristic in wireless sensor networks. Journal of Network and Computer Applications, 83, 40-52. doi:10.1016/j.jnca.2017.01.031Rosa, R. de A., Machado, A. M., Ribeiro, G. M., & Mauri, G. R. (2016). A mathematical model and a Clustering Search metaheuristic for planning the helicopter transportation of employees to the production platforms of oil and gas. Computers & Industrial Engineering, 101, 303-312. doi:10.1016/j.cie.2016.09.006Faris, H., Mirjalili, S., & Aljarah, I. (2019). Automatic selection of hidden neurons and weights in neural networks using grey wolf optimizer based on a hybrid encoding scheme. International Journal of Machine Learning and Cybernetics, 10(10), 2901-2920. doi:10.1007/s13042-018-00913-2De Rosa, G. H., Papa, J. P., & Yang, X.-S. (2017). Handling dropout probability estimation in convolution neural networks using meta-heuristics. Soft Computing, 22(18), 6147-6156. doi:10.1007/s00500-017-2678-4Rere, L. M. R., Fanany, M. I., & Arymurthy, A. M. (2016). Metaheuristic Algorithms for Convolution Neural Network. Computational Intelligence and Neuroscience, 2016, 1-13. doi:10.1155/2016/1537325Jothi, R., Mohanty, S. K., & Ojha, A. (2017). DK-means: a deterministic K-means clustering algorithm for gene expression analysis. Pattern Analysis and Applications, 22(2), 649-667. doi:10.1007/s10044-017-0673-0García, J., Pope, C., & Altimiras, F. (2017). A Distributed K-Means Segmentation Algorithm Applied to Lobesia botrana Recognition. Complexity, 2017, 1-14. doi:10.1155/2017/5137317Arunkumar, N., Mohammed, M. A., Abd Ghani, M. K., Ibrahim, D. A., Abdulhay, E., Ramirez-Gonzalez, G., & de Albuquerque, V. H. C. (2018). K-Means clustering and neural network for object detecting and identifying abnormality of brain tumor. Soft Computing, 23(19), 9083-9096. doi:10.1007/s00500-018-3618-7Abdel-Basset, M., Wang, G.-G., Sangaiah, A. K., & Rushdy, E. (2017). Krill herd algorithm based on cuckoo search for solving engineering optimization problems. Multimedia Tools and Applications, 78(4), 3861-3884. doi:10.1007/s11042-017-4803-xChi, R., Su, Y., Zhang, D., Chi, X., & Zhang, H. (2017). A hybridization of cuckoo search and particle swarm optimization for solving optimization problems. Neural Computing and Applications, 31(S1), 653-670. doi:10.1007/s00521-017-3012-xLi, J., Xiao, D., Lei, H., Zhang, T., & Tian, T. (2020). Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location. Mathematics, 8(2), 149. doi:10.3390/math8020149Pan, J.-S., Song, P.-C., Chu, S.-C., & Peng, Y.-J. (2020). Improved Compact Cuckoo Search Algorithm Applied to Location of Drone Logistics Hub. Mathematics, 8(3), 333. doi:10.3390/math8030333Yepes, V., Alcala, J., Perea, C., & González-Vidosa, F. (2008). A parametric study of optimum earth-retaining walls by simulated annealing. Engineering Structures, 30(3), 821-830. doi:10.1016/j.engstruct.2007.05.023Molina-Moreno, F., García-Segura, T., Martí, J. V., & Yepes, V. (2017). Optimization of buttressed earth-retaining walls using hybrid harmony search algorithms. Engineering Structures, 134, 205-216. doi:10.1016/j.engstruct.2016.12.04

The buttressed walls problem: An application of a hybrid clustering particle swarm optimization algorithm

[EN] The design of reinforced earth retaining walls is a combinatorial optimization problem of interest due to practical applications regarding the cost savings involved in the design and the optimization in the amount of CO2 emissions generated in its construction. On the other hand, this problem presents important challenges in computational complexity since it involves 32 design variables; therefore we have in the order of 10^20 possible combinations. In this article, we propose a hybrid algorithm in which the particle swarm optimization method is integrated that solves optimization problems in continuous spaces with the db-scan clustering technique, with the aim of addressing the combinatorial problem of the design of reinforced earth retaining walls. This algorithm optimizes two objective functions: the carbon emissions embedded and the economic cost of reinforced concrete walls. To assess the contribution of the db-scan operator in the optimization process, a random operator was designed. The best solutions, the averages, and the interquartile ranges of the obtained distributions are compared. The db-scan algorithm was then compared with a hybrid version that uses k-means as the discretization method and with a discrete implementation of the harmony search algorithm. The results indicate that the db-scan operator significantly improves the quality of the solutions and that the proposed metaheuristic shows competitive results with respect to the harmony search algorithm.The first author was supported by the Grant CONICYT/FONDECYT/INICIACION/11180056, the other two authors were supported by the Spanish Ministry of Economy and Competitiveness, along with FEDER funding (Project: BIA2017-85098-R).Garcia, J.; Martí Albiñana, JV.; Yepes, V. (2020). The buttressed walls problem: An application of a hybrid clustering particle swarm optimization algorithm. Mathematics. 8(6):862-01-862-22. https://doi.org/10.3390/math8060862S862-01862-228

Stock price prediction with optimized deep LSTM network with artificial rabbits optimization algorithm

Algorithms and the Foundations of Software technolog

Prediction of Stocks and Stock Price using Artificial Intelligence : A Bibliometric Study using Scopus Database

Prediction of stocks and the prices of the stock is one of the most crucial points of discussion amongst the researchers and analysts in the financial domain to date. Every stakeholder and most importantly the investor desires to earn higher profit for his investment in the market and try to use several different strategies to invest their money. There are numerous methods to predict and analyse the movement of the stock prices. They are broadly divided into – statistical and artificial intelligence-based methods. Artificial intelligence is used to predict the futuristic prices of stocks and use wide range of algorithms like – SVMs, CNNs, LSTMs, RNNs , etc. This bibliometric study focusses on the study based primarily on the Scopus database. We have considered important keywords, authors, citations along with the correlations between the co-appearing authors, source titles and keywords with the use of network diagrams for visualisation. On the basis of this paper, we conclude that there is ample opportunity for research in the domain of financial market

A novel hybrid swarm optimized multilayer neural network for spatial prediction of flash floods in tropical areas using sentinel-1 SAR imagery and geospatial data

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. Flash floods are widely recognized as one of the most devastating natural hazards in the world, therefore prediction of flash flood-prone areas is crucial for public safety and emergency management. This research proposes a new methodology for spatial prediction of flash floods based on Sentinel-1 SAR imagery and a new hybrid machine learning technique. The SAR imagery is used to detect flash flood inundation areas, whereas the new machine learning technique, which is a hybrid of the firefly algorithm (FA), Levenberg–Marquardt (LM) backpropagation, and an artificial neural network (named as FA-LM-ANN), was used to construct the prediction model. The Bac Ha Bao Yen (BHBY) area in the northwestern region of Vietnam was used as a case study. Accordingly, a Geographical Information System (GIS) database was constructed using 12 input variables (elevation, slope, aspect, curvature, topographic wetness index, stream power index, toposhade, stream density, rainfall, normalized difference vegetation index, soil type, and lithology) and subsequently the output of flood inundation areas was mapped. Using the database and FA-LM-ANN, the flash flood model was trained and verified. The model performance was validated via various performance metrics including the classification accuracy rate, the area under the curve, precision, and recall. Then, the flash flood model that produced the highest performance was compared with benchmarks, indicating that the combination of FA and LM backpropagation is proven to be very effective and the proposed FA-LM-ANN is a new and useful tool for predicting flash flood susceptibility