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Flood mapping based on novel ensemble modeling involving the deep learning, Harris Hawka* optimization algorithm and stacking based machine learning

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APPLIED WATER SCIENCE 2024年第4期14卷 78页

作者： Costache, Romulus Pal, Subodh Chandra Pande, Chaitanya B. Islam, Abu Reza Md. Towfiqul Alshehri, Fahad Abdo, Hazem Ghassan Transilvania Univ Brasov Dept Civil Engn 5 Turnului Str Brasov 500152 Romania Danube Delta Natl Inst Res & Dev 165 Babadag St Tulcea 820112 Romania Natl Inst Hydrol & Water Management Bucuresti Ploiesti Rd 97E Bucharest 013686 Romania Univ Bucharest Res Inst 90-92 Sos Panduri Bucharest 050107 Romania Univ Burdwan Dept Geog Bardhaman 713104 West Bengal India Al Ayen Univ Sci Res Ctr New Era & Dev Civil Engn Res Grp Thi Qar 64001 Iraq Univ Tenaga Nas Inst Energy Infrastructure Kajang 43000 Malaysia King Saud Univ Coll Sci Geol & Geophys Dept Abdullah Alrushaid Chair Earth Sci Remote Sensing Riyadh Saudi Arabia Begum Rokeya Univ Dept Disaster Management Rangpur 5400 Bangladesh Daffodil Int Univ Dept Dev Studies Dhaka 1216 Bangladesh Tartous Univ Fac Arts & Humanities Geog Dept Tartous Syria

Among the various natural disasters that take place around the world, flood is considered to be the most extensive. There have been several floods in Buzau river basin, and as a result of this, the area has been chosen as the study area. For the purpose of this research, we applied deep learning and machine learning benchmarks in order to prepare flood potential maps at the basin scale. In this regard 12 flood predictors, 205 flood and 205 non-flood locations were used as input data into the following 3 complex models: Deep Learning Neural Network-Harris Hawk optimization-Index of Entropy (DLNN-HHO-IOE), Multilayer Perceptron-Harris Hawk optimization-Index of Entropy (MLP-HHO-IOE) and Stacking ensemble-Harris Hawk optimization-Index of Entropy (Stacking-HHO-IOE). The flood sample was divided into training (70%) and validating (30%) sample, meanwhile the prediction ability of flood conditioning factors was tested through the Correlation-based Feature Selection method. ROC Curve and statistical metrics were involved in the results validation. The modeling process through the stated algorithms showed that the most important flood predictors are represented by: slope (importance approximate to 20%), distance from river (importance approximate to 17.5%), land use (importance approximate to 12%) and TPI (importance approximate to 10%). The importance values were used to compute the flood susceptibility, while Natural Breaks method was used to classify the results. The high and very high flood susceptibility is spread on approximately 35-40% of the study zone. The ROC Curve, in terms of Success, Rate shows that the highest performance was achieved FPIDLNN-HHO-IOE (AUC = 0.97), followed by FPIStacking-HHO-IOE (AUC = 0.966) and FPIMLP-HHO-IOE (AUC = 0.953), while the Prediction Rate indicates the FPIStacking-HHO-IOE as being the most performant model with an AUC of 0.977, followed by FPIDLNN-HHO-IOE (AUC = 0.97) and FPIMLP-HHO-IOE (AUC = 0.924).

关键词： Artificial intelligence optimization algorithm Flood potential Buzau river basin Romania

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Performance evaluation of nanotubular halloysites from weathered pegmatites in removing heavy metals from water through novel artificial intelligence-based models and human-baseda* optimization algorithm

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CHEMOSPHERE 2021年 282卷 131012-131012页

作者： Bui Hoang Bac Hoang Nguyen Nguyen Thi Thanh Thao Le Thi Duyen Vo Thi Hanh Nguyen Tien Dung Luong Quang Khang Do Manh An Hanoi Univ Min & Geol Fac Geosci & Geoengn Dept Explorat Geol Duc Thang Ward 18 Vien St Hanoi Vietnam Hanoi Univ Min & Geol Ctr Excellence Anal & Expt Duc Thang Ward 18 Vien St Hanoi Vietnam Hanoi Univ Min & Geol Min Fac Dept Surface Min Duc Thang Ward 18 Vien St Hanoi Vietnam Hanoi Univ Min & Geol Ctr Min Electromech Res Duc Thang Ward 18 Vien St Hanoi Vietnam Hanoi Univ Min & Geol Fac Basic Sci Dept Chem Duc Thang Ward 18 Vien St Hanoi Vietnam

The efforts of this study aimed to evaluate the feasibility of the nanotubular halloysites in weathered pegmatites (NaHWP) for removing heavy metals (i.e., Cd2+, Pb2+) from water. Furthermore, two novel intelligent models, such as teaching-learning-based optimization (TLBO)-artificial neural network (ANN), and TLBO-support vector regression (SVR), named as TLBO-ANN and TLBO-SVR models, respectively, were proposed to predict the Cd2+ and Pb2+ absorption efficiencies from water using the NaHWP absorbent. Databases used, including 53 experiments for Pb2+ absorption and 56 experiments for Cd2+ absorption from water, under the catalysis of different conditions, such as initial concentration of Pb2+ and Cd2+, solution pH, adsorbent weight, and contact time. Subsequently, the TLBO-ANN and TLBO-SVR models were developed and applied to predict the efficiencies of Cd2+ and Pb2+ absorption from water, aiming to evaluate the role as well as the effects of different conditions on the absorption efficiencies using the NaHWP absorbent. The standalone ANN and SVM models were also taken into consideration and compared with the proposed hybrid models (i.e., TLBO-ANN and TLBO-SVR). The results showed that the NaHWP detected in a Kaolin mine (Vietnam) with 70% nanotubular halloysites is a potential adsorbent for water treatment to eliminate heavy metals from water. The two novel hybrid models proposed, i.e., TLBO-ANN and TLBO-SVR, also yielded the dominant performances and accuracies in predicting the Cd2+ and Pb2+ absorption efficiencies from water, i.e., RMSE = 1.190 and 1.102, R-2 = 0.951 and 0.957, VAF = 94.436 and 95.028 for the TLBO-ANN and TLBO-SVR models, respectively, in predicting the Pb2+ absorption efficiency from water;RMSE = 3.084 and 3.442, R-2 = 0.971 and 0.965, VAF = 96.499 and 96.415 for the TLBO-ANN and TLBOSVR models, respectively, in predicting the Cd2+ absorption efficiency from water. Furthermore, the validation results also demonstrated these findings in practic

关键词： Heavy metals Water treatment Nanotubular halloysites Weathered pegmatites Artificial intelligence optimization algorithm

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Accuracy assessment and improvement of SRTM, ASTER, FABDEM, and MERIT DEMs by polynomial anda* optimization algorithm: A case study (Khuzestan Province, Iran)

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OPEN GEOSCIENCES 2023年第1期15卷

作者： Saberi, Azim Kabolizadeh, Mostafa Rangzan, Kazem Abrehdary, Majid Shahid Chamran Univ Ahvaz Fac Earth Sci Dept RS & GIS Ahvaz Iran Univ West Trollhattan Sweden

Satellite digital elevation models (DEMs) are used for decision-making in various fields. Therefore, evaluating and improving vertical accuracy of DEM can increase the quality of end products. This article aimed to increase the vertical accuracy of most popular satellite DEMs (i.e., the ASTER, Shuttle Radar Topography Mission [SRTM], Forest And Buildings removed Copernicus DEM [FABDEM], and Multi-Error-Removed Improved-Terrain [MERIT]) using the particle swarm optimization (PSO) algorithm. For this purpose, at first, the vertical error of DEMs was estimated via ground truth data. Next, a second-order polynomial was applied to model the vertical error in the study area. To select the polynomial with the highest accuracy, employed for vertical error modeling, the coefficients of the polynomial have been optimized using the PSO algorithm. Finally, the efficiency of the proposed algorithm has been evaluated by other ground truth data and in situ observations. The results show that the mean absolute error (MAE) of SRTM DEM is 4.83 m while this factor for ASTER DEM is 5.35 m, for FABDEM is 4.28, and for MERIT is 3.87. The obtained results indicated that the proposed model could improve the MAE of vertical accuracy of SRTM, ASTER, FABDEM, and MERIT DEMs to 0.83, 0.51, 0.37, and 0.29 m, respectively.

关键词： SRTM ASTER DEM DEM accuracy assessment optimization algorithm DEM accuracy improvement

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Study on a Hybrid Hydrological Forecasting Model SCE-GUH by Coupling SCE-Ua* optimization algorithm and General Unit Hydrograph

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WATER 2023年第15期15卷 2783-2783页

作者： Xu, Yingying Liu, Chengshuai Yu, Qiying Zhao, Chenchen Quan, Liyu Hu, Caihong Zhengzhou Univ Yellow River Lab Zhengzhou 450001 Peoples R China

Implementing real-time prediction and warning systems is an effective approach for mitigating flash flood disasters. However, there is still a challenge in improving the accuracy and reliability of flood prediction models. This study develops a hydrological prediction model named SCE-GUH, which combines the Shuffled Complex Evolution-University of Arizona* optimization algorithm with the general unit hydrograph routing method. Our aims were to investigate the applicability of the general unit hydrograph in runoff calculations and its performance in predicting flash flood events. Furthermore, we examined the influence of parameter variations in the general unit hydrograph on flood simulations and conducted a comparative analysis with the conventional Nash unit hydrograph. The research findings demonstrate that the utilization of the general unit hydrograph method can considerably decrease computational errors and enhance prediction accuracy. The flood peak detection rate was found to be 100% in all four study watersheds. The average Nash-Sutcliffe efficiency coefficients were 0.83, 0.83, 0.84, and 0.87, while the corresponding coefficients of determination were 0.86, 0.85, 0.86, and 0.94, and the absolute errors of peak present time were 0.19 h, 0.40 h, 0.91 h, and 0.82 h, respectively. Moreover, the utilization of the general unit hydrograph method was found to significantly reduce the peak-to-current time difference, thereby enhancing simulation accuracy. Parameter variations have a substantial influence on peak flow characteristics. The SCE-GUH model, which incorporates the topographic and geomorphological features of the watershed along with thea* optimization algorithm, is capable of effectively characterizing the catchment properties of the watershed and offers valuable insights for enhancing the early warning and prediction of hydrological forecasting.

关键词： general unit hydrograph rainfall-runoff relationship optimization algorithm flash flood simulation application test surface confluence

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WiFi Intrusion Detection using Artificial Neurons with Bio-inspireda* optimization algorithm

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Procedia Computer Science 2023年 218卷 1238-1246页

作者： Lenin Narengbam Shouvik Dey

Wireless network growth has been remarkable in recent years. There is a significant increase in the manoeuvring of mobile and stand-alone appliances, with WiFi network connectivity as the main reason for this growth. In electronics products, these devices have become indispensable. Wireless networks have become increasingly vulnerable to attacks as their popularity has grown. Still, high accuracy and detection rate, and low false positive rate are needed for network intrusion detection systems (IDS). In this study, we propose artificial neuron training with a bio-inspireda* optimization algorithm (BOA) for WiFI networks to efficiently detect intrusion against them. The proposed WiFi intrusion detection framework has artificial neurons trained with Harris Hawks optimization (N-HHO) benchmarked on publicly available Aegean Wi-Fi intrusion Datstaset (AWID). We trained this dataset to propose N-HHO and validate performance evaluation with other BOAs and machine learning (ML) techniques. Our WiFi intrusion detection framework outperforms all other techniques and it could be an alternate option to be used for WiFi network security.

关键词： Artificial neuron network optimization algorithm intrusion detection system WiFi network

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esign and Application ofa* optimization algorithm for Vegetable Planting and Distribution Based on Adaptive Differential Evolution algorithm

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Procedia Computer Science 2022年 208卷 247-254页

作者： Yunjian Dai Xiaodong Li

Now, we have entered the "internet plus" era, and all industries are making use of information technology to seek a longer-term development. For vegetable distribution, we must rely on the power of information technology. Faced with complex global optimization problems, traditionala* optimization algorithms have many performance limitations, such as complex solution process and easy to fall into local optimization, which can't meet the needs of practical applications. This paper studies the design and application ofa* optimization algorithm for vegetable planting and distribution based on adaptive differential evolution algorithm. The method adopted in this paper is adaptive differential evolution algorithm. In the original differential evolution algorithm, there are three undetermined parameters: population size (NP), variation factor (F) and crossover probability (CR). In the process of evolution, NP takes a fixed value, while the key control parameters F and CR, which affect the performance of the algorithm, will adaptively take different values on [0,1], and the values of F and CR will be recalculated for each individual in each generation. Let F and CR automatically adjust and change in the process of evolution. The research in this paper shows that the algorithm in this paper is effective and suitable for application.

关键词： Adaptive differential evolution algorithm planting distribution of vegetables optimization algorithm internet plus

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AN OPTIMAL APPROACH FOR EEG/ERP NOISE CANCELLATION USING ADAPTIVE FILTER WITH OPPOSITIONAL WHALEa* optimization algorithm

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BIOMEDICAL ENGINEERING-APPLICATIONS BASIS COMMUNICATIONS 2019年第5期31卷

作者： Nagal, Rachana Kumar, Pradeep Bansal, Poonam AMITY Univ AMITY Sch Engn & Technol Noida 201303 Uttar Pradesh India Maharaja Surajmal Inst Technol Dept Comp Sci & Engn New Delhi 110086 India

In this paper, the Oppositional Whalea* optimization algorithm (OWOA) is applied to Adaptive Noise Canceller (ANC) for the filtering of Electroencephalography/Event-Related Potentials (EEG/ERP) signals. Performance of ANC will be improved by calculating the optimal weight value and proposed OWOA technique is used to update weight value. Adaptive filter's noise reduction capability has been tested through consideration of White Gaussian Noise (WGN) over contaminated EEG signals at various SNR levels (-10 dB, -15 dB and -20 dB). The performance of the proposed OWOA algorithm is assessed in terms of Signal to Noise Ratio (SNR) in dB, mean value, and the correlation between resultant and input ERP. In this work, ANCs are also implemented by utilizing conventional gradient-based techniques like Recursive Least Square (RLS), Least Mean Square (LMS) and othera* optimization algorithms such as Genetic algorithm (GA), Particle Swarm optimization (PSO) and WOA techniques. In average cases of noisy environment, comparative analysis shows that the proposed OWOA technique provides higher SNR value and significantly lower mean, and correlation as compared to gradient-based and swarm-based techniques. The comparative results show that extracting the desired EEG component is more effective in the proposed OWOA method. So, it has seen that OWOA-based noise reduction technique removing the artifacts and improving the quality of EEG signals significantly for biomedical analysis.

关键词： optimization algorithm Adaptive noise canceller White Gaussian noise Gradient based algorithm Nature inspired algorithm

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APPLICATION OF PARAMETERa* optimization algorithm IN DIGITAL ARCHITECTURE DESIGN

ITALIAN JOURNAL OF PURE AND APPLIED MATHEMATICS

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ITALIAN JOURNAL OF PURE AND APPLIED MATHEMATICS 2018年第39期 385-392页

作者： Zhu, Yunhua Neijiang Normal Coll Sch Civil Engn & Architecture Neijiang City 641100 Sichuan Peoples R China

In recent years, digital technology has been used in all aspects of peoples work and life more and more frequently and has exerted a certain impact on the design of buildings. More and more architectural design teams add mathematical logic and digital technology to the early stages of their design to better control the design work and implement the construction. Digital building design mainly includes parametric design and algorithm generation design, the former of which is mainly studied in this paper. As a modeling design algorithm which puts an emphasis on logic and reason, parametric design emphasizes the scientific nature of architectural design. In order to achieve the optimal design of the combination of building physics, institutional performance and parameterization, this paper applies the Dijkstra algorithm and the most energy efficient scheme generation (MEESG) energy consumption prediction method to the optimization design of wiring and performance and achieves good results, suggesting that the parametera* optimization algorithm has a good impetus to the design of digital building.

关键词： Digitization parameterization optimization algorithm architectural design

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Using proposeda* optimization algorithm for solving inverse kinematics of human upper limb applying in rehabilitation robotic

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ARTIFICIAL INTELLIGENCE REVIEW 2022年第1期55卷 679-705页

作者： Nguyen, Trung Bui, Tam Pham, Ha Hanoi Univ Sci & Technol HUST Sch Mech Engn Hanoi Vietnam Shibaura Inst Technol Dept Machinery & Control Syst Tokyo Japan Hanoi Univ Ind Fac Informat Technol Hanoi Vietnam

The requirement to solve the problem of Inverse Kinetics (IK) plays a very important role in the robotics field in general, and especially in the field of rehabilitation robots, in particular. If the solutions of this problem are not suitable, it can cause undesirable damage to the patient when exercising. Normally, the problem of Inverse Kinematics in the robotics field, as well as the natural field, especially for redundant driven systems, often requires the application of a lot of techniques. The redundancy in Degree of Freedom (DoF), the nonlinearity of the system leads to solve inverse kinematics problem more challenge. In this study, we proposed to apply the self-adaptive control parameters in Differential Evolution with search space improvement (Pro-ISADE) to solve the problem for the human upper limb, which is a very typical redundancy model in nature. First of all, the angles of the joints were measured by a proposed Exoskeleton type Human Motion Capture System (E-HMCS) when the wearer performs some Activities of Daily Living (ADL) and athletic activities. The values of these measured angles joints then were put into the forward kinematics model to find the end effector trajectories. After having these orbits, they were re-fed into the proposed Pro-ISADE algorithm mentioned above to process the IK problem and obtain the predicted joints angular values. The experimental results showed that the predicted joints' values closely follow the measured joints' values. That demonstrates the ability to apply the Pro-ISADE algorithm to solve the problem of Inverse Kinetics of the human upper limb as well as the upper limb rehabilitation robot arm.

关键词： optimization algorithm Inverse kinematics (IK) Exoskeleton type motion capture system (E-HMCS) Activities of daily living (ADL)

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Aerodynamics Design for Automotive Shape based on Approximatea* optimization algorithm

Aerodynamics Design for Automotive Shape based on Approximat...

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作者： Lin-lin WU Yu FU Xiao-bing BU Xiang-rong LI Feng-ling GAO CAE Performance Division Vehicle Crash Testing and Research Department CATARC Automotive Test Center (Tianjin) Co. Ltd

Since it is difficult to find the optimal solution directly by the traditional CFD optimization method due to its strong dependence on the designer's experience, an automatic aerodynamic optimization design platform for automotive shape was built based on mesh deformation technology, surrogate model anda* optimization algorithm in this paper. A parameterized model of an automotive was established. Latin hypercube method was adopted to select sample points. The drag coefficients corresponding to sample points were calculated by CFD simulation, whereby the influence of each parameter on drag coefficient was obtained. By comparing the calculation time, optimization effect and optimization accuracy of 9 combinations of surrogate models anda* optimization algorithms, the combination of RBF model and NLPQL algorithm was selected as the optimal one which is the most appropriate for the aerodynamic optimization design for automotive shape.

关键词： Automotive shape Parameterization Surrogate model optimization algorithm

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