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A comprehensive review of flood-prone area zonation using ensemble and hybrid machine learning models with a framework proposal for modelling
(2025-01-01)
Long, Gen
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Tantanee, Sarintip
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Nusit, Korakod
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Sooraksa, Pitikhate
This research offers a systematic review encompassing 63 relevant peer-reviewed papers concerning flood susceptibility, hazard and risk assessment using various ensemble machine learning and hybrid approaches. It examines publication details, study characteristics, terminology, flood inventories, conditioning factors, data resolution, and modeling approaches. A key contribution is a proposed framework for practising ensemble or hybrid modeling. The framework comprises data preparation, checking for multicollinearity, factor selection and weighting, optional factor optimization, k-fold cross validation where appropriate, ensemble or hybrid modeling, and model evaluation. This framework aims to facilitate research activities and enhance model quality. Furthermore, the statistical outcomes can benefit researchers by guiding their further research. The knowledge produced by this study will thus help advance understanding of the application of ensemble machine learning and hybrid methods for the zonation of flood-prone areas and guide the direction of further research on enhancing the effectiveness of flood risk management strategies.
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Flood Susceptibility Mapping Using Machine Learning Models with Novel Flood Inventory Sampling Strategies
(2025-01-01)
Nusit, Korakod
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Tantanee, Sarintip
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Sooraksa, Pitikhate
In this study, we introduce an innovative frequency-area-weighted sampling method to address spatial and temporal biases in flood inventory creation. Focusing on Thailand’s Nan River Basin, we integrated 13 flood conditioning factors and developed a point-based inventory that includes 3000 flood and 3000 non-flood samples, proportionally allocated on the basis of flood recurrence intervals and spatial distribution. We evaluated four machine learning models—artificial neural network, support vector machine, K-nearest neighbors, and random forest (RF) models—to assess their performance in flood susceptibility mapping (FSM). Among these, the RF model demonstrated the highest predictive capability, achieving an area under the curve (AUC) of 0.979 for the test set and an AUC of 0.984 for the verification set. The resulting susceptibility map identified 10.64% of the study area as “very high” risk, providing critical insights for prioritizing flood mitigation efforts. This work advances FSM methodology by effectively bridging the temporal flood frequency and spatial heterogeneity in inventory design, offering a robust framework for data-driven flood risk management in vulnerable regions.
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BioSim Incubator: A Non-Biological Egg Incubation Simulation Platform
(2025-01-01)
Rasmidatta, Rapinpa
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Sooraksa, Pitikhate
This study presents the design, development, and evaluation of a BioSim incubator that integrates IoT-based environmental control with a biosimulation framework for predicting embryo development. The system employed an ESP8266 microcontroller, an SHT30 sensor, and a Node-RED/MQTT architecture to enable the real-time monitoring, automated actuation, and feedback control of temperature, humidity, and airflow. Experimental trials replicated the two key incubation stages: the development stage (Days 1-18, 37.5 ± 0.2 °C, 50-55% RH) and the lockdown stage (Days 19-21, 37.5 ± 0.2 °C, 65-70% RH). Results showed that the incubator maintained stable conditions within biologically favorable ranges, with only minor fluctuations that were rapidly corrected by the control logic. Comparison with practical risk guidelines indicated that the incubator’s performance corresponded predominantly to the “guaranteed survival” zone. To extend evaluation beyond environmental parameters, a virtual embryo feedback system was implemented as part of the BioSim framework, mapping deviations in temperature and humidity to predicted biological outcomes, which consistently indicated healthy chick development. In addition to demonstrating technical feasibility, the BioSim incubator provides a safe and accessible tool for STEM education and training, allowing students to study the relationship between environmental control and biological development without the use of live eggs.
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Physics-informed Platform for Flight Dynamics Simulation
(2025-01-01)
Atayagul, Pattiwat
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Sooraksa, Pitikhate
In this study, we present the state-of-the-art development of the flight dynamics model of a rigid body from the theoretical aspect of flight dynamics, design the software architecture, and validate the proposed architecture by comparing the simulation results with the check-cases for the verification of six-degree-of-freedom flight vehicle simulations document issued by the National Aeronautics and Space Administration (NASA). We also design the computational workflow between the ordinary differential equations of the system and other axillary components by weaving those relations in the object-oriented programing style and powered with Python scientific libraries. The simulation outcomes in all cases are well matched with the majority of NASA baseline datasets under the same flight simulation conditions, which reflect the accuracy of the model with considerable confidence.
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Eye-hand Coordination Simulator of Robot Arms for Science, Technology, Engineering, and Mathematics Education
(2025-01-01)
Preedanont, Pimpran
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Sooraksa, Pitikhate
In this paper, we present an eye-hand coordination simulator for robot arms as a compact cyber-physical science, technology, engineering, and mathematics (STEM) learning unit that links visual perception to robot motion in pick/place interactions with a mobile robot as an automatic guided vehicle (AGV). The unit integrates four domains into one workflow: science (kinematics and motion), technology (sensors, motor controllers, vision), engineering (mechanisms and control states), and mathematics (geometric computation and frame transforms). The pick/place machine prototype includes linear X-Y-Z-axes with rotary and flip joints to realign an item box between a shelf and an AGV. A vision system detects a pair of fiducial circles to estimate the AGV centerline, yaw, and slot positions, while displacement sensors measure stand-off and assist parallel alignment. Performance was evaluated using a mock-up AGV positioned with varied offsets and yaw within a ±10 mm parking tolerance. Across 10 trials, the vision-based estimates of middle-slot X and stand-off Sx closely matched tape measurements, achieving 98-100% accuracy. The results show that the simulator is dependable in vision-guided coordination and usable as a simple, accessible platform for STEM education.
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Improved Active Noise Cancellation Using Variable Step-Size Combined Fx-LMS Algorithm
(2025-01-01)
Kar, Asutosh
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Burra, Srikanth
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Shoba, S.
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Vasundhara
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Mladenovic, Vladimir
Active noise cancellation of audio signals, which has been the subject of a substantial quantity of research in the past, remains a significant obstacle in many aspects of acoustic signal processing. In a natural environment, the system’s parameters are in constant flux. Consequently, we shall investigate the filtered-x least mean square (FxLMS) algorithm. The adaptive filter is used as a controller to generate an anti-noise signal in the feedforward FxLMS algorithm, which only addressed narrowband and broadband noise. The feedback FxLMS algorithm, on the other hand, enhances the algorithm’s convergence, but cannot eliminate the effect of residual noise’s uncorrelated disturbance. However, both the correlated and uncorrelated disturbances must be under user control. Therefore, a combination of the feedback and feed-forward FxLMS algorithms was employed, which consists of two portions for estimating the primary disturbances and the uncorrelated disturbances. A variable step size algorithm has been employed to attain a balance between system stability and control efficiency. In Akhtar’s method, an external adaptive filter was applied to the feed-forward system in order to estimate a more precise error signal and enhance the performance of the systems. These improvements will improve the algorithm’s ability to control both primary and uncorrelated noise.
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Flood susceptibility mapping in the Yom River Basin, Thailand: stacking ensemble learning using multi-year flood inventory data
(2025-01-01)
Long, Gen
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Tantanee, Sarintip
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Nusit, Korakod
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Sooraksa, Pitikhate
Accurate assessment models of flood susceptibility are crucial for informing risk management strategies related to severe threats posed by floods. This study assessed flood susceptibility in the Yom River Basin, Thailand, using conventional and ML methods. SE was compared with KNN, SVM, DT, RF, and a Stacking ensemble model (SVM, DT, RF). A point-based flood inventory was sampled from multi-year flood polygons using a method considering flood frequency and inundation size. Results showed all ML models, except KNN, outperformed SE. RF achieved AUCs of 96.0% (test) and 96.1% (verification), while Stacking achieved 99.9% (test) and 96.1% (verification). Stacking also outperformed in accuracy (0.982, 0.893), precision (0.974, 0.915), F1 (0.990, 0.866), sensitivity (0.982, 0.890), specificity (0.974, 0.920), and kappa (0.964, 0.786). These findings highlight the potential of using ensemble ML techniques to significantly improve flood susceptibility mapping and risk management in data-limited regions such as the Yom River Basin.
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Adaptive Tap-Length Based Sub-band Mean M-Estimate Filtering for Active Noise Cancellation
(2024-09-01)
Kar, Asutosh
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Shoba, S.
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Burra, Srikanth
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Goel, Pankaj
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Kumar, Sanjeev
Electronic equipment used on a daily basis now frequently includes active noise cancellation. The adaptive filters, which are positioned within, are essential for noise cancellation. An essential component to take into account for the overall performance is the structural and computational complexity of the filter. The filter’s structure has an impact on this. The amount of taps determines the structure. Active noise cancellation filters often have set tap lengths and are lengthy, which causes sluggish convergence and delay. As a result, a trade-off between the filter’s length and convergence is required. This is conceivable if there is a flexible filter with a tap length that adapts to the environment while still ensuring acceptable convergence. This study proposes a novel Minimum Mean M-estimate method with changeable tap length and uses a sub-band adaptive filtering technique to shorten the filter’s length. In order to maximize the filter’s efficiency, the advantages of three approaches are specifically merged in this work. They are the proposed algorithm, the proposed method’s variable tap length variant, and the sub-band adaptive filtering. The simulation’s findings and recommendations are supported.
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Calibration of Bi-prism Stereo Systems: A Model Free Approach
(2024-07-01)
Dissanayaka, Supun
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Sooraksa, Pitikhate
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Kaitwanidvilai, Somyot
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Morris, John
A simple stereo system can be constructed from a single camera using a prism in the optical path to provide the required two views of a system. The simplicity of these systems has several advantages, particularly if the target is an underwater robot, where compact size and ability to seal the optical components are key factors. However, dispersion by the prism, in addition to the lens distortion, makes calibration challenging. By using a model-free approach, we were able to calibrate a prism-based stereo system effectively. We also aimed to use readily available 45° prisms, which present significant dispersion in the system, but retain simplicity and reduce cost, compared to custom low angle prisms. Modern LEDs provide high intensity, low bandwidth light sources and we used a set of three sources, roughly centered on the RGB channels of a readily available commercial camera. Our system used a circular target pattern covering the binocularly visible region in the scene and collected sets of images at known distances, using three separate light sources. From these images, we generated two look-up tables, one for each pixel in the image and a disparity derived by matching corresponding points, Cp(u,v,du), which has three dimensions, and another look-up table, which has a single dimension, Cz(z), so are not quite large, and not beyond the memory capability of even small modern camera systems, but provide fast, O(1), lookup times, suitable for real-time systems. Our calibration strategy enables a simple stereo system built from a single camera to measure depths in a scene: the single camera requires no electronic synchronization and is built from a single, inexpensive, and readily available optical component – a right-angle prism.
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Building a Rule-Based Expert System to Enhance the Hard Disk Drive Manufacturing Processes
(2024-01-01)
Kirdponpattara, Suppakrit
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Sooraksa, Pitikhate
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Boonjing, Veera
The manufacturing of hard disk drives involves the intricate assembly of numerous components, making the testing process time-consuming and resource intensive. To optimize the manufacturing process and increase testing efficiency, the development of a rule-based expert system is proposed. This system leverages predictive models constructed from assembly process data to identify potentially defective hard drives before undergoing extensive testing. By preemptively identifying defects, this approach substantially reduces testing time and enhances tester capacity. Given the categorical and imbalanced nature of assembly data, Decision Trees are employed as the prediction model. Specifically, three Decision Tree algorithms are explored: ID3, C4.5, and CART. In addition, four feature selection techniques, namely Information Gain, Gain Ratio, Chi-Square, and Symmetrical Uncertainty, are utilized to identify high-impact features. Our experimental findings reveal that Information Gain coupled with the C4.5 algorithm yields the most favorable results in terms of prediction accuracy, modeling efficiency, and rule generation. Moreover, our study establishes that setting the failure probability threshold between 0.15 and 0.70 provides the shortest total test time for the proposed process, as supported by a 95% confidence level. This achievement represents a statistically significant enhancement compared with the existing manufacturing process.

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