Journal of Computer Science and Digital Technology
Journal of Computer Science and Digital Technology(JCSDT) is a peer-reviewed academic journal dedicated to advancing knowledge in the fields of computer science and engineering. The journal is interested in research on Software Engineering, Computer Hardware & Architecture, Cybernetics, Automation & Control Systems, Robotics, Information Science & Library Science, Artificial Intelligence, Computer Science Theory & Methods, Logic, Information Systems, and Interdisciplinary Applications of Computer Science.
All articles published in this journal are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author(s) and source are properly credited. Authors retain copyright of their work, and readers are free to copy, share, adapt, and build upon the material for any purpose, including commercial use, as long as appropriate attribution is given.
Design and Implementation of an Intelligent Motion Control and Automatic Detection System for Magnetic Grid Manufacturing
Magnetic grid sensors are widely used in industrial robots, CNC machine tools, and intelligent manufacturing systems due to their high precision and strong environmental adaptability. However, conventional magnetic grid production still suffers from low assembly consistency, inefficient manual inspection, weak process traceability, and insufficient integration between motion control and quality management systems. To address these challenges, this paper proposes an intelligent automatic detection and motion control system for magnetic grid manufacturing. The proposed system integrates high-precision motion control algorithms, multi-axis coordinated control, automatic signal acquisition, human–machine interaction (HMI), and software–hardware collaborative integration into a unified production platform. A modular system architecture consisting of equipment execution, control management, and data application layers is designed to realize automated assembly positioning, precision calibration, real-time signal analysis, and digital quality control. To improve positioning stability and trajectory tracking accuracy, feedforward compensation and friction compensation strategies are introduced into the motion control framework. In addition, a real-time data acquisition and traceability mechanism is established to support process monitoring, defect analysis, and production optimization. Experimental verification and mass-production deployment demonstrate that the proposed system significantly improves assembly consistency and inspection efficiency. The positioning repeatability reaches ±0.01 mm, the single-station inspection time is reduced by more than 50%, and the product defect rate decreases from 12% to below 3%. The developed system provides an effective technical solution for intelligent upgrading of precision sensor manufacturing and offers practical references for industrial automation, intelligent quality control, and digital manufacturing applications.
Copyright (c) 2025 Shishui Zhou, Chaohui Zhang, Lujie Ren, Jianming Mao, Haijun Lei, Dengcheng Lu (Author) https://creativecommons.org/licenses/by/4.0
Design and Implementation of a High-Precision Grating Measurement System Based on Optical Signal Processing and Absolute Coding Algorithms
Precision grating sensors are critical components in intelligent manufacturing systems, precision motion control, and high-end automated equipment, where measurement accuracy and signal stability directly affect system positioning performance and operational reliability. However, conventional grating measurement technologies still face challenges related to signal interference, environmental adaptability, coding accuracy, and long-term engineering stability. To address these issues, this paper presents the design and engineering implementation of a high-precision grating measurement system integrating optical sensing, coding signal processing, anti-interference optimization, and absolute coding algorithms. The proposed system establishes a complete technical framework covering grating scribing accuracy control, optical system optimization, coding signal processing, interpolation subdivision algorithms, and full-process accuracy calibration. To improve measurement resolution and environmental robustness, multi-stage digital filtering, automatic gain calibration, electromagnetic compatibility optimization, and temperature compensation strategies are introduced. In addition, a high-resolution absolute coding algorithm based on Gray-code and segmented coding mechanisms is developed to achieve stable absolute position output and power-off position retention. The proposed algorithms and signal-processing methods can be deployed on embedded MCUs and dedicated ASIC platforms for industrial applications. Experimental verification and industrial deployment demonstrate that the developed system achieves high measurement stability, strong anti-interference capability, and reliable long-term operation under harsh industrial conditions. The proposed technology has been successfully applied in CNC machine tools, servo systems, and automated equipment, providing an effective technical solution for domestic substitution and intelligent upgrading of precision displacement measurement systems.
Copyright (c) 2025 Jianming Mao, Haijun Lei, Dengcheng Lu, Shishui Zhou, Chaohui Zhang, Lujie Ren (Author) https://creativecommons.org/licenses/by/4.0
Intelligent Absolute Magnetic Grating and Rotary Encoder System Based on TMR Sensing and Embedded Signal Processing for Industrial Automation Applications
With the rapid advancement of intelligent manufacturing, industrial robotics, digital motion-control systems, and high-end automated equipment, position sensing technologies have become fundamental components for precision motion control and intelligent industrial feedback systems. Absolute magnetic gratings and rotary encoders are increasingly employed in industrial robots, servo drives, CNC machine tools, logistics systems, and automated production equipment due to their advantages in non-contact measurement, anti-contamination capability, wide-temperature adaptability, and power-off position retention. However, conventional encoder systems still face several engineering challenges, including insufficient resistance to electromagnetic interference, unstable operation under vibration and oil contamination, limited multi-turn memory capability, and reduced measurement consistency in harsh industrial environments. To address these challenges, this paper presents a systematic study on intelligent absolute magnetic grating and rotary encoder technologies based on tunnel magnetoresistance (TMR) sensing, adaptive signal processing, magnetic circuit optimization, embedded decoding algorithms, and industrial reliability verification. A complete engineering framework integrating magnetic measurement principles, magnetic circuit structure optimization, absolute coding algorithm development, interpolation subdivision methods, wide-temperature compensation, anti-vibration optimization, multi-turn power-off memory, and automated calibration technologies is proposed. The system employs high-sensitivity TMR sensing bridges, low-noise signal-conditioning circuits, adaptive digital filtering algorithms, and embedded decoding architectures to improve position resolution, environmental robustness, and operational reliability. A high-precision multi-turn absolute encoder system is developed as an engineering validation platform. Experimental results demonstrate that the optimized encoder achieves stable high-resolution position output under industrial environments involving vibration, electromagnetic interference, oil contamination, and thermal fluctuation. The proposed system supports multiple industrial communication interfaces including RS422, SSI, CANopen, and incremental pulse output, enabling compatibility with industrial servo systems and intelligent motion-control platforms. Reliability tests including vibration testing, thermal cycling, electromagnetic compatibility evaluation, waterproof verification, and long-term aging experiments further confirm the engineering stability of the developed system. Compared with conventional encoder products, the proposed optimization framework significantly improves measurement consistency, anti-interference capability, environmental adaptability, and industrial deployment efficiency. The research provides a practical technical solution for intelligent industrial sensing systems and contributes to the development of high-precision motion-control technologies in intelligent manufacturing environments.
Copyright (c) 2025 Lujie Ren, Jianming Mao, Haijun Lei, Dengcheng Lu, Shishui Zhou, Chaohui Zhang (Author) https://creativecommons.org/licenses/by/4.0
Intelligent Industrial Instrument Accuracy Enhancement and Process Optimization System Based on Embedded Signal Processing and Reliability Verification
Industrial instruments are critical sensing and control components in intelligent manufacturing systems, industrial automation platforms, precision inspection environments, and digital production lines. The measurement accuracy, operational stability, and environmental adaptability of industrial instruments directly affect production quality, process controllability, and equipment reliability. However, conventional industrial instruments still face several practical engineering challenges, including signal distortion caused by electromagnetic interference, insufficient sensing adaptability in complex industrial environments, unstable mass-production consistency, and limited reliability verification capability. To address these issues, this paper proposes an intelligent industrial instrument optimization framework integrating embedded signal processing, adaptive sensing technology, production process optimization, and reliability verification methods. The proposed system combines modular circuit architecture, adaptive sensor interface optimization, Kalman-filter-based signal processing, temperature compensation strategies, automated calibration mechanisms, and industrial reliability evaluation technologies into a unified engineering implementation platform. High-performance embedded processing units and industrial communication architectures are integrated to improve data acquisition stability, anti-interference capability, and dynamic response performance. A high-precision industrial pressure measurement instrument is selected as the engineering validation platform. Through process parameter optimization, dynamic calibration, automated production management, and environmental adaptability verification, the optimized instrument achieves measurement accuracy within ±0.1%FS and stable operation under vibration, electromagnetic interference, and wide-temperature industrial environments. Experimental results demonstrate that the proposed framework significantly improves signal stability, manufacturing consistency, production efficiency, and long-term operational reliability compared with conventional industrial instrument systems. The proposed research establishes a practical technical framework for intelligent industrial sensing systems and provides engineering support for digital manufacturing, industrial automation, embedded measurement systems, and intelligent quality-control applications.
Copyright (c) 2025 Haijun Lei, Dengcheng Lu, Shishui Zhou, Chaohui Zhang, Lujie Ren, Jianming Mao (Author) https://creativecommons.org/licenses/by/4.0
Design and Implementation of a Non-Contact Magnetic Sensing and Signal Processing System for High-Precision Robot Joint Encoders
Robot joint encoders are critical sensing components in industrial robots, where positioning accuracy, dynamic response, and environmental adaptability directly affect motion-control performance and operational reliability. However, conventional encoder technologies still face challenges related to signal instability under harsh industrial conditions, insufficient high-speed response capability, and limited long-term reliability. To address these issues, this paper presents the design and implementation of a non-contact magnetic sensing and signal-processing system for high-precision robot joint encoders. The proposed system integrates incremental magnetic grid design, tunnel magnetoresistance (TMR) sensing technology, adaptive signal conditioning, multi-dimensional error compensation, and high-speed signal-processing algorithms into a unified encoder framework. A dual-reading-head magnetic grid structure and dynamic gain compensation mechanism are introduced to improve signal stability under vibration, temperature variation, oil contamination, and electromagnetic interference conditions. In addition, adaptive filtering and real-time interpolation algorithms are employed to enhance dynamic response capability and suppress nonlinear measurement errors during high-speed robot motion. The developed encoder system supports multiple industrial communication interfaces, including RS422, SSI, and CANopen, enabling flexible integration with industrial robot control systems. Experimental results demonstrate that the proposed encoder achieves repeatability accuracy within ±1 μm and a frequency response of 8000 kHz while maintaining stable operation under harsh industrial environments. Engineering deployment in industrial robots and precision manufacturing equipment further verifies the robustness, reliability, and practical applicability of the proposed system. The research provides an effective technical solution for high-precision robotic motion sensing and intelligent industrial measurement systems.
Copyright (c) 2026 Chaohui Zhang, Lujie Ren, Jianming Mao, Haijun Lei, Dengcheng Lu, Shishui Zhou (Author) https://creativecommons.org/licenses/by/4.0
Design Optimization and Intelligent Process Iteration for Industrial Instrument Systems in Intelligent Manufacturing Environments
With the rapid development of intelligent manufacturing, industrial automation, edge computing, and digital production systems, industrial instruments have become critical components for industrial sensing, data acquisition, process monitoring, and intelligent control. The accuracy, reliability, and environmental adaptability of industrial instruments directly affect production efficiency, product consistency, and the stability of automated manufacturing systems. However, conventional industrial instruments still face multiple engineering challenges, including measurement drift under complex industrial conditions, insufficient adaptability to variable process environments, limited anti-interference capability, and low consistency during large-scale production. To address these challenges, this paper presents a systematic study on industrial instrument design optimization and intelligent process iteration technologies. The proposed framework integrates modular circuit architecture, sensor adaptation technology, process parameter optimization, reliability verification, automated manufacturing, and full-process quality control into a unified engineering implementation system. A modular low-noise circuit structure combined with isolated power design, adaptive filtering, and electromagnetic compatibility optimization is introduced to improve signal stability and measurement accuracy. In addition, sensor selection and interface adaptation strategies are optimized according to industrial operating conditions such as temperature variation, vibration, pressure fluctuation, and electromagnetic interference. Furthermore, multi-level calibration methods, automated production processes, and reliability verification mechanisms are established to improve product consistency and long-term operational stability. A high-precision industrial pressure instrument is used as an engineering validation case to verify the effectiveness of the proposed optimization framework. Experimental and industrial deployment results demonstrate that the optimized system achieves measurement accuracy within ±0.1%, response time below 20 ms, and stable operation under harsh industrial environments ranging from −20°C to 85°C. Compared with conventional industrial instruments, the proposed approach significantly improves measurement consistency, environmental adaptability, manufacturing efficiency, and product reliability. The research provides a practical technical framework for intelligent industrial instrument development and contributes to the integration of digital manufacturing, industrial sensing, and intelligent control technologies.
Copyright (c) 2025 Dengcheng Lu, Shishui Zhou, Chaohui Zhang, Lujie Ren, Jianming Mao, Haijun Lei (Author) https://creativecommons.org/licenses/by/4.0
Editorial: Research Topic on Intelligent Industrial Sensing, Embedded Measurement Systems, and Precision Motion-Control Technologies in Digital Manufacturing Environments
The rapid evolution of intelligent manufacturing, industrial automation, digital production systems, and embedded sensing technologies is reshaping the global industrial landscape. As modern manufacturing systems continue to advance toward higher precision, stronger intelligence, and deeper digital integration, industrial sensing systems, motion-control platforms, intelligent measurement technologies, and automated quality-control architectures have become fundamental technological pillars supporting the next generation of industrial transformation. In this issue of the Journal of Computer Science and Digital Technology, we are honored to invite the research and engineering team from Hopo Technology (Ningbo) Co., Ltd. to present a series of specialized studies focusing on intelligent sensing systems, industrial automation technologies, embedded signal processing, precision measurement platforms, and industrial reliability optimization. The five research articles included in this research topic systematically investigate key technical challenges and engineering implementation methods related to magnetic grid manufacturing, precision grating measurement systems, industrial instrument optimization, intelligent encoder systems, and embedded industrial sensing architectures. These studies not only reflect practical industrial engineering experience, but also demonstrate the interdisciplinary integration of computer science, embedded systems, intelligent control, industrial communication, automation engineering, and digital manufacturing technologies. Through a combination of theoretical analysis, embedded algorithm development, system architecture optimization, industrial deployment validation, and mass-production engineering verification, the published works provide valuable technical references for intelligent manufacturing applications, industrial digitalization, and high-precision motion-control systems. This research topic highlights the increasing importance of software–hardware collaborative systems, intelligent sensing technologies, embedded signal-processing algorithms, and industrial reliability verification within the broader context of digital industrial transformation.
Copyright (c) 2025 Guoqing Sun (Author) https://creativecommons.org/licenses/by/4.0
A Discussion on Ethics, Trust and Security Governance in the Evolution of Artificial Intelligence Technology
With the rapid advancement and widespread deployment of artificial intelligence , issues related to ethics, trust, and security have become increasingly prominent, posing significant challenges to its sustainable development. This paper aims to systematically investigate these intertwined challenges by constructing a structured analytical framework encompassing three core dimensions: ethics, trust, and security.Methodologically, the study analyzes the evolutionary trajectory and application contexts of AI, and synthesizes key risk patterns and governance concerns. It examines major ethical issues, including algorithmic bias, data privacy, responsibility attribution, and value alignment, and further explores the underlying tension between technological rationality and social values.In addition, the paper proposes a multi-level trust formation mechanism based on technological reliability, institutional assurance, and public cognition, emphasizing that trust emerges from the joint interaction of technical systems and governance structures. At the security level, it highlights the complexity and cross-domain nature of AI risks, and advocates for a full life-cycle governance framework integrating risk assessment, technical auditing, and continuous supervision.The study’s key contribution lies in integrating ethical, trust, and security perspectives into a unified analytical framework, and in proposing a collaborative governance approach involving governments, enterprises, and international stakeholders.
Copyright (c) 2026 Shufeng Wang (Author) https://creativecommons.org/licenses/by/4.0
Night UAV Vehicle Detection Based on Infrared-Visible Fusion and Improved YOLO11
To address the challenges of vehicle detection accuracy in nighttime UAV aerial photography scenarios caused by low light conditions, strong noise, and dense small object distribution, this paper proposes an improved YOLO11 vehicle detection method based on multi-modal fusion. First, a collaborative enhancement strategy combining CLAHE and Gamma correction is applied to preprocessing nighttime visible light images, effectively restoring vehicle texture details in dark areas. Second, the enhanced visible light images are fused with infrared images through fixed-weight weighted fusion (infrared weight α=0.7), fully leveraging the complementary advantages of both modalities. Finally, the MANet module is introduced on top of YOLO11n to enhance backbone multi-scale feature extraction capabilities, while the ADFPN-DASI module is designed to collaboratively optimize neck multi-scale feature representation. Experiments conducted on the DroneVehicle dataset demonstrate that the proposed method achieves an mAP50 of 80.53%, representing an improvement of 11.45 percentage points over the YOLO11n baseline. The method maintains lightweight advantages in parameter and computational resources, outperforming mainstream comparison methods such as YOLOv5n.
Copyright (c) 2026 Hongxia Su (Author) https://creativecommons.org/licenses/by/4.0
Precise Identification of Multi-Regional Relative Poverty: A Two-Stage Knowledge-Distilled Adaptive Framework
As poverty reduction strategies shift comprehensively toward alleviating relative poverty, precisely identifying multidimensional relative poverty populations has become critical for social governance. However, existing data-driven models often face algorithmic bottlenecks—such as spatial heterogeneity, regional sample sparsity, and extreme category imbalance—when applied to complex scenarios characterized by vast territories and significant regional disparities. To address this, this study proposes a two-stage knowledge-distilled adaptive gradient boosting framework (TKDAF). First, in the prior knowledge extraction stage (Stage I), a base structure-regularized gradient boosting tree model is constructed. Combined with SHAP game-theoretic attribution, this stage quantifies and extracts the global objective weights of poverty-inducing features across regions. Second, in the spatial adaptive enhancement stage (Stage II), the S-DAGB (Spatial-Adaptive Distilled Gradient Boosting) core prediction model is introduced. It achieves deep integration of multiple regularization and feature enhancement mechanisms by incorporating: feature space nonlinear reconstruction based on prior knowledge, a dynamic category weighting mechanism based on effective sample size (ENS), and spatial adaptive optimization using the TPE Bayesian algorithm.Empirical results based on the 2020 China Family Panel Studies (CFPS) multidimensional dataset demonstrate that the S-DAGB model not only effectively overcomes the generalization bottleneck of deep tree models in the sample-constrained Northeast region (achieving 93.52% accuracy),but also significantly improves precision in regions with highly heterogeneous features and extreme class imbalance, such as central and western China. This effectively reduces wasteful allocation of poverty alleviation resources caused by false positive errors. This study provides an algorithmic solution that combines high accuracy with interpretability for precise identification of relative poverty in complex data distribution scenarios.
Copyright (c) 2026 Guanghuang Liu (Author) https://creativecommons.org/licenses/by/4.0
RF-PBFT is an Improved PBFT Consensus Algorithm Based on the Random Forest Algorithm
The Practical Byzantine Fault-Tolerant (PBFT) consensus algorithm faces challenges such as random master node selection, high communication overhead, and a lack of incentive and penalty mechanisms, which undermine its efficiency and security in large-scale network environments. To address these issues, this paper proposes an improved PBFT consensus algorithm, RF-PBFT, which integrates a random forest-based node grouping mechanism with a dynamic reputation system. The algorithm leverages key behavioral data of nodes during the consensus process to train a random forest model, partitioning nodes into a consensus set and a candidate set. Nodes with superior predictive performance are selected to participate in the consensus process, thereby reducing redundant communication overhead. Furthermore, a differentiated reputation mechanism is established for the consensus group and the candidate group to encourage positive behavior and deter malicious actions. The master node is elected through a combination of prediction results and voting, enhancing system reliability and security. Simulation results demonstrate that, compared with traditional PBFT and other state-of-the-art variants, RF-PBFT significantly reduces communication overhead, decreases consensus latency, and improves throughput, validating its effectiveness in multi-node blockchain systems.
Copyright (c) 2026 Yingchen Xu, Ye Tian, Ying Jing, Fujiang Yuan (Author) https://creativecommons.org/licenses/by/4.0
Theoretical Analysis of the Impact of Generative Artificial Intelligence on Computer Science Education
Generative artificial intelligence (generative AI) has emerged as one of the most transformative technological advancements of the 21st century. In the realm of computer science education, its potential to revolutionize curriculum design, pedagogy, and the overall learning experience has generated considerable interest. This paper offers a comprehensive theoretical analysis of the multifaceted impact of generative AI on computer science education. Distinct from empirical studies, this research exclusively engages in a rigorous discussion anchored in existing theoretical frameworks and scholarly insights. Drawing from constructivist learning theory, technology acceptance models, and ethical considerations, the paper explores how generative AI tools might reshape the roles of educators and learners, transform the delivery of educational content, and stimulate innovation in computer science curricula. Furthermore, the analysis interrogates the potential challenges and risks associated with these technologies, including the dilemmas of academic integrity, algorithmic bias, and a possible overreliance on automation. The discussion concludes with reflections on the future trajectory of AI-enhanced learning environments and recommendations for theoretical development that may guide future empirical inquiries.
Copyright (c) 2025 Munger Sun (Author) https://creativecommons.org/licenses/by/4.0
Current Application Status, Challenges, and Future Prospects of Artificial Intelligence in the Accounting Field
This paper examines the current application status, challenges, and future prospects of artificial intelligence (AI) in the accounting field. It highlights how AI technologies, such as robotic process automation (RPA), machine learning, natural language processing (NLP), and computer vision, are transforming traditional accounting practices. By automating routine tasks like data entry, invoice processing, and audit analysis, AI enhances efficiency, accuracy, and strategic decision-making capabilities. However, the adoption of AI in accounting also poses challenges related to data quality and security, model explainability, ethical and legal risks, and talent shortages. The paper further discusses the potential displacement of certain accounting roles due to automation and the need for a nuanced understanding of AI's role in shaping the future of accounting. By providing insights into these dynamics, the study aims to inform stakeholders across the accounting ecosystem and foster a collaborative approach to navigating the digital frontier.
Copyright (c) 2025 Junli Wang (Author) https://creativecommons.org/licenses/by/4.0
The Effects of Mobile and Server-Side Automated Testing on DevOps Performance Efficiency
This study examines the comparative influence of mobile software automated testing and server-side automated testing on enterprise DevOps performance efficiency. Through qualitative analysis of multiple case studies across technology organizations, we identify key performance indicators affected by both testing approaches. Moreover, Findings suggest that while server-side automated testing provides more immediate efficiency gains through standardized environments, mobile automated testing delivers greater long-term value through reduced regression cycles and improved platform compatibility detection. The research highlights the need for balanced investment across both testing domains to maximize overall development efficiency and product quality. We propose a theoretical framework for organizations to assess their testing maturity and optimize resource allocation based on project characteristics and organizational goals.
Copyright (c) 2025 Jun Cui (Author) https://creativecommons.org/licenses/by/4.0
With the increasing importance of wind energy in the global energy structure, wind power forecasting has become one of the key technologies to ensure grid stability and improve energy dispatch efficiency. This paper uses the wind speed and wind power data of US wind farms in 2012 to predict and compare wind speed and wind power of different LSTM variant models (including traditional LSTM, xLSTM, sLSTM and mLSTM) by sampling every 5 minutes. The research focuses on comparing the performance of each model in predicting wind speed under the same power output conditions. The experimental results are evaluated by three common evaluation indicators: MAE , MSE and R². The results show that the mLSTM model performs best in wind speed forecasting, with better accuracy and stability than other LSTM variants. The research in this paper provides a new method to improve the accuracy of wind power forecasting and provides effective decision support for the operation and management of wind farms.
Copyright (c) 2025 Peng Zhao, Junye Yang, Senhao Zhang (Author) https://creativecommons.org/licenses/by/4.0
Dairy Product Production Prediction Based on BiLSTM-Attention model
With the rapid development of the dairy industry, accurate prediction of production is crucial to optimizing production plans. To this end, this paper proposes a prediction model that combines a bidirectional long short-term memory network (BiLSTM) with an attention mechanism (Attention). BiLSTM can effectively capture long-term and short-term dependencies in time series, while the attention mechanism can dynamically focus on the features of key time points, thereby improving prediction accuracy. Experiments show that the BiLSTM-Attention model improves the accuracy of dairy production prediction compared to traditional regression analysis and a single LSTM model, especially when processing long time series data. This study provides an effective solution for the accurate prediction of dairy production.
Copyright (c) 2025 Xiangyuan Qi, Guanglei Qiang, Fujiang Yuan (Author) https://creativecommons.org/licenses/by/4.0
Flood Probability Prediction Based on xLSTM
Flood disasters occur frequently and pose significant threats to human life and property, making flood forecasting essential for disaster prevention and mitigation. This paper proposes a probabilistic flood occurrence prediction model based on the extended Long Short-Term Memory (xLSTM) network. Leveraging multi-source time-series data such as historical rainfall, flow rate, and water level, the model captures temporal features associated with flood events and predicts future flood risks in probabilistic terms. Empirical analysis on multiple benchmark flood datasets demonstrates that the xLSTM model outperforms traditional models such as LSTM and GRU in terms of prediction accuracy and generalization capability. The proposed approach shows strong application potential and practical value, providing timely and accurate flood forecasts to support effective flood risk management.
Copyright (c) 2025 Fujiang Yuan, Rongmu Chai, Guanglei Qiang (Author) https://creativecommons.org/licenses/by/4.0