Positioning System Research Articles

LB-LIOSAM: an improved mapping and localization method with loop detection

Purpose In large-scale environments, LIO-SAM (Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping) exhibits poor robustness due to the accumulation of errors caused by factors such as the prevalence of similar surroundings and the lack of features in certain open areas. Therefore, the purpose of this study is to optimize the loop detection module of LIO-SAM to reduce error accumulation and enhance mapping and localization performance. Design/methodology/approach Based on the LIO-SAM framework, the LinK3D (Linear Keypoints Representation for 3D LiDAR Point Cloud) feature extraction algorithm is integrated in the front end, while the BoW3D (Bag of Words for Real-Time Loop Closing in 3D LiDAR SLAM) loop detection algorithm is integrated in the back end. The features extracted by LinK3D serve as the range factors for the LiDAR, the BoW3D generates loop closure factors and these, along with inertial measurement unit (IMU) preintegration factors and global positioning system (GPS) factors, are added to the factor graph of LIO-SAM. This addition of constraints enhances the mapping and localization effects, optimizing the overall mapping and localization performance. Findings Based on the electrically controlled car, experiments were conducted in the experimental scenario proposed in this paper. Compared to LIO-SAM, the method presented in this paper significantly reduces cumulative errors. While ensuring real-time performance, it demonstrates superior mapping and localization effects. Originality/value This paper proposes and validates a method that integrates LinK3D, BoW3D and LIO-SAM, named LB-LIOSAM, which enhances the accuracy of feature extraction, optimizes the loop detection module of LIO-SAM and improves its mapping and localization performance in specific environmental scenarios.

The INS/GPS integrated navigation based on autonomous underwater vehicle using modified extended Kalman filter

Ensuring highhtab accuracy and robustness remains a paramount concern in the realm of underwater positioning and navigation research. This paper proposes a filtering algorithm grounded in Inertial Measurement Unit/Global Positioning System (INS/GPS) integrated navigation to effectively address the challenges posed by non-Gaussian noise, stemming from outliers and measurement inaccuracies. Currently, most filtering algorithms are developed based on the criteria of minimum mean square error (MMSE) or maximum entropy criteria (MCC). To address the challenge of handling complex non-Gaussian noises, this paper uses the Minimum Error Entropy Criterion (MEE) and Extended Kalman Filter (EKF) with Kernel Risk-Sensitive Loss (KRSL) instead of MMSE or MCC to develop an online filtering algorithm with a recursive process, and adjusts the kernel size of MEE within a reasonable range by constructing an adaptive factor to deal with outliers generated during measurement and excessive convergence caused by the inapplicability of the kernel size. Through extensive target tracking simulations and surface Autonomous Underwater Vehicle (AUV) localization experiments, results demonstrate the efficacy of the proposed algorithm. Notably, the algorithm optimizes kernel sizes for different types of noises, ensuring robust state estimation and rapid convergence without compromising filtering accuracy.

WSN Localization in Smart Cities Using Hybrid (TDOA & RSSI) Localization Techniques with Extended Kalman Filter

In the context of smart cities, localization technologies are essential for tracking objects in both indoor and outdoor environments. The characteristics of urban settings such as building density, signal interference, and multipath propagation significantly impact the accuracy of localization algorithms. This paper reviews existing localization techniques, categorizing them into range-based and range-free methods, and discusses key algorithms including trilateration, multilateration, and triangulation. We propose a hybrid localization framework that combines Time Difference of Arrival (TDOA) and Received Signal Strength Indicator (RSSI) techniques, enhanced by an Extended Kalman Filter (EKF) for improved accuracy and robustness. Additionally, we present a design architecture for a transmitter and receiver system utilizing Long Range (LoRa) technology, facilitating the development of low-cost, low-power tracking devices that operate independently of Global Positioning System (GPS). Our approach aims to enhance the efficacy of localization in smart city applications, ultimately contributing to improved urban management and safety.

Energetic and Neuromuscular Demands of Unresisted, Parachute- and Sled-Resisted Sprints in Youth Soccer Players: Differences Between Two Novel Determination Methods

The aim of this study was to analyze the differences in terms of (1) muscle activation patterns; (2) metabolic power (MP) and energy cost (EC) estimated via two determination methods (i.e., the Global Positioning System [GPS] and electromyography-based [EMG]); and (3) the apparent efficiency (AE) of 30-m linear sprints in seventeen elite U17 male soccer players performed under different conditions (i.e., unloaded sprint [US], parachute sprint [PS], and four incremental sled loads [SS15, SS30, SS45, SS60, corresponding to 15, 30, 45 and 60 kg of additional mass]). In a single testing session, each participant executed six trials (one attempt per sprint type). The results indicated that increasing the sled loads led to a linear increase in the relative contribution of the quadriceps (R2 = 0.98) and gluteus (R2 = 0.94) and a linear decrease in hamstring recruitment (R2 = 0.99). The MP during the US was significantly different from SS15, SS30, SS45, and SS60, as determined by the GPS and EMG approaches (p-values ranging from 0.01 to 0.001). Regarding EC, significant differences were found among the US and all sled conditions (i.e., SS15, SS30, SS45, and SS60) using the GPS and EMG methods (all p ≤ 0.001). Moreover, MP and EC determined via GPS were significantly lower in all sled conditions when compared to EMG (all p ≤ 0.001). The AE was significantly higher for the US when compared to the loaded sprinting conditions (all p ≤ 0.001). In conclusion, muscle activation patterns, MP and EC, and AE changed as a function of load in sled-resisted sprinting. Furthermore, GPS-derived MP and EC seemed to underestimate the actual neuromuscular and metabolic demands imposed on youth soccer players compared to EMG.

Mountain Landslide Monitoring Using a DS-InSAR Method Incorporating a Spatio-Temporal Atmospheric Phase Screen Correction Model

The detection of potential rural mountain landslide displacements using time-series interferometric Synthetic Aperture Radar has been challenged by both atmospheric phase screens and decoherence noise. In this study, we propose the use of a combined distributed scatterer (DS) and the Prophet_ZTD-NEF model to rapidly map the landslide surface displacements in Diqing Tibetan Autonomous Prefecture, China. We conducted tests on 28 full-resolution SENTINEL-1A images to validate the effectiveness of our methods. The conclusions are as follows: (1) Under the same sample conditions, confidence interval estimation demonstrated higher performance in identifying SHPs compared to generalized likelihood ratio test. The density of DS points was approximately eight times and five times higher than persistent scatterer interferometry and small baseline subset methods, respectively. (2) The proposed Prophet_ZTD-NEF model considers the spatial and temporal variability properties of tropospheric delays, and the root mean square error of measured values was approximately 1.19 cm instead of 1.58 cm (PZTD-NEF). (3) The proposed Prophet_ZTD-NEF method reduced the mean standard deviation of the corrected interferograms from 1.88 to 1.62 cm and improved the accuracy of the deformation velocity solution by approximately 8.27% compared to Global Position System (GPS) measurements. Finally, we summarized the driving factors contributing to landslide instability.

Visible light visual indoor positioning system for based on residual convolutional networks and image restoration

Abstract Visible light positioning technology, with its advantages of low cost, strong anti-interference, and high precision, is widely researched and applied in various different scenarios. In this paper, for the complexity of indoor environments, considering the problem of occlusion by various obstacles that may exist in indoor spaces, which may lead to incomplete imaging of CMOS image sensors, a maximum gray value-based occlusion recovery and decoding scheme is proposed. This scheme effectively solves the problem of visible light transmission channel being blocked and accomplishes LED-ID decoding. In addition, the overflow effect due to uneven light irradiation gathered in each pixel row affects the accuracy of decoding LED-ID, which in turn leads to poor positioning accuracy. In this paper, we propose to use an adaptive gamma correction method to eliminate the influence of the overflow effect and to improve the accuracy of decoding. In order to improve the positioning accuracy, a visible light positioning algorithm based on residual convolutional network (VisiResNet) is proposed to achieve high accuracy positioning. The experimental results show that the average positioning error is 9.7 cm in a space of 9m× 3m× 3m, and a decoding accuracy of 90% within 1.4m is achieved in the face of different occlusion situations. The system can achieve centimeter-level positioning accuracy and meet the indoor positioning requirements.

System Identification for Robust Control of an Electrode Positioning System of an Industrial Electric Arc Melting Furnace

Through system identification for robust control methods and utilizing real-time experimental field data, a comprehensive mathematical model is derived that represents the dynamic performance of a single electrode positioning system (EPS) in an industrial electric arc melting furnace (EAF). This EPS is characterized by large, time-varying dynamic parameters, which fluctuate based on operating conditions, specifically as the electrode weight changes within its operational range. The system identification methodology for robust control is developed in four main steps, progressing from experimental design to model validation. This approach yields a nominal model of the actual system and provides a trustworthy estimate of the region of uncertainty of the model, bounded by models of the real system under maximum and minimum electrode weight conditions (limit operating models). The methodology generates three fourth-order time-delay models using an ARMAX structure. The results are promising, as system identification for robust control enables the derivation of mathematical models specifically tailored for designing robust controllers. These controllers significantly enhance the EPS control system’s performance and substantially reduce energy consumption and environmental emissions.

Improved Kalman Filtering Algorithm Based on Levenberg–Marquart Algorithm in Ultra-Wideband Indoor Positioning

To improve the current indoor positioning algorithms, which have insufficient positioning accuracy, an ultra-wideband (UWB) positioning algorithm based on the Levenberg–Marquardt algorithm with improved Kalman filtering is proposed. An alternative double-sided two-way ranging (ADS-TWR) algorithm is used to obtain the distance from the UWB tag to each base station and calculate the initial position of the tag by the least squares method. The Levenberg–Marquardt algorithm is used to correct the covariance matrix of the Kalman filter, and the improved Kalman filtering algorithm is used to filter the initial position to obtain the final position of the tag. The feasibility and effectiveness of the algorithm are verified by MATLAB simulation. Finally, the UWB positioning system is constructed, and the improved Kalman filter algorithm is experimentally verified in LOS and NLOS environments. The average X-axis and the Y-axis positioning errors in the LOS environment are 6.9 mm and 5.4 mm, respectively, with a root mean square error of 10.8 mm. The average positioning errors for the X-axis and Y-axis in the NLOS environment are 20.8 mm and 18.0 mm, respectively, while the root mean square error is 28.9 mm. The experimental results show that the improved algorithm has high accuracy and good stability. At the same time, it can effectively improve the convergence speed of the Kalman filter.

Mamba-VNPS: A Visual Navigation and Positioning System with State-Selection Space

This study was designed to address the challenges of autonomous navigation facing UAVs in urban air mobility environments without GPS. Unlike traditional localization methods that rely heavily on GPS and pre-mapped routes, Mamba-VNPS leverages a self-supervised learning framework and advanced feature extraction techniques to achieve robust real-time localization without external signal dependence. The results show that Mamba-VNPS significantly outperforms traditional methods across multiple aspects, including localization error. These innovations provide a scalable and effective solution for UAV navigation, enhancing operational efficiency in complex spaces. This study highlights the urgent need for adaptive positioning systems in urban air mobility (UAM) and provides a methodology for future research on autonomous navigation technologies in both aerial and ground applications.

A multi-verse Optimizer Approach Based on a Salp Swarm for Image Feature Selection to Observe Changes in Urban Lands

Many changes have occurred in most urban areas, and it is usually governed by many geographical and social factors, the result will be either the expansion or contraction of these cities. In this paper, an optimizer method called salp swarm optimization (SSO) is used to build a driving force model for urban lands according to the changes in the driving force mechanism. The principle of (SSO) is extracting the driving force classification rules corresponding to different types of land use change samples by imitating the behavior of salp. The classification rules are constructed in the form of "IF ... THEN", and three different fitness functions are selected for simulation verification. The data set used in this search represents images from Global Positioning System (GPS) satellites and remote sensing data. The experimental results show that the overall accuracy evaluation of the salp swarm optimization model is superior to other algorithms, indicating that the SSA is feasible for land use change modeling.

Automated Estimation of Building Heights with ICESat-2 and GEDI LiDAR Altimeter and Building Footprints: The Case of New York City and Los Angeles

Accurate estimation of building height is crucial for urban aesthetics and urban planning as it enables an accurate calculation of the shadow period, the effective management of urban energy consumption, and thorough investigation of regional climatic patterns and human-environment interactions. Although three-dimensional (3D) cadastral data, ground measurements (total station, Global Positioning System (GPS), ground laser scanning) and air-based (such as Unmanned Aerial Vehicle—UAV) measurement methods are used to determine building heights, more comprehensive and advanced techniques need to be used in large-scale studies, such as in cities or countries. Although satellite-based altimetry data, such as Ice, Cloud and land Elevation Satellite (ICESat-2) and Global Ecosystem Dynamics Investigation (GEDI), provide important information on building heights due to their high vertical accuracy, it is often difficult to distinguish between building photons and other objects. To overcome this challenge, a self-adaptive method with minimal data is proposed. Using building photons from ICESat-2 and GEDI data and building footprints from the New York City (NYC) and Los Angeles (LA) open data platform, the heights of 50,654 buildings in NYC and 84,045 buildings in LA were estimated. As a result of the study, root mean square error (RMSE) 8.28 m and mean absolute error (MAE) 6.24 m were obtained for NYC. In addition, 46% of the buildings had an RMSE of less than 5 m and 7% less than 1 m. In LA data, the RMSE and MAE were 6.42 m and 4.66 m, respectively. It was less than 5 m in 67% of the buildings and less than 1 m in 7%. However, ICESat-2 data had a better RMSE than GEDI data. Nevertheless, combining the two data provided the advantage of detecting more building heights. This study highlights the importance of using minimum data for determining urban-scale building heights. Moreover, continuous monitoring of urban alterations using satellite altimetry data would provide more effective energy consumption assessment and management.

Application of non-base station positioning system in rice transplanting

Abstract. Rice transplanting machines have significantly increased the efficiency of rice planting and reduced labor costs. However, the transportation and loading of rice seedlings in the paddy fields still require a substantial amount of labor. In response to this need, implementing a fully automated rice transportation and loading system has become a demand for some enterprises, with the positioning of the rice transplanting machine being a critical subsystem to help locate the transportation destination. This paper, based on the theories of micro-controllers and multi-target positioning algorithms without base stations, utilizes literature review and theoretical analysis to design a base-station-free positioning system on a micro-controller. The system uses the Inertial Measurement Unit (IMU) module of the lower machine to collect the relative positions of the path nodes of the rice transplant machine. The upper machine's Multi-target Dead Reckoning (MDR) algorithm then determines the actual path, thereby pinpointing the specific location of the transplanter. This design will help complete the positioning of the rice transplanting machine in the paddy fields, reduce the impact of environmental factors on positioning accuracy, and lower the modification costs.

Two-Dimensional Differential Positioning with Global Navigation Satellite System Signal Frequency Division Relay Forwarding to Parallel Leaky Coaxial Cables in Tunnel

To address the issue of GNSS receivers being unable to function properly in tunnels due to the loss of Global Navigation Satellite System (GNSS) signals, this paper proposes a two-dimensional differential positioning system for tunnel environments based on dual leaky coaxial (LCX) cables with GNSS signal frequency relay forwarding. The system receives mixed GNSS signals from open environments and utilizes the frequency selection capabilities of the MAX2769E chip to separate and generate radio frequency signals at different frequencies corresponding to GPS, BDS, and GLONASS. These signals are then used to drive three ports of the LCX cables, which are laid in parallel within the tunnel. By leveraging the uniform radiation characteristics of the LCX cables, stable GNSS signal coverage is achieved throughout the tunnel. On the receiving end, the GNSS receiver achieves two-dimensional positioning by utilizing inter-satellite pseudorange differences and reference point error correction. The simulation results indicate that the dual T-shaped radiating LCX cables configuration offers excellent positioning accuracy and noise resistance, achieving meter-level positioning accuracy in tunnel environments.

Next-Generation Remote Sensing Data at Multiple Spatial Scales Improves Understanding of Habitat Selection by a Small Mammal

Recent advances in optical remote sensing (RS) technology in combination with lightweight Global Positioning System (GPS) tracking devices now make analyzing the multi-scale habitat selection (HS) of small mammals < 2 kg possible. However, there have been relatively few multi-scale HS studies integrating fine-scale RS data with data-rich, GPS-derived movement data from small mammals. This is critical because small mammals commonly select habitat features across multiple scales. To address this gap, we investigated the HS of a small mammal, fox squirrels (Sciurus niger), which are known to cover relatively large areas and select fine-scale environmental features. We specifically asked the following questions: (1) Do next-generation RS variables improve HS models at single spatial scales? (2) Do multi-scale HS models improve upon those at single spatial scales? Using data from 45 individuals, we constructed HS models at three spatial scales: 4 ha (210 m × 210 m), 0.09 ha (30 m × 30 m), and 0.01 ha (10 m × 10 m) using traditional and next-generation RS data. The 4-ha model, using traditional and next-generation RS data, produced the best single-scale model, explaining 58% of the variations in HS. However, the multi-scale model provided the most informative model, explaining 68% of the variations in HS. Our models provide evidence for the value of next-generation RS data when quantifying HS and additional support for the idea of studying HS at multiple spatial scales.

Investigation of the Effect of Integrated Offset, GPS, and InSAR Data in the Stochastic Source Modeling of the 2002 Denali Earthquake

This study investigates the effect of geological field measurement (offset), global positioning system (GPS), and interferometric synthetic aperture radar (InSAR) data on the estimation of the co-seismic earthquake displacements of the 2002 Denali earthquake. The analysis is conducted using stochastic source modeling. Uncertainties associated with each dataset limit their effectiveness in source model selection and raise questions about the adequate number of datasets and their type for reliable source estimation. To address these questions, stochastic source models with heterogeneous earthquake slip distributions are synthesized using the von Kármán wavenumber spectrum and statistical scaling relationships. The surface displacements of the generated stochastic sources are obtained using the Okada method. The surface displacements are compared with the available datasets (i.e., offset, GPS, and InSAR) individually and in an integrated form. The results indicate that the performance of stochastic source generation can be significantly improved in the case of using GPS data and in the integrated case. Overall, based on the case study of the 2002 Denali earthquake, the combined use of all available datasets increases the robustness of the stochastic source modeling method in characterizing surface displacement. However, GPS data contribute more than InSAR and offset data in producing reliable source models.

Using spatial video and deep learning for automated mapping of ground-level context in relief camps

BackgroundThe creation of relief camps following a disaster, conflict or other form of externality often generates additional health problems. The density of people in a highly stressed environment with questionable safe food and water access presents the potential for infectious disease outbreaks. These camps are also not static data events but rather fluctuate in size, composition, and level and quality of service provision. While contextualized geospatial data collection and mapping are vital for understanding the nature of these camps, various challenges, including a lack of data at the required spatial or temporal granularity, as well as the issue of sustainability, can act as major impediments. Here, we present the first steps toward a deep learning-based solution for dynamic mapping using spatial video (SV).MethodsWe trained a convolutional neural network (CNN) model on a SV dataset collected from Goma, Democratic Republic of Congo (DRC) to identify relief camps from video imagery. We developed a spatial filtering approach to tackle the challenges associated with spatially tagging objects such as the accuracy of global positioning system and positioning of camera. The spatial filtering approach generates smooth surfaces of detection, which can further be used to capture changes in microenvironments by applying techniques such as raster math.ResultsThe initial results suggest that our model can detect temporary physical dwellings from SV imagery with a high level of precision, recall, and object localization. The spatial filtering approach helps to identify areas with higher concentrations of camps and the web-based tool helps to explore these areas. The longitudinal analysis based on applying raster math on the detection surfaces revealed locations, which had a considerable change in the distribution of tents over space and time.ConclusionsThe results lay the groundwork for automated mapping of spatial features from imagery data. We anticipate that this work is the building block for a future combination of SV, object identification and automatic mapping that could provide sustainable data generation possibilities for challenging environments such as relief camps or other informal settlements.

Research on intelligent cutting control technology of transverse moving machine for large cross-section roadheader

With the continuous development of the coal mine industry in our country, the cross-section of the roadway is getting larger and larger. The widely used EBZ series roadheader cannot complete the roadway cutting operation with a large cross-section at one time, so it usually needs to move the equipment left and right to complete all its cutting tasks. This paper studies the intelligent control technology of large cross-section roadway cutting, which mainly solves the positioning and attitude determination technology of roadheader in the large cross-section, and offers an accurate shape-cutting control technology and a path planning technology using a lateral moving machine. This paper proposes to combine the signals of laser sensor and fiber-optic inertial navigation system to form a combined positioning and attitude determination system, so as to make use of the advantages and avoid the disadvantages of their respective systems, and improve the accuracy of roadheader positioning and attitude determination. A swing kinematics model of the cutting arm is established, based on which the precise motion control of the first roadway section cutting and the second roadway section cutting can be realized. A key parameter calculation model of roadheader lateral moving machine is put forward to calculate the operation parameters for roadheader lateral moving machine path planning. Finally, the experimental research on intelligent cutting control technology of large cross-section roadheader is carried out in 12307 intelligent heading face of Wangjialing Coal Mine. The results show that the positioning error of the combined positioning and attitude determination system proposed in this paper is between 50-90 mm in the X-axis direction and between 25-45 mm in the Y-axis direction. The average attitude measurement error of heading angle is between 0.5 and 1.5°. This makes the technology appropriate for engineering use. The studied cutting control system achieves a cutting error of 25-50 mm for the first section, and a cutting error of 40-90 mm for the complete section. The complete section cutting error meets the ±100 mm cutting error requirement required in engineering. The intelligent cutting control technology of the lateral moving machine of the large-section tunnel boring machine studied in this paper can realize accurate cutting of the large-section tunnel of 5.6 meters. The research results of this paper provide a reference for the intelligent construction of heading faces.

SPATIAL DISTRIBUTION AND PATRONAGE OF RECREATIONAL FACILITIES IN UNIVERSITY OF ILORIN, NIGERIA

The paper examined the spatial distribution and patronage pattern of recreational facilities in the University of Ilorin (UNILORIN). The study used primary data, which were obtained through a structured questionnaire administered randomly to the selected academic and non - teaching staff of UNILORIN. Geo-spatial data on the locations of the recreational facilities were collected and analyzed with the use of Global Positioning System (GPS) and Nearest Neighbour Analysis respectively. Pearson’s Product Moment Correlation Analysis was employed to examine the relationship between residence and patronage of recreational facilities at 5% level of significance. With an Rn value of 1.47 (p=0.000378), the distribution of recreational facilities in UNILORIN was dispersed. The paper also observed that, there was no significant relationship between staff place of residence and their patronage of recreational facilities (r = 0.05, p value = 0.474). The study established that health benefits and pleasure with Enhancing Factor Index of 2.58 and 2.46 respectively, were the major enhancing factors of recreational facilities patronage. The paper concluded that locations of recreational facilities are far from each other and by implication the distribution is nor regular. The paper therefore recommended that future recreational facilities should be evenly distributed so as to foster more patronage by staff members of University of Ilorin.

SPATIAL DISTRIBUTION AND PATRONAGE OF RECREATIONAL FACILITIES IN UNIVERSITY OF ILORIN, NIGERIA

The paper examined the spatial distribution and patronage pattern of recreational facilities in the University of Ilorin (UNILORIN). The study used primary data, which were obtained through a structured questionnaire administered randomly to the selected academic and non - teaching staff of UNILORIN. Geo-spatial data on the locations of the recreational facilities were collected and analyzed with the use of Global Positioning System (GPS) and Nearest Neighbour Analysis respectively. Pearson’s Product Moment Correlation Analysis was employed to examine the relationship between residence and patronage of recreational facilities at 5% level of significance. With an Rn value of 1.47 (p=0.000378), the distribution of recreational facilities in UNILORIN was dispersed. The paper also observed that, there was no significant relationship between staff place of residence and their patronage of recreational facilities (r = 0.05, p value = 0.474). The study established that health benefits and pleasure with Enhancing Factor Index of 2.58 and 2.46 respectively, were the major enhancing factors of recreational facilities patronage. The paper concluded that locations of recreational facilities are far from each other and by implication the distribution is nor regular. The paper therefore recommended that future recreational facilities should be evenly distributed so as to foster more patronage by staff members of University of Ilorin.

Integrated Early Flood Prediction using Sentinel-2 Imagery with VANET-MARL-based Deep Neural RNN

<p style="line-height:200%;"><span style="font-family:"Times New Roman","serif";font-size:12.0pt;line-height:200%;" lang="EN-US">Floods are a major contributor to the destruction of infrastructure and the overall economy of afflicted countries, leading to loss of life and significant damage. Remote sensing, satellite imagery photography, global positioning system, and geographic information system (GIS) are commonly used to identify floods and analyze the associated damages. The research presented here integrates Sentinel-2 satellite images, VANET with Multi-Agent Reinforcement Learning (MARL), and a deep neural RNN for early flood prediction. Sentinel-2 imaging delivers extensive geographical and temporal data regarding land cover and water bodies, while VANET-MARL provides real-time ground-truth information and distributed decision-making capabilities. The Deep Neural RNN efficiently acquires intricate patterns from the combined data to forecast the likelihood, intensity, and scope of floods. The experimental findings clearly show that the suggested system outperforms standard methods in terms of accuracy, precision, recall, and lead time. VANET-MARL integration improves the system's ability to adapt and remain strong in changing circumstances. The results showed that the method was 94.8% accurate in early predicting floods.</span></p>