Spatial Mapping Research Articles

Hydrochemical assessment of borehole water in Effurun, Delta state Niger Delta, Nigeria: implications for public health and safety

Borehole water plays a critical role in providing drinking water in Effurun, Delta State, Nigeria. However, concerns about potential contamination from both natural and anthropogenic sources necessitate regular groundwater quality assessments. This study evaluates the hydrochemical properties of borehole water to ensure public health safety and to understand the groundwater's suitability for consumption. The aim of this study is to assess the quality of borehole water in Effurun by analyzing major ions, trace metals, and other hydrochemical parameters, and to compare the findings with WHO and NSDWQ drinking water standards. Ten borehole water samples were collected from various locations across Effurun. Hydrochemical analyses were conducted to measure cations (calcium, magnesium, sodium, potassium), anions (chloride, bicarbonate, sulfate, nitrate), and trace metals (lead, copper, zinc, manganese). Statistical analysis and spatial mapping were employed to evaluate the distribution of ion concentrations and identify areas of concern. Calcium levels in the boreholes ranged from 29.95 mg/L to 64.50 mg/L, while magnesium levels were more consistent, ranging between 9.50 mg/L and 14.20 mg/L. Sodium and potassium showed moderate correlations with calcium. Chloride levels varied widely, indicating potential contamination from seawater intrusion or industrial discharge. Elevated sulfate and nitrate concentrations pointed to agricultural runoff. Trace metals, particularly lead, exceeded safety limits, with lead concentrations ranging from 0.092 mg/L to 0.127 mg/L, posing significant health risks. Spatial analysis revealed varying ion concentrations across Effurun, with higher calcium and magnesium levels near water bodies, suggesting local geology's influence. The central area exhibited moderate ion concentrations, while the southeastern region had the highest calcium levels, possibly due to geological formations rich in calcium or human activities. The study highlights diverse hydrochemical characteristics of Effurun's borehole water, with some areas showing contamination beyond acceptable limits. Regular monitoring is essential to ensure safe drinking water and address potential health risks. The findings stress the need for improved water resource management and contamination control. This study provides a comprehensive spatial analysis of groundwater quality in Effurun, identifying specific areas at risk of contamination and offering data-driven recommendations for public health and sustainable water management.

Investigation of tactile and strain sensing utilizing carbon-fiber network in silicone elastomer matrix

Abstract As the popularity of immersive and interactive technology continues to grow, there is demand for wearable or conformable tactile and strain sensors for human-computer-interaction (HCI). A major challenge in current conformable sensors is durability, tremendously limiting their applications in the market. This study utilizes long carbon-fiber networks in silicone elastomer matrix as electrodes for sensing, which consequently strengthens the sensors. Row and column pattern of long carbon-fiber was used to create projected capacitive touch sensors at every intersection and demonstrate multi-touch and spatial mapping capabilities. Similarly, these intersections were also demonstrated as piezoresistive force sensor where deformation from pressure on the intersection decreases the resistance between the row and column electrodes by altering the electrical network among the carbon-fibers. Lastly, piezoresistive strain sensors were manufactured by impregnating a pre-woven carbon-fiber fabric with silicone. Deformation in the 45-degree angle relative to the fabric caused changes in the electrical interactions between carbon-fibers and therefore the resistance along the sensor as well. These strain sensors showed an interesting behavior of increasing then decreasing resistance as the tensile load shifted from the silicone matrix to the carbon-fibers. The ultimate strength of properly bonded strain sensors using Smooth-On Sil-Poxy was significantly greater than the strength of pure two-part silicone. An intricately designed carbon-fiber network within a silicone matrix could combine these sensing mechanisms to potentially create a durable and compact multifunctional sensor that expands wearable sensing technology to rugged environments.

High-Resolution Mapping of Topsoil Sand Content in Planosol Regions Using Temporal and Spectral Feature Optimization

Soil sand content is an important characterization index of soil texture, which directly affects soil water regulation, nutrient cycling, and crop growth potential. Therefore, its high-precision spatial distribution information is of great importance for agricultural resource management and land use. In this study, a remote sensing prediction method based on the combination of time-phase optimization and spectral feature preference is innovatively proposed for improving the mapping accuracy of the sand content in the till layer of a planosol area. The study first analyzed the prediction performance of single-time-phase images, screened the optimal time-phase (May), and constructed a single-time-phase model, which achieved significant prediction accuracy, with a coefficient of determination (R2) of 0.70 and a root mean square error (RMSE) of 1.26%. Subsequently, the model was further optimized by combining multiple time phases, and the prediction accuracy was improved to R2 = 0.77 and the RMSE decreased to 1.10%. At the feature level, the recursive feature elimination (RF-RFE) method was utilized to preferentially select 19 key spectral variables from the initial feature set, among which the short-wave infrared bands (b11, b12) and the visible bands (b2, b3, b4) contributed most significantly to the prediction. Finally, the prediction accuracy was further improved to R2 = 0.79 and RMSE = 1.05% by multi-temporal-multi-feature fusion modeling. The spatial distribution map of sand content generated by the optimized model shows that areas with high sand content are primarily located in the northern and central regions of Shuguang Farm. This study not only provides a new technical path for accurate mapping of soil texture in the planosol area, but also provides a reference for the improvement of remote sensing monitoring methods in other typical soil areas. The research results can provide a reference for mapping high-resolution soil sand maps over a wider area in the future.

Using geographic interviews to conduct community assessments—insights for MSW pedagogy

ABSTRACT Social work is predicated as a person-in-environment profession, but few tools presently exist for students to operationalize person-in-environment through assessment and systematic understanding. Geographic interviews, which combine spatial mapping and qualitative go-along interviews, are one possible tool with promise for application in the social work classroom. This teaching note describes the process of integrating geographic interviews as one data collection method MSW students could utilize in a community assessment assignment. We share learnings from student assignments (N = 8) and instructor focus groups (N = 4). We found that while there was a high level of student and instructor interest, the unfamiliarity of the tool and perceived time required meant that some students did not choose to use the tool. When they did use the tool, they found it valuable for their learning; however, many students did not use the tool to expected fidelity. We suggest the need for more substantial training of students and instructors when using this valuable tool, including examples, practice assignments, and suggestions for intentional adaptation.

Plasmonic Light Emission by Inelastic Charge Transport in Ultrathin Zinc Oxide/Metal Heterostructures.

Controlling light emission from plasmonic nanojunctions is crucial for developing tunable nanoscale light sources and integrated photonic applications. It requires precise engineering of plasmonic nanocavity electrodes and a detailed understanding of electrically driven light emission. Using scanning tunneling microscopy-induced luminescence (STML), we studied plasmonic light emission from ultrathin ZnO/Ag(111) inside a silver nanocavity. At positive bias, plasmonic luminescence, caused by radiative decay of localized surface plasmons (LSP), is spectrally low-pass filtered by the ZnO layers. The emission of photon energies above the conduction band edge energy (ECB) of ZnO is suppressed, while the spectral distribution below ECB resembles the LSP resonance on Ag(111). This spectral filtering is absent at negative bias and depends on the local electronic structure, as confirmed by spatial STML mapping. Our findings demonstrate that the ZnO conduction band serves as the initial state for plasmonic luminescence driven by inelastic electron transport across the ZnO/Ag(111) interface.

GIS based ground water assessment of Nilakkottai Taluk, Tamil Nadu, India: hydrogeochemistry and statistical perspective

Water quality is imperative for drinking and agriculture purposes in order to meet the increasing requirements for water. The systematic assessment of groundwater quality in Nilakkottai Taluk, Dindigul District, Tamil Nadu, was performed. In order to ascertain the quality of the study area’s groundwater, various water quality indices, spatial distribution maps, multivariate statistical analysis, and hydrofacies diagrams have been contemplated. 40 samples were collected and analysed for 20 water quality parameters, using the standard techniques. The quality results of the irrigation analysis showed that the groundwater samples were satisfactory for agricultural use. The deduction of four principal components denotes that hydrogeochemical processes and anthropogenic inputs were the main controlling factors. The durov plot demonstrated the dominance of Ca-HCO3 type groundwater, indicating a weathering process through fresh water recharge. This study insisted that majority of the samples satisfactory for crop yield and need to be protected from further contamination.

An Intuitive Approach on Transfer Learning with an IBF+IHP Model for Stroke Classification and Prediction

A cerebral stroke can have significant health ramifications. Efficient stroke prevention requires precise prevention and prompt detection of risk factors. This study introduces a novel predictive modeling technique that uses uncomplicated spatial filter maps and ensemble approaches to enhance stroke risk prediction. The proposed approach utilizes ensemble approaches along with comprehensible spatial filter maps to uncover significant spatial patterns in brain imaging data. The ensemble approach employs a multitude of prediction models to enhance the accuracy of stroke risk forecasts. The experimental findings demonstrate that spatial filter maps and ensemble techniques surpass traditional models in predicting performance. This study showcases the potential of spatial filters to include several patient data to accurately predict stroke risk with a 98% success rate.

Geo-environmental GIS modeling to predict flood hazard in heavy rainfall eastern Himalaya region: a precautionary measure towards disaster risk reduction.

The Eastern Himalaya region is highly susceptible to flood and other hydrological hazards due to frizzle geophysical setup, reshaping geomorphology, and heavy annual rainfall (1600-3200 mm). Despite that, anthropogenic activities have been enhancing this susceptibility which increases the intensity and impact of floods in terms of economic loss, human loss, and environmental degradation. Addressing this environmental problem, a geospatial technology-based case study of the Kohima district, Nagaland state (India), a part of the eastern Himalaya is presented here. Various experiential models are available for computing flood hazards; however, the geospatial technique-based analytic hierarchy process (AHP) method was applied in this study due to its robustness and high accuracy level. AHP integrates reclassified GIS layers of hazard-triggering factors and sub-factors by assigning relative weights 1-9 based on their corresponding impacts on flood occurrence. Overlay operation of reclassified GIS layer (causative factors and sub-factors) in ArcMap 10.8 software generated flood spatial variability map which shows four zones, namely low, moderate, high, and very high hazard zones, covers 23%, 35%, 28%, and 14% proportion of total area (978.96 km2), respectively. The study poses a serious concern for the study area as most of the densely populated urban centers fall into moderate to very high flood hazard zones including the state capital city Kohima. So, to avert a worse flood disaster, a flood hazard zone study is the need of the hour. The present study can be used as a decision support system (DSS) for flood disaster risk reduction, infrastructural development, and land use planning in Kohima district.

Assessing water color anomalies: A hue angle approach in the Gulf of Izmit.

Understanding water color is important because it is influenced by biogeochemical constituents, directly affects human perceptions of water quality, and serves as a key water quality metric. This study evaluates the hue angle methodology for monitoring water quality dynamics, using the Gulf of Izmit in the Sea of Marmara as a case study. We employed in-situ water quality measurements, spectroradiometer data, and Sentinel-2 satellite imagery from campaigns in October 2021 and 2022. Hue angles were computed for these dates, and spatial distribution maps were generated to assess water quality dynamics and detect anomalies. The accuracy of these maps was validated using hue angle values from ASD FieldSpec 4 spectroradiometer measurements, with metrics including Mean Relative Error (MRE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) demonstrating the method's feasibility (RMSE, MRE, MAE for 2021: 3.16°, 0.02%, 2.91°; for 2022: 2.36°, 0.01%, 1.65°). Correlation analyses with selected optically active constituents, such as Chlorophyll-a (Chl-a) and electrical conductivity (EC), as well as non-optically active constituents like Secchi Disk Depth (SDD) and silicate, revealed strong correlations, particularly improving from 2021 to 2022. The methodology's performance was further evaluated using Sentinel-2 data from April 29, 2021, in the Gulf of Gemlik, chosen for its similar environmental challenges. High correlations with in-situ measurements were observed, even without spectroradiometer validation, providing robust evidence of the method's effectiveness in detecting water quality anomalies across diverse coastal environments.

Improved Aftershock Forecasts Using Mainshock Information in the Framework of the ETAS Model

AbstractThe Epidemic Type Aftershock Sequence (ETAS) model is the most widely used and powerful statistical model for aftershock forecasting. While the distribution of aftershocks around the mainshock is anisotropic, the spatial probability density function of the ETAS model is commonly assumed to be isotropic due to insufficient information. In addition, its parameter estimation can be highly biased due to catalog incompleteness after the mainshock. Thus, we extended the recently developed 2D temporal ETASI, which accounts for short‐term incompleteness, to 2D and 3D spatiotemporal ETASI, considering additional spatial occurrence probabilities in the framework of ETAS and ETASI to improve aftershock forecasting. We replaced the isotropic spatial kernel with anisotropic kernels estimated by a spatial probability map of stress scalars, including Coulomb stress changes on master fault orientation (MAS), Coulomb stress changes on variable mechanisms (VM), maximum shear (MS), and von Mises stress (VMS), and the nearest distance to the ruptured fault of the mainshock (R). The fit to six prominent mainshock‐aftershock sequences in California demonstrates that the ETASI model outperforms the standard ETAS model. Furthermore, positive information gains indicate that using stress calculations as additional input information can improve the parameter fit. This improvement is weaker in 3D, which is likely related to greater positional uncertainty in the depth domain. However, incorporating the probability map calculated as a function of the nearest distance to the mainshock rupture leads to the best performance in all model variants.

ANALYSIS OF THE IMPACT OF NATURAL HAZARDS ON TOURISM ON THE LEFT BANK OF LAKE IZVORU MUNTELUI (NORTH-EASTERN ROMANIA)

One of the areas with great touristic potential in Romania’s mountains is Izvoru Muntelui Lake (Neamt county) from the western slopes of the Stânișoara Mountains in the Eastern Carpathians. The area is remarkably picturesque in terms of both the natural surroundings and history, including traditions, folklore, and ethnography. Nevertheless, the region is exposed to natural risk phenomena such as floods, landslides, crumbling, etc., which can affect different economic fields, including tourism. The present study aims to analyze the relationship between this zone's natural hazards and tourism activities and their impact. Our approach is based on quantitative and qualitative analysis, interview and questionnaire techniques, but also on literature review, field research, statistics, overlay and spatial mapping of data using GIS techniques and critical interpretation of results through mathematical statistics using Covariance Product of Deviations from the mean and the Bravais-Pearson linear correlation coefficient to measure the intensity of the interdependence between the degree of damage to the road infrastructure and the tourist infrastructure. The results show that the landslides significantly impact the public infrastructures between Poiana Teiului and Bicaz, heavily affecting the national road DN15. A total of 73 impact points were inventoried, representing sections of roads and road infrastructure elements affected by landslides. At the same time, the analysis of the responses to the questionnaire revealed that natural hazards, especially landslides and floods, are perceived as a threat by most tourism operators in the area. The classification of the vulnerability of tourism infrastructure into four categories (low, medium, high, and very high), based on susceptibility maps, has led to the determination of vulnerability to landslides, revealing the following findings: four accommodation units have a medium vulnerability index, two have a high vulnerability index, and one records a very high vulnerability index. It is highlighted that an accurate perception of natural hazards can contribute to increasing the awareness and preparedness of vulnerable people, thus increasing the resilience of present and future generations.

Estimating infectious disease transmission risk: An analysis based on multi-scale modeling of respiratory droplet transport

In the present work, a multi-scale model is used to simulate the trajectory of exhaled droplets for different types of respiratory events. By estimating the saliva viral content, information on the exhaled droplet trajectory and size distribution are exploited to derive spatial maps of viral concentration. The maps are used to estimate the risk of infection by direct inhalation for a susceptible individual exposed to exhalations. Discriminating between the droplets falling to the ground and those remaining airborne, further estimates of the risks associated with fomite and airborne transmission routes are made possible. Simulations are based on an analytical model, recently developed by the authors, implementing the equations of droplet transport, evaporation, energy, and chemical composition. Turbulent dispersion is modeled with a random walk approach, whereas the droplets are released randomly within the exhalation time span and mouth/nose orifice cross section. Droplet transport within the respiratory cloud, a critical aspect in this kind of investigation, is evaluated by coupling the analytical model to the results of unsteady computational fluid dynamics simulations of the respiratory scenarios investigated, namely: mouth breathing, nose breathing, speaking, coughing, and sneezing. Results are compared to those obtained in a previous work where the respiratory cloud was simulated through a two-dimensional unsteady empirical model proposed by the authors based on momentum conservation. The multi-scale model provides better predictions of small droplet trajectories albeit at a higher computational cost. The risk maps provided constitute a useful tool for assessing prevention needs in controlling the spread of epidemics.

Mapping eye, arm, and reward information in frontal motor cortices using electrocorticography in non-human primates.

Goal-directed reaches give rise to dynamic neural activity across the brain as we move our eyes and arms, and process outcomes. High spatiotemporal resolution mapping of multiple cortical areas will improve our understanding of how these neural computations are spatially and temporally distributed across the brain. In this study, we used micro-electrocorticography (µECoG) recordings in two male monkeys performing visually guided reaches to map information related to eye movements, arm movements, and receiving rewards over primary motor cortex, premotor cortex, frontal eye field, and dorsolateral pre-frontal cortex. Time-frequency and decoding analyses revealed that eye and arm movement information shifts across brain regions during a reach, likely reflecting shifts from planning to execution. Although eye and arm movement temporally overlapped, phase clustering analyses enabled us to resolve differences in eye and arm information across brain regions. This analysis revealed that eye and arm information spatially overlapped in motor cortex, which we further confirmed by demonstrating that arm movement decoding performance from motor cortex activity was impacted by task-irrelevant eye movements. Phase clustering analyses also identified reward-related activity in the pre-frontal and premotor cortex. Our results demonstrate µECoG's strengths for functional mapping and provide further detail on the spatial distribution of eye, arm, and reward information processing distributed across frontal cortices during reaching. These insights advance our understanding of the overlapping neural computations underlying coordinated movements and reveal opportunities to leverage these signals to enhance future brain-computer interfaces.Significance statement Picking up your coffee mug requires coordinating movements of your eyes and hand and processing the outcomes of those movements. Mapping how neural activity relates to different functions helps us understand how the brain performs these computations. Many mapping techniques have limited spatial or temporal resolution, restricting our ability to dissect computations that overlap closely in space and time. We used micro-electrocorticography recordings to map neural activity across multiple cortical areas while monkeys made goal-directed reaches. These measurements revealed high spatial and temporal resolution maps of neural activity related to eye, arm, and reward information processing. These maps reveal overlapping neural computations underlying movement and open opportunities to use eye and reward information to improve therapies to restore motor function.

Monitoring of Traffic Flow Parameters Using Geospatial Technology for Effective Traffic Management around Maiduguri Central Business District

Abstract: The ever-increasing traffic flow has become one of the most critical challenges faced by municipalities and governments worldwide. This paper intends to ascertain the traffic flow parameters using geospatial technology for effective traffic management around the Central Business District (CBD) in Maiduguri to provide solutions to the menace of traffic congestion. Data were obtained from primary and secondary sources. The primary data were obtained from Unmanned Aerial Vehicles (UAV) and Global Positioning System (GPS). UAVs were utilized to record (videography) the traffic flow of the chosen traffic corridors during the peak hours (morning, 7:00 to 9:00 am and evening, 4:00 to 6:00 pm). The classification of the vehicles and conversion to a common standard format was done in Microsoft Excel before being carried forward for GIS Operations in ArcGIS 10.7 Environment to achieve geospatial spatial analysis and mapping of the field data. The results show that Tricycles dominated the traffic during the peak periods, followed by Cars. This indicates that the Tricycle has been the overriding and preferred means of public transport in Maiduguri since 2009 after the ban placed on motorcycles. The also reveals that no matter the direction of movement in all the approach arms, Tricycles override all other means of transportation. The results also show that all the Trio Intersections were observed to have high traffic volume on all their trajectory arms, with only the Tabacco intersection having medium traffic volume during the morning peak period. Given the above findings, the study recommended that the Government to consider the construction of a flyover, particularly at the post office intersection.

Long-term impact of different prevalent cropping systems on soil physico-chemical characteristics under subtropical climate conditions of Punjab, Pakistan

Lack of site-specific nutrients information for different cropping systems has been a major challenge in addressing declining soil fertility levels and enhance crop productivity in Punjab, Pakistan. Therefore, the study was designed to assess and quantify soil physico-chemical characteristics, crop yield and economic feasibility of different cropping systems (CS), including groundnut-wheat (G-W), rice-wheat (R-W), fallow-gram/wheat (F-G/W), mix cropping (Mix C) and cotton-wheat (C-W). A total of 470 georeferenced soil samples were collected using a random survey approach, and the samples were analyzed for soil texture, pH, electrical conductivity (EC), organic matter percentage (OM), total nitrogen percentage (TN), accessible phosphorus (AvP) (mg kg−1) and extractable potassium (ExK) (mg kg−1). For crop yield and economic feasibility, the data collected for each crop were summed up and mean data for every cropping system were compared. Cotton-wheat cropping system had the highest mean value of EC (1.57 dS/m), while mix cropping showed the maximum level of OM (0.53%), TN (0.028%), AvP (5.16 mg kg−1), yield (10.09 t ha−1), gross revenue (PK Rs. 87,883) and benefit cost ratio (2.2). The R-W cropping system had the highest pH (8.37), ExK (127.39 mg kg−1) and total cost (PK Rs. 46,882.25). Curiously, the fallow-gram/wheat cropping system had lower value of OM, TN, AvP, ExK, yield, GR and BCR, highlighted its poor performance compared to mix cropping system. Deficiencies in OM were widespread across all cropping systems, with only 3.1% of samples under mix cropping system being in the medium range. Similarly, TN %, AvP and ExK were deficient in varying degrees across all cropping systems, particularly in the fallow-gram/wheat cropping system. Spatial variability maps showed that nutrient deficiencies were more pronounced from the southern to northern side of study area. Our findings indicate that mixed cropping can improve soil health and enhance crop productivity, supporting the need for targeted nutrient management in Punjab agriculture systems.

An Integrated Statistical, Geostatistical and Hydrogeological Approach for Assessing and Modelling Groundwater Salinity and Quality in Nile Delta Aquifer

The phenomenon of seawater intrusion is becoming increasingly problematic, particularly in low-lying coastal regions and areas that rely heavily on aquifers for their freshwater supply. It is, therefore, vital to address the causes and consequences of this phenomenon in order to ensure the security of water resources and the sustainable use of water. The objective of this paper was twofold: firstly, to delineate zones with different salinization levels over time; secondly, to investigate the factors controlling seawater intrusion of the Nile Delta aquifer. Aquifer data were collected in Sharkia governorate, Egypt, over three historical periods of years: 1996, 2007, and 2018. The dataset used to create the linear model of coregionalization consisted of hydrogeological (water level), hydrodynamic (pH, EC, Na, Mg, K, Ca, HCO3, SO4), and auxiliary (distances from salt and freshwater sources) variables. Cokriging was applied to produce spatial thematic maps of the studied variables for the three years of the survey. In addition, factorial cokriging was applied to understand the processes beyond the change in the aquifer water quality and map the zones with similar characteristics. Results of mapping the first factor at long range over the three years indicated that there was an increase in seawater intrusion, especially in the northeastern part of the study area. The main cause of aquifer salinization over time was the depletion of the groundwater resource due to overexploitation.

Harmonic Motion Tracing Patterns in a Rotating Field

Abstract: In this study, we present a radar detection system for real-time object localization based on ultrasonic sensor technology. The system combines ultrasonic sensors, a servo motor, and an Arduino microcontroller to send ultrasonic waves, detect their echoes, and compute distances with high accuracy. This information is processed to generate a spatial map of the environment around it, allowing for precise object detection and localization in real time. The system works by utilizing the servo motor to spin the ultrasonic sensor, taking distance readings over an array of angles. The data is processed and presented, giving a real-time visualization of the objects found in the surroundings. This method guarantees high precision with simplicity and affordability. The flexibility of the system to various environmental conditions makes it a perfect solution for a variety of applications such as robotics, security systems, surveillance, obstacle detection, and industrial automation. One of the major benefits of this system is its cost-effectiveness and ease of construction, making it affordable for hobbyists, researchers, and industry experts. The modular design also guarantees flexibility, making it possible to customize it for particular applications or integrate it with sophisticated systems. Our study emphasizes the potential of ultrasonic sensor technology in object detection systems, opening doors to novel applications in automation, navigation, and safety systems.

360 Degree Radar Using Ultrasonic Sensor

This paper presents the design and implementation of a 360-degree radar system using a single ultrasonic sensor, an Arduino board, and a 5V stepper motor with a driver module. The objective of this project is to create a low-cost, efficient radarlike system capable of scanning the environment over a full 360-degree range. The ultrasonic sensor, mounted on the stepper motor, allows for distance measurements at multiple angular positions, providing a comprehensive spatial mapping of the surrounding area. The Arduino board functions as the central controller, handling sensor data acquisition, motor control, and real-time data processing. The 5V stepper motor, combined with the driver module, enables precise rotational movement for accurate scanning, while ensuring smooth and reliable operation. The system's capabilities are demonstrated in various applications such as obstacle detection, environmental mapping, and basic autonomous navigation. This paper also discusses the challenges encountered during system integration, including achieving accurate motor control, sensor calibration, and managing processing speed for real-time data visualization. The results indicate that this radar system, powered by affordable components, is an effective solution for applications in robotics, surveillance, and educational projects.

Spatial Mapping of Vegetation’s Potential to Counter Seasonal Groundwater Salinity in Coastal Bangladesh

Spatial Mapping of Vegetation’s Potential to Counter Seasonal Groundwater Salinity in Coastal Bangladesh

A review of integrated use of machine learning algorithms, GIS and remote sensing techniques in the prediction of rainfall patterns and floods in the U. S.

This paper systematically reviews the integrated use of machine learning algorithms, Geographic Information Systems (GIS), and Remote Sensing (RS) techniques in the prediction of rainfall patterns and flood events in the U.S. With increasing climate variability, accurate forecasting of rainfall and flood risks has become critical to safeguarding communities and infrastructure. GIS enables spatial analysis and mapping of flood-prone areas, supporting risk assessment and disaster preparedness. RS contributes real-time satellite imagery and environmental data, essential for tracking rainfall patterns and assessing surface conditions. Machine learning algorithms enhance these technologies by providing predictive modeling capabilities, allowing for more precise forecasts of rainfall intensity and flood potential. This paper explores the synergy between GIS, RS, and machine learning, emphasizing their combined impact on improving flood prediction accuracy and decision-making in disaster management. Key challenges, including data heterogeneity, computational demands, and the integration of diverse datasets, are discussed. Additionally, the paper reviews current U.S. policies on data-sharing and technology adoption, highlighting the need for regulatory frameworks that support innovation while ensuring data privacy and accuracy. Through an analysis of recent studies, this paper presents a comprehensive overview of the advantages and constraints of using these integrated technologies for flood prediction, offering insights into future directions and recommendations for enhancing flood management systems. The review concludes that advancing integrated GIS, RS, and machine learning applications will require addressing data-related challenges and fostering collaborative efforts across agencies to strengthen flood prediction and resilience capabilities in the U.S.