Interpolation Technique Research Articles

Analysis of COVID-19 epidemic with intervention impacts by a fractional operator

This study introduces an innovative fractional methodology for analyzing the dynamics of COVID-19 outbreak, examining the impact of intervention strategies like lockdown, quarantine, and isolation on disease transmission. The analysis incorporates the Caputo fractional derivative to grasp long-term memory effects and non-local behavior in the advancement of the infection. Emphasis is placed on assessing the boundedness and non-negativity of the solutions. Additionally, the Lipschitz and Banach contraction theorem are utilized to validate the existence and uniqueness of the solution. We determine the basic reproduction number associated with the model utilizing the next generation matrix technique. Subsequently, by employing the normalized sensitivity index, we perform a sensitivity analysis of the basic reproduction number to effectively identify the controlling parameters of the model. To validate our theoretical findings, numerical simulations are conducted for various fractional order values, utilizing a two-step Lagrange interpolation technique. Furthermore, the numerical algorithms of the model are represented graphically to illustrate the effectiveness of the proposed methodology and to analyze the effect of arbitrary order derivatives on disease dynamics.

Fertility and quality of arable soils in Poland: spatial–temporal analysis of long-term monitoring

Soil quality is a globally important issue from the point of view of food production, maintenance of appropriate environmental quality and ecological balance. Assessing its quality and fertility using indices is one of the methods that make it possible to formulate recommendations for good soil management. The aim of the study was, therefore, to determine the spatial and temporal distribution of soil properties in the context of soil quality assessment (SQI) and soil fertility assessment (SFI, SEF, NIV, and SSFI) using the example of a Central European country (Poland). The inverse distance-weighted spatial interpolation technique was used to estimate the spatial distribution of soil quality and fertility indicators. So far, no research has been carried out that would include a comprehensive analysis of soil fertility and quality indicators in such a large research area over many years. The results of 216 points of monitoring the chemistry of arable soils in Poland from the period 1995–2020 for 28 physicochemical soil parameters were considered. The analyses carried out showed significant variability in the properties of the described soils in Poland, as evidenced by the principal component analysis (PCA) and the coefficient of variation (exceptions: bulk density, pH). It was found that heavy metals (Zn, Cd, Cu) were responsible for the deterioration of soil properties. The results obtained for different indices were different (SFI – most soils with good fertility, SEF – high fertility, NIV – soils highly enriched with nutrients, SQI – average soil quality). It was found that the SSFI is the most appropriate indicator among the analyzed indicators for Polish conditions in relation to arable soils, as it is adapted and calibrated to the specifics of this area. The Spearman correlation matrix showed that the largest group of statistically highly significant parameter pairs belonged to heavy metals (Ni/Zn, Zn/Pb, Zn/Cu, Ni/Cu). In contrast, the strongest correlations were recorded for Ni (with effective cation exchange capacity (ECEC), Mn, Mgav, Zn, Cu). The assessment revealed limiting factors and temporal trends in the overall evaluation of the quality of the country’s agricultural soils, which are important from the point of view of both sustainable development and agricultural production.

Integrating Interpolation and Extrapolation: A Hybrid Predictive Framework for Supervised Learning

In the domain of supervised learning, interpolation and extrapolation serve as crucial methodologies for predicting data points within and beyond the confines of a given dataset, respectively. The efficacy of these methods is closely linked to the nature of the dataset, with increased challenges when multivariate feature vectors are handled. This paper introduces a novel prediction framework that integrates interpolation and extrapolation techniques. Central to this method are two main innovations: an optimization model that effectively classifies new multivariate data points as either interior or exterior to the known dataset, and a hybrid prediction system that combines k-nearest neighbor (kNN) and linear regression. Tested on the port state control (PSC) inspection dataset at the port of Hong Kong, our framework generally demonstrates superior precision in predictive outcomes than traditional kNN and linear regression models. This research enriches the literature by illustrating the enhanced capability of combining interpolation and extrapolation techniques in supervised learning.

Measuring prairie snow water equivalent with combined UAV-borne gamma spectrometry and lidar

Abstract. Despite decades of effort, there remains an inability to measure snow water equivalent (SWE) at high spatial resolutions using remote sensing. Passive gamma ray spectrometry is one of the only well-established methods to reliably remotely sense SWE, but airborne applications to date have been limited to observing kilometre-scale areal averages. Noting the increasing capabilities of unoccupied aerial vehicles (UAVs) and miniaturization of passive gamma ray spectrometers, this study tested the ability of a UAV-borne gamma spectrometer and concomitant UAV-borne lidar to quantify the spatial variability of SWE at high spatial resolutions. Gamma and lidar observations from a UAV (UAV-gamma and UAV-lidar) were collected over two seasons from shallow, wind-blown, prairie snowpacks in Saskatchewan, Canada, with validation data collected from manual snow depth and density observations. A fine-resolution (0.25 m) reference dataset of SWE, to test UAV-gamma methods, was developed from UAV-lidar snow depth and snow survey snow density observations. The ability of UAV-gamma to resolve the areal average and spatial variability of SWE was promising with appropriate flight characteristics. Survey flights flown at a velocity of 5 m s−1, altitude of 15 m, and line spacing of 15 m were unable to capture the average or spatial variability of SWE within the uncertainty of the reference dataset. Slower, lower, and denser flight lines at a velocity of 4 m s−1, altitude of 8 m, and line spacing of 8 m were able to successfully observe areal average SWE and its variability at spatial resolutions greater than 22.5 m. Using a combination of UAV-based gamma SWE and UAV-based lidar snow depth improved the spatial representation of SWE substantially and permitted estimation of SWE at a spatial resolution 0.25 m with a ± 14.3 mm error relative to the reference SWE dataset. UAV-borne gamma spectrometry to estimate SWE is a promising and novel technique that has the potential to improve the measurement of shallow prairie snowpacks, and when combined with UAV-borne lidar snow depths, can provide fine-resolution, high-accuracy estimates of prairie SWE. Research on optimal hardware, data processing, and interpolation techniques is called for to further improve this remote sensing product and explore its application in other environments.

Evaluation and comparison of infiltration models for estimating infiltration capacity of different textures of irrigated soils

Accurate estimation of infiltration rates is crucial for effective irrigation system design and evaluation by optimizing irrigation scheduling, preventing soil erosion, and enhancing water use efficiency. This study evaluates and compares selected infiltration models for estimating water infiltration rates in the Shillanat-iv irrigation scheme in northern Ethiopia. Soil samples were collected to determine textural classes using hydrometer soil texture analysis and the United States Department of Agriculture (USDA) textural triangle. The soil textural map of the study was created using the inverse distance weight interpolation technique in ArcGIS version 10.4. Infiltration rates were measured using the double-ring infiltrometer for five soil textures: clay loam, loam, sandy clay loam, clay, and sandy loam. Six infiltration models (Kostiakov, Modified Kostiakov, Revised Modified Kostiakov, Philip, Horton, and Novel) were employed and evaluated using statistical parameters. Model calibration and validation were conducted using data from 38 points within the study area. The parameter values of the infiltration models were optimized using SPSS statistical software using least-squares errors. The results showed that, Revised Modified Kostiakov, Modified Kostiakov, and Novel infiltration models demonstrated superior capability in estimating infiltration rates for clay loam, loam, and sandy loam soil textures, respectively. Horton's model outperformed other models in estimating infiltration rates for both sandy clay loam and clay soil textures. The appropriately fitted infiltration models can be utilized in designing the irrigation system to estimate the infiltration rate of soil textures within the selected irrigation scheme and at similar sites with comparable soil textures.

Automated high-precision alignment of geologic maps with well orientation

The 3D reflection seismic interpretation entails the creation of structure maps of subsurface horizons across extensive areas, often spanning tens of thousands of square kilometers within sedimentary basins. To be correlated with well measurements, maps made from 3D reflection seismic are routinely assigned the correct depth at well control points. However, integrating orientation information measured from wells remains a challenge due to the absence of upscaling guidelines. We address the scale-dependent nature of geologic structure and well orientation information in subsurface mapping, particularly focusing on integrating kilometer-scale seismic interpretation with centimeter-scale well-measured dip and azimuth data. Our pioneering methodology streamlines this integration process through three essential steps: first, transforming well dip and azimuth into a gradient representation; second, smoothly extending gradient differences between the map and well within a user-defined radius of influence; and third, constructing an updated horizon map by bicubic spline interpolation to ensure automated and high-precision alignment. In addition, a structure-oriented interpolation technique is introduced to preserve faults and local structures during orientation correction. Application of the methodology to a 3D reflection seismic horizon demonstrates its effectiveness in automating the complex task of tying subsurface maps to well orientation information. This not only reduces the need for manual interventions but also introduces a new perspective on subsurface mapping. It provides a robust framework that enhances the accuracy and reliability of geologic interpretation, offering significant advancements beyond previous efforts in the literature.

The Role of Spatial Distribution of Geotechnical Soil Parameters in Site Investigation

Abstract The correct investigation of the foundation soil is essential for the optimal and efficient design of any structure. The interaction of a structure with the foundation soil can only be evaluated through the physical and mechanical characteristics of the intercepted geotechnical layers on the entire zone of influence. For this reason, the role of technical documentation that includes and summarizes field investigations and laboratory tests is particularly important. An important and sometimes complicated component in providing useful design information is the division into geotechnical or computational layers. This can be done at different levels, starting from the physical characteristics such as color, grain size distribution, plasticity and consistency and can continue with the evaluation of the mechanical characteristics of compressibility and shear strength. The aim of the paper is to create a graphical representation of the geotechnical parameters using a spatial interpolation technique (Kriging method). The creation of 2D maps using SURFER software assists geotechnical engineers in the correct interpretation of the geotechnical parameters. This interpolation technique for division into layers is also useful in quarries and borrows pits, when soil is used as construction material.

An Effective Soil Analysis and Crop Yield Prediction Based on Optimised Light GBM in Smart Agriculture

ABSTRACTIn the agricultural sector, crop yield prediction plays an important role as it helps farmers make decisions about the growing season and type of crops to get better yield. The main goal in the agricultural sector is to reduce operating costs and pollution by improving crop yields and quality. This paper proposes an effective method for soil analysis and crop yield prediction for intelligent agriculture. The collected data are preprocessed using missing value interpolation and data normalisation techniques. Feature selection is performed on the preprocessed data using the Aquila‐based adaptive optimisation algorithm, which selects the best trait subset for yield prediction. An improved lightweight gradient‐boosting machine based on the Battle Royale Optimisation technique is used for classification. The performance of the proposed system is evaluated using mean absolute error, root mean square error, R‐squared, mean square error, mean square logarithmic error and mean absolute percentage error, and the proposed system achieved an accuracy of 97%. The proposed system accurately predicts crop yields, improving crop production and quality.

Approximate GCD of several multivariate sparse polynomials based on SLRA interpolation

To compute the greatest common divisor (GCD) of a set of multivariate polynomials, modular algorithms are typically employed to prevent any growth in the coefficient polynomials in the intermediate expressions. However, when dealing with multivariate polynomials with a priori errors on their coefficients, using such modular algorithms becomes challenging. This is because any resulting approximate GCD computed in one variable may have perturbations depending on the evaluation point and may not be an image of the same desired multivariate approximate GCD. This necessitates computing it as given multivariate polynomials, and operating with large matrices whose size is exponential in the number of variables. In this paper, we present a new modular algorithm, suitable for dense cases and effective for sparse ones, called “SLRA interpolation”. This algorithm uses the multidimensional fast Fourier transform (FFT) and the structured low-rank approximation (SLRA) of non-square block diagonal matrices. The SLRA interpolation technique may reduce the time-complexity for one iteration in the computation of approximate GCD of several multivariate polynomials, especially for the sparse case.

A comprehensive study of source apportionment, spatial distribution, and health risks assessment of heavy metal(loid)s in the surface soils of a semi-arid mining region in Matehuala, Mexico

BackgroundThis study investigates the contamination level, spatial distribution, pollution sources, potential ecological risks, and human health risks associated with heavy metal(loid)s (i.e., arsenic (As), copper (Cu), iron (Fe), manganese (Mn), lead (Pb), and zinc (Zn)) in surface soils within the mining region of Matehuala, located in central Mexico. ObjectivesThe primary objectives are to estimate the contamination level of heavy metal(loid)s, identify pollution sources, assess potential ecological risks, and evaluate human health risks associated with heavy metal(loid) contamination. MethodsSoil samples from the study area were analysed using various indices including Igeo, Cf, PLI, mCd, EF, and PERI to evaluate contamination levels. Source apportionment of heavy metal(loid)s was conducted using the APCS-MLR and PMF receptor models. Spatial distribution patterns were determined using the most efficient interpolation technique among five different approaches. The total carcinogenic risk index (TCR) and total non-carcinogenic index (THI) were used in this study to assess the potential carcinogenic and non-carcinogenic hazards posed by heavy metal(loid)s in surface soil to human health. ResultsThe study reveals a high contamination level of heavy metal(loid)s in the surface soil, posing considerable ecological risks. As was identified as a priority metal for regulatory control measures. Mining and smelting activities were identified as the primary factors influencing heavy metal(loid) distributions. Based on spatial distribution mapping, concentrations were higher in the northern, western, and central regions of the study area. As and Fe were found to pose considerable and moderate ecological risks, respectively. Health risk evaluation indicated significant levels of carcinogenic risks for both adults and children, with higher risks for children. ConclusionThis study highlights the urgent need for monitoring heavy metal(loid) contamination in Matehuala's soils, particularly in regions experiencing strong economic growth, to mitigate potential human health and ecological risks associated with heavy metal(loid) pollution.

An R-based integrated method for producing river bathymetry and cross-sections from recreational-grade sonar sensor data

Water bodies’ bathymetry is a crucial information for understanding and sustainably managing water resources. Bathymetric surveys can be expensive due to sonar equipment cost, but low-cost alternatives options exist. We present a methodology that standardize the bathymetric data collection and processing of recreational-grade sonar data. The sonar data postprocessing if fully implemented in R, with ready to use functions able to produce bathymetric maps or extract river cross sections’ metrics with minimal computing efforts. The method robustly produces a variety of outputs; the performance of the equipment adopted and of the interpolation technique allow for high accuracy and low-cost bathymetric reconstruction.•The method implemented allow for a robust and consistent processing of recreational-grade sonar water depth measures.•Through R-based functions the data are postprocessed to obtain bathymetry maps also for complex shape waterbodies.•Further metrics of rivers/channel cross sections can be extracted.

Global well-posedness of three-dimensional magneto-micropolar fluid equations with partial dissipation

In this paper, the three-dimensional magneto-micropolar fluid equations with partial dissipation is studied on R 3 . The main purpose of this paper is to establish global solutions with a small initial value. More specifically, we demonstrate the global stability of the perturbation solutions near the background magnetic field. The proof is mainly based on the energy estimate, the tricky interpolation techniques and bootstrapping argument.

Interpolation insights: Accurate mass attenuation coefficient and effective atomic number estimation in PbO and BaO doped borosilicate glasses

Interpolation techniques such as Piecewise Cubic Hermite Interpolating Polynomials (PCHIP) and Cubic Splines (CS) were used to estimate the mass attenuation coefficient values (MAC) and the effective atomic number values (Zeff) of borosilicate glasses doped with PbO and BaO. The mathematical models were used to obtain a function between the parameters and the energy of the incoming photons, to provide a better understanding of the glass system. The glasses had a composition of 10Na2O–15PbO-(65-x)B2O3–10SiO2-xBaO, where x = 5, 10, 15, 20, and 25 mol%. The derived values were compared against theoretical data obtained from Phy-X, to determine the accuracy of the mathematical models. The results demonstrate that both the CS and PCHIP methods are useful tools that can accurately be used to determine the MAC and Zeff values of the investigated samples. For instance, at certain energies such as 0.103 MeV, the relative difference between the MAC values obtained through PCHIP and through Phy-X is as low as 0.07, demonstrating the effectiveness of the PCHIP model at these energies. The same is true for the Zeff values, where the same relative difference at 0.384 MeV is as low as 0.0002. Therefore, both the mathematical models demonstrate an effective ability at interpolating the MAC and Zeff values of the investigated glass system.

Model order reduction by convex displacement interpolation

We present a nonlinear interpolation technique for parametric fields that exploits optimal transportation of coherent structures of the solution to achieve accurate performance. The approach generalizes the nonlinear interpolation procedure introduced in [Iollo, Taddei, J. Comput. Phys., 2022] to multi-dimensional parameter domains and to datasets of several snapshots. Given a library of high-fidelity simulations, we rely on a scalar testing function and on a point set registration method to identify coherent structures of the solution field in the form of sorted point clouds. Given a new parameter value, we exploit a regression method to predict the new point cloud; then, we resort to a boundary-aware registration technique to define bijective mappings that deform the new point cloud into the point clouds of the neighboring elements of the dataset, while preserving the boundary of the domain; finally, we define the estimate as a weighted combination of modes obtained by composing the neighboring snapshots with the previously-built mappings. We present several numerical examples for compressible and incompressible, viscous and inviscid flows to demonstrate the accuracy of the method. Furthermore, we employ the nonlinear interpolation procedure to augment the dataset of simulations for linear-subspace projection-based model reduction: our data augmentation procedure is designed to reduce offline costs — which are dominated by snapshot generation — of model reduction techniques for nonlinear advection-dominated problems.

Dengue dynamics in Nepal: A Caputo fractional model with optimal control strategies

An infectious disease called dengue is a significant health concern nowadays. The dengue outbreak occurred with a single serotype all over Nepal in 2023. In the tropical and subtropical regions, dengue fever is a leading cause of sickness and death. Currently, there is no specified treatment for dengue fever. Avoiding mosquito bites is strongly advised to reduce the likelihood of controlling this disease. In underdeveloped countries like Nepal, the implementation of appropriate control measures is the most important factor in preventing and controlling the spread of dengue illness. The Caputo fractional dengue model with optimum control variables, including mosquito repellent and insecticide use, investigates the impact of alternative control strategies to minimize dengue prevalence. Using the fixed point theorem, the existence and uniqueness of a solution will be demonstrated for the problem. Ulam-Hyers stability, disease-free equilibrium point stability, and basic reproduction number are studied for the proposed model. The model is simulated using a two-step Lagrange interpolation technique, and the least squares method is used to estimate parameter values using real monthly infected data. We then analyze the sensitivity analysis to determine influencing parameters and the control measure effects on the basic reproduction number. The Pontryagin Maximum Principle is used to determine the optimal control variable in the dengue model for control strategies. The present study suggests that the deployment of control measures is extremely successful in lowering infectious disease incidences. Which facilitates the decision-makers to practice rigorous evaluation of such an epidemiological scenario while implementing appropriate control measures to prevent dengue disease transmission in Nepal.

An enhanced stability evaluation system for entry-type excavations: Utilizing a hybrid bagging-SVM model, GP and kriging techniques

In underground mining, especially in entry-type excavations, the instability of surrounding rock structures can lead to incalculable losses. As a crucial tool for stability analysis in entry-type excavations, the critical span graph must be updated to meet more stringent engineering requirements. Given this, this study introduces the support vector machine (SVM), along with multiple ensemble (bagging, adaptive boosting, and stacking) and optimization (Harris hawks optimization (HHO), cuckoo search (CS)) techniques, to overcome the limitations of the traditional methods. The analysis indicates that the hybrid model combining SVM, bagging, and CS strategies has a good prediction performance, and its test accuracy reaches 0.86. Furthermore, the partition scheme of the critical span graph is adjusted based on the CS-BSVM model and 399 cases. Compared with previous empirical or semi-empirical methods, the new model overcomes the interference of subjective factors and possesses higher interpretability. Since relying solely on one technology cannot ensure prediction credibility, this study further introduces genetic programming (GP) and kriging interpolation techniques. The explicit expressions derived through GP can offer the stability probability value, and the kriging technique can provide interpolated definitions for two new subclasses. Finally, a prediction platform is developed based on the above three approaches, which can rapidly provide engineering feedback.

Mapping benthic sediment types and composition in a turbid Jamaican bay using hydroacoustic data and different spatially explicit interpolation techniques

Hydroacoustic sensors are better suited for mapping shallow turbid coastal waters, but surveys must take place along transects, necessitating the interpolation of processed, large, spatially dependent hydroacoustic datasets to produce continuous maps. The aim of this study was to evaluate the performance of interpolation techniques that can handle data obtained from a hydroacoustic survey (covering 23 km2) conducted in 2016 in a shallow bay in southwestern Jamaica. Independent data or a 5-fold cross validation were used to compare the accuracies of continuous maps of benthic sediment percentage composition and submerged aquatic vegetation cover (SAV), interpolated from the survey data using random forest (RF) regression (RFr), RFr kriging (RFrk) and Ensemble Machine Learning (EML) with rotated/oblique coordinates. Categorical/thematic maps of sediment types interpolated using RF (RFc) and EML classifiers and a spatial Markov chain model with indicator kriging predictor/simulation (Mc-IK) were also compared. RFr, RFc, RFrk and EML covariates included coordinates and auxiliary data. The following maps were more accurate than corresponding interpolated maps: EML - mud, sand, silt, clay and SAV; RFr - loss on ignition [LOI]; RFrk - calcium carbonate; Mc-IK – sediment types with 3 and 4 classes. The Mc-IK maps were also the most suitable maps for assessing spatial and quantitative changes. The predominant substrate type in the bay was muddy sand covering 15.7 km2. The highest percentage composition of mud, silt, clay, and organic matter [LOI] was found close to the mouth of the Black River where sandy mud was found, indicating a high input of terrigenous sediment. Calcium carbonate composition showed an opposite spatial trend and was positively associated with higher SAV percentage cover. The bay is now part of a protected area, as such, these maps will be used as a baseline to gauge the impact of increased protection/management on benthic dynamics.

Spatial and Temporal Variations of Soil pH in Farmland in Xinjiang, China over the Past Decade

Soil pH is crucial for the quality of the farmland and crop growth. The objective of this study is to analyze the spatial and temporal variations of farmland soil pH in Xinjiang (XJ), and to provide a scientific basis for soil improvement and agricultural production optimization. Based on soil pH data from XJ farmland in two periods, 2008~2010 and 2019~2021, geostatistical methods and kriging spatial interpolation techniques were employed to analyze the spatiotemporal changes in soil pH and to investigate the relationship between relevant influencing factors and pH over more than a decade. The results indicate that the spatiotemporal distribution of soil pH in XJ farmlands is uneven. Between 2019 and 2021, the average soil pH was 8.11, generally being on the higher side, with a coefficient of variation (CV) of 3.85%, indicating minimal spatial variability. In the farmland soil of Southern Xinjiang (S-XJ), the average pH value was 8.17, significantly higher than that of Northern Xinjiang (N-XJ), which was 8.10, demonstrating a spatial pattern of “higher in the south, lower in the north”. Over the past decade, soil pH in XJ has significantly increased from 8.11 to 8.13, with a 0.05 unit increase in the north and a 0.01 unit increase in the south (p < 0.05). Regionally, Altay saw the largest increase of 0.22 units, while Bortala Mongol Autonomous Prefecture (Bortala) experienced the most significant decrease, dropping by 0.59 units. Furthermore, this study found that factors such as topography, nutrients, and irrigation methods all have certain influences on the spatial distribution of soil pH in XJ farmland, while variations in climate factors and fertilization levels may affect its long-term temporal changes. These research findings will provide new insights for adjusting and updating agricultural management measures related to soil pH regulation in XJ.

A new quadrant constellation graph-based water quality index and map

Water quality (WQ) evaluation is very important issue in human health and environment management. Many WQ evaluation (WQE) methods based on statistical methods and multi-criteria decision making (MCDM) methods have been developed and used. The common drawback is that they lack in visualization/intuitiveness of the WQE results. They evaluate the WQ level only quantitatively. To intuitively represent the WQ level, the previous WQE methods have applied some additional graphical methods such as concentration/distribution/contour maps combined with complicated interpolation techniques based on geographic information system. The objective of this research is to develop a new effective approach to evaluate the WQ level not only quantitatively but also intuitively without requiring additional complicated graphical technique. To do this, we proposed a new quadrant constellation graph-based water quality index (QCG-WQI) and map (QCG-WQM). The QCG-WQI evaluates the WQ level quantitatively, and QCG-WQM evaluates the WQ level intuitively. To demonstrate the effectiveness, three illustration cases were considered and compared with the results from some well-known MCDM methods. The results demonstrate that the new approach is very effective intuitive and quantitative WQE tool. It could be widely applied to not only WQE and environmental evaluation but also MCDM problems in many physical research works.

Geospatial Analysis of Soil Fertility in Muzaffarpur District, Bihar, India: Integrating GPS and GIS Technologies

The soil fertility maps generated using Global Positioning System (GPS) and Geographic Information Systems (GIS) serve as crucial tools for effective nutrient management decision-making. However, soil fertility data for the Minapur, Kanti, and Marwan blocks of Muzaffarpur district, Bihar, India was found to be insufficient. Therefore, a soil fertility inventory research was conducted in these three blocks to create thematic soil fertility maps. A total of 40 geo-referenced composite soil samples were collected from various locations within the study area using a handheld GPS device. The processed soil samples were analyzed for various soil fertility parameters using standard methods. Soil nutrient status and fertility maps were then created using ArcGIS software with Inverse Distance Weighted (IDW) interpolation techniques. The results clearly indicated that the soil reaction was alkaline, with a pH value exceeding 7.5. The content of soil organic matter, potassium, and sulphur was found to be low to medium, while available nitrogen and phosphorus levels were recorded as very low in these blocks. Finally it is concluded that the study generated thematic soil fertility maps for Minapur, Kanti, and Marwan blocks of Muzaffarpur district, Bihar, revealing alkaline soil with low to medium organic matter, potassium, and sulphur and very low nitrogen and phosphorus levels.