Geostatistical Techniques Research Articles

Air Quality of Bursa: Temporal and Spatial Evaluation of PM10, PM2.5, NO2 and SO2 Pollutants Using IDW Geostatistical Technique

One of the most significant environmental issues is air pollution. Human health is negatively impacted by this issue in addition to the environment. In this study, spatial and temporal analysis of particulate matter 10 (PM10), particulate matter 2.5 (PM2.5), sulfur dioxide (SO2) and nitrogen dioxide (NO2) pollutants for 2022 was conducted within the provincial borders of Bursa, Turkey. The monthly and seasonal air pollution maps have been generated using 24-hour measurements obtained from air quality monitoring stations within the city. Data for PM10, PM2.5, SO2, and NO2 were utilized in the creation of these maps. The pollution maps were analyzed based on the criteria outlined in the World Health Organization (WHO) report, as well as the limit values set by the European Union and the national regulations. According to the results obtained, the amount of pollutants in summer is lower than in winter. While PM10 was on average 39.32 μg/m³ in the summer months, it exceeded the national legislation by 3.09% in the winter. When evaluated during the average summer season, the concentrations of PM2.5 and SO2 were observed to be 19.29 μg/m³ and 5.82 μg/m³, respectively. In contrast, during the winter season, the concentrations were found to be 47.30 μg/m³ for PM2.5 and 11.07 μg/m³ for SO2. There is no legal limit for PM2.5 in national legislation. SO2 was found to be below the specified legal regulations. While NO2 was on average 25.13 μg/m³ in the summer months, it exceeded the national legislation by 9.74% in the winter. The main sources of pollutants in the region were examined and according to the findings, factors such as the city being an industrial zone and high traffic density increased the pollutants. The results of this investigation show that air pollution poses a significant issue in Bursa.

Groundwater quality mapping based on the Wilcox Classification method for agricultural purposes: Qazvin Plain aquifer case

Water quality is an essential component in managing surface and groundwater resources and for various uses; it is considered a necessary principle in planning. This study aims to map the groundwater quality of the Qazvin Plain aquifer in Iran for agricultural use based on the Wilcox classification method. For this purpose, the parameters of electrical conductivity (EC) and sodium adsorption ratio (SAR) of wells in the years 2015-2018 have been used. For interpolation, the inverse distance weighting (IDW) method with optimal power and Kriging geostatistical techniques were used based on spherical, exponential, and Gaussian semicircle algorithms. Electrical conductivity and SAR maps were drawn in the GIS platform after selecting the best interpolation method due to minor errors. The Wilcox method was used to classify the water quality of the studied wells. In most cases, the IDW method with optimal power was selected as the superior interpolation method. During the study, the results obtained from the water quality maps showed that the level attributed to the "high salinity" water class increased from 25.98 to 36.44, and the level attributed to the "slightly salty" water class decreased from 12.54 to 3.14. Finally, the results showed that the quality of underground water for agricultural purpose in the Qazvin Plain aquifer became more unfavourable during the studied period

A comparative study of interpolation methods for the development of ore distribution maps

Mining projects require precise knowledge about tonnage and quality of ore reserves for planning and decision-making. This is hard to establish as exploration operations, which are costly, time-consuming and require an accurate description of the deposit. Geologists use deterministic and geostatistical methods as interpolation tools to generate a continuous (or prediction) ore distribution map from known sampled data. A comparative study between four different geostatistical interpolation techniques: Ordinary Kriging (OK), Simple Kriging (SK), Universal Kriging (UK) and Empirical Bayesian Kriging (EBK); and four deterministic techniques: Inverse Distance Weighting (IDW), Global Polynomial Interpolation (GPI), Radial Basis Function (RBF), and Local Polynomial Interpolation (LPI) were applied. The 95% confidence level was measured and the effectiveness of each interpolation method was assessed through cross-validation for the construction of the corresponding maps displaying the distribution of ore on the surface with the use of GIS. The output methods are ranked to perform a comprehensive analysis using the error statistics methods: Mean Error (ME), Root Mean Square Error (RMSE), Mean Standardized Error (MSE) and Root Mean Square Standardized Error (RMSSE). The results show that GPI, EBK and Kriging methods perform the best results while IDW has good statistical analysis factors but it came in the tail of the rank. Therefore, there is no appropriate interpolation method accurate for all cases, each method must be statistically evaluated before each application and essentially based on real data.

Modeling Groundwater Resources in Data-Scarce Regions for Sustainable Management: Methodologies and Limits

Groundwater modeling in data-scarce regions faces significant challenges due to the lack of comprehensive, high-quality data, impacting model accuracy. This systematic review of Scopus-indexed papers identifies various approaches to address these challenges, including coupled hydrological-groundwater models, machine learning techniques, distributed hydrological models, water balance models, 3D groundwater flow modeling, geostatistical techniques, remote sensing-based approaches, isotope-based methods, global model downscaling, and integrated modeling approaches. Each methodology offers unique advantages for groundwater assessment and management in data-poor environments, often combining multiple data sources and modeling techniques to overcome limitations. However, these approaches face common challenges related to data quality, scale transferability, and the representation of complex hydrogeological processes. This review emphasizes the importance of adapting methodologies to specific regional contexts and data availability. It underscores the value of combining multiple data sources and modeling techniques to provide robust estimates for sustainable groundwater management. The choice of method ultimately depends on the specific objectives, scale of the study, and available data in the region of interest. Future research should focus on improving the integration of diverse data sources, enhancing the representation of complex hydrogeological processes in simplified models, and developing robust uncertainty quantification methods tailored for data-scarce conditions.

Assessing microbial risks of Escherichia coli: A spatial and temporal study of virulence and resistance genes in surface water in resource-limited regions.

Environmental microbial pollution can potentially cause illnesses and deaths among human populations. Environmental factors such as geomorphology, land use, human activities, and seasonality could influence pathogen dissemination. This study performed a space-temporal analysis of Escherichia coli concentrations, virulence (eaeA, bfpA, stx2, aatA, and st), and resistance (tetA, blaTEM, blaOXA, Intl-1, Intl-2, sul-1, and sul-2) genes in surface water within the Virilla sub-watershed, Costa Rica. The research employs microbiological, geostatistical, and quantitative microbial risk assessment (QMRA) techniques to evaluate the health risks associated with diarrheagenic E. coli. Samples collected monthly over a year were analyzed for fecal coliforms, E. coli, Enterococcus faecalis, physicochemical parameters, and the presence of virulence and resistance genes. The geographically weighted regression model identified significant associations between the virulence and resistance genes presence and environmental factors influence such as land use, slope, altitude, and seasonal changes. The results show that urban and grazing land use significantly influence the presence of resistance genes, while human fecal pollution is associated with higher concentrations of virulence genes. Seasonal variations showed that wet seasons contributed to increased gene presence (number of positive samples) and diversity (variety of virulence and resistance genes) compared with dry season. The QMRA results indicated that the total health disease burden was higher than the benchmark for drinking water recommended by WHO (1.00×10-6 DALY - Disability-Adjusted Life Years - per person per year) by between one and four magnitudes orders, particularly in regions with urban and grazing land use, human fecal pollution class, and lower altitudes. This study highlights the relevance of integrating environmental and microbial data to enhance water quality management and public health strategies in resource-limited regions. It underscores the need for targeted interventions, such as improved wastewater treatment and establishing buffer zones, to mitigate the risks associated with microbial pollution in surface waters.

CHARACTERIZATION OF ISOBATHS IN THE CENOMANIAN RESERVOIR OF THE MUANDA FIELD – IMPLICATIONS FOR OPTIMAL OIL RESOURCE MANAGEMENT

The study of isobaths characterization in the Cenomanian reservoir of the Muanda field focuses on optimizing petroleum resource management. Using geostatistical techniques and numerical analysis methods, the isobaths of the K, J, I and H layers were mapped. The K layer, the most superficial, is 44 meters thick, while the J and I layers reach 48 meters respectively. This research highlights the spatial variability of isobaths, a key factor in understanding the rapid depletion of the reservoir, which is mainly attributed to the presence of argillites. The lithological heterogeneity, characterized by high clay content, influences reservoir permeability, complicating fluid flow and, consequently, well productivity. The study also subdivided the reservoir into distinct zones according to clay content, revealing that the southwestern zone has a clay content of over 57.42%, while the southeastern zone is less clayey, with a content of less than 41.87%. The results underline the importance of a geostatistical approach to resource management, identifying target areas for potential interventions such as hydraulic fracturing. This study thus contributes to a better understanding of reservoir dynamics and to the formulation of appropriate strategies for the sustainable exploitation of petroleum resources in the Muanda region.

Analyses and assessment of soil salinity modeling: review of papers from Scopus database

Soil is part and parcel of operating essential functions of ecosystems. Its degradation including salinization may affect negatively both agriculture and human life. Soil salinization is a form of environmental destruction resulting in loss of soil quality, especially in dry and semi-arid zones including both primary and secondary salinization. Today, salt-affected areas cover 7% of the Earth’s surface with hotspots in the United States, Argentina, Pakistan, China, Sudan, and many states in Central and Western Asia existing in a minimum of 100 countries. Timely and effective management is required to preserve soil fertility for the next generation. Currently, modeling is an effective approach to identify, evaluate and predict soil salinization distribution. Modeling with remote sensing techniques is widely used to identify salt-affected soil, map saline locations, and estimate salinity levels. Today, a lot of geostatistical techniques are applied to predict soil salinity over time including Ordinary Kriging, Co-kriging and Indicator Kriging. The current study originated as a review with the purpose of exploring the scientific background of soil salinity modeling throughout the world. Our research, which includes 177 Scopus-indexed final published articles in English, focuses at publishing trends, types, top publishers, notable authors, affiliated institutions, geographical and thematic emphasis areas, co-authorship and keyword occurrences. Additionally, this paper provides a review of the most popular models and conditioning factors. Among various models, multiple linear regression was used as the most prevalent model type for soil salinity estimation in 10.1% of the publications, and electrical conductivity is considered as the most value conditioning factor in 19.2% of analyzed papers. Finally, it is concluded that recent technological developments in remote sensing are significant factors in increasing research interest in soil salinity modeling.

Analyzing the impact of variations in land use and elevation on selected soil microbial indices and spatial distribution.

Soil biological characteristics are highly sensitive to land use changes, making them valuable indicators of soil quality. This study assesses the effects of three land use types (agriculture, rangeland, and forest) and elevation variations on soil microbial parameters and their spatial distribution in the Khaneghah region. Standard physicochemical and biological properties of the soil were measured on a total of 72 soil samples collected using systematic and random sampling techniques. Spatial distribution maps of the biological indices were generated using geostatistical techniques, specifically the Kriging method, within a geographic information system (GIS). The results revealed significantly higher values for microbial biomass carbon (MBC = 900 mg Cmic-CO2 kg-1), nitrogen (MBN = 8.97 mg Nmic kg-1), basal respiration (BR = 25.1 mg C-CO2 g-1 day-1), and the total microbial population (MPN = 0.63 × 109 cells g-1) in forest soils compared to rangeland and agricultural soils. The alignment between land use maps and biological index maps reinforced these findings. Although the correlations between biological indices and physicochemical properties were generally weak (positive or negative), organic matter content, field capacity moisture, and silt percentage exhibited a slight positive correlation with most of the microbial indices evaluated. The comparison of soil microbial indices with the digital elevation model map indicated higher levels of MBC, MBN, BR, and MPN at elevated regions. However, the microbial quotient and metabolic quotient (qCO₂) did not show significant changes with increasing elevation. The study also confirmed the effectiveness of Kriging interpolation in mapping specific soil microbial indices, as the correlation between Kriging estimates and measured values at sampling points exceeded 0.2, demonstrating statistical significance at a 5% confidence level.

Fast mixture spatial regression: A mixture in the geographical and feature space applied to predict porosity in the post-salt

Extracting geological resources like hydrocarbon fluids requires significant investments and precise decision-making processes. To optimize the efficiency of the extraction process, researchers and industry experts have explored innovative methodologies, including the prediction of optimal drilling locations. Porosity, a key attribute of reservoir rocks, plays a crucial role in determining fluid storage capacity. Geostatistical techniques, such as kriging, have been widely used for estimating porosity by capturing spatial dependence in sampled point-referenced data. However, the reliance on geographical coordinates for determining spatial distances may present challenges in scenarios with small and widely separated samples. In this paper, we develop a mixture model that combines the covariance generated by geographical space and the covariance generated in an appropriate feature space to enhance estimation accuracy. Developed within the Bayesian framework, our approach utilizes flexible Markov Chain Monte Carlo (MCMC) methods and leverages the Nearest-Neighbor Gaussian Process (NNGP) strategy for scalability. We present a controlled empirical comparison, considering various data generation configurations, to assess the performance of the mixture model in comparison to the marginal models. Applying our models to a three-dimensional reservoir demonstrates its practical applicability and scalability. This research presents a novel approach for improved porosity estimation by integrating spatial and covariate information, offering the potential for optimizing reservoir exploration and extraction activities.

Identifying hidden factors influencing soil Olsen-P in an alkaline calcareous soil using machine learning and geostatistical techniques

Phosphorus (P) deficiency is one of the major constraints for sustainable crop production in calcareous soils. This study aimed to elucidate the key soil characteristics modulating the variability of soil Olsen P in these typical soils. A comprehensive soil sampling initiative (1.5 samples per hectare) was conducted on a 100-ha farm, considering 31 attributes that included soil physical and chemical properties, and geographic attributes. Three machine learning algorithms—partial least squares regression (PLSR), random forest (RF), and cubist regression (CR)—were employed to understand key variables controlling soil Olsen P. Furthermore, the same data set was used to spatially map the variations in Olsen P levels using ordinary kriging. The results revealed that soil chemical factors, specifically exchangeable manganese and zinc, cation exchange capacity, and carbonate, played a crucial role in controlling P levels. Among the machine learning models, the best performing model was RF (R2 = 0.95, RMSE = 1.30 mg kg−1) followed by CR (R2 = 0.92 and RMSE = 1.43 mg kg−1). Additionally, the analysis using a Gaussian semi-variogram model showed a good performance (R2 = 0.78, RMSE = 2.05 m) in visualizing the spatial distribution of Olsen P, revealing its heterogeneity. The resulting pattern of Olsen P distribution may be attributed not only to soil properties but also to external factors, such as sediment transport through watercourses across the study area and atmospheric deposition from a nearby P mining site. Overall, the combination of geostatistical methods and machine learning approach demonstrates a significant potential in understanding the complexity of soil available P (Olsen-P) that could help to develop sustainable and precise P management.

Geostatistical Interpolation Approach for Improving Flood Simulation Within a Data‐Scarce Region in the Tibetan Plateau

ABSTRACTThe complex orography of the Tibetan plateau (TP) and the scarcity and uneven spatial distribution of meteorological stations present significant challenges in accurately estimating meteorological variables for hydrological simulations. This study aims to enhance the accuracy of daily precipitation and temperature interpolation for hydrological simulations in the Lhasa River Basin (LRB), particularly during flood events. We evaluate and compare the performance of deterministic Inverse Distance Weighting—IDW and geostatistical (Ordinary Kriging—OK and Kriging with External Drift—KED) interpolation methods for estimating precipitation and temperature patterns. Subsequently, we investigate the influence of different interpolation methods on hydrological simulations by using the interpolated meteorological data as input for the Water Balance Simulation Model (WaSiM) to simulate daily discharge in the LRB. Our results revealed that geostatistical methods, specifically OK and KED, are more effective in capturing the spatial variability and anisotropy inherent in precipitation patterns influenced by the Indian summer monsoons. In addition, the KED method effectively captured the daily variation of the temperature lapse rate, indicating the inadequacy of using a constant lapse rate for hydrological modelling in high‐elevation regions like the TP. The geostatistical technique outperformed the Deterministic method, with KED realising the best temperature and precipitation interpolation performance based on cross‐validation results. However, although KED provides superior results based on cross‐validation performance, applying its precipitation interpolation as input into WaSiM led to the poorest discharge simulation. The combination of OK for precipitation and KED for temperature produced the most accurate discharge simulations in the LRB, highlighting the importance of not solely relying on cross‐validation results but also considering the practical implications of interpolation methods on hydrological model outputs. Our study offers a robust framework for improving flood simulations and water resource management in a data‐scarce, high‐elevation region like the TP.

Optimization of underground mining production layouts considering geological uncertainty using deep reinforcement learning

Mineral extraction plays a key role in the global raw materials supply chain, however the exhaustion of shallow deposits and typical scarcity of sampled data during exploration activities creates challenges in mine planning and design, where decision-making is highly sensitive to uncertainty in geology and mineral grade prediction. Geostatistical techniques are commonly used to generate a set of equiprobable simulated numerical models to capture these uncertainties, however incorporating these simulated models within a mine planning and design framework remains a major challenge. The purpose of this paper is to propose a novel approach to decision-making in underground mine design that can use information from an ensemble of numerical realizations of a mineral resource to improve the financial performance of the asset. A deep reinforcement learning (DRL) framework, using the proximal policy optimization (PPO) algorithm, is developed for the design of underground mining production level layouts. A case study is presented using a gold mineral resource characterized by an ensemble of 100 numerical realizations to verify the advantages of the proposed method, considering a baseline consisting of an industry standard automated design method. The DRL approach achieved an 8.3% higher expected profit, a 3.4% more gold mined than the baseline, and has the added functionality of considering uncertainty in mineral grades.

A Spatial Analysis of Coffee Plant Temperature and Its Relationship with Water Potential and Stomatal Conductance Using a Thermal Camera Embedded in a Remotely Piloted Aircraft

Coffee is a key agricultural product in national and international markets. Physiological parameters, such as plant growth indicators, can signal interruptions in these processes. This study aimed to characterize the temperature obtained by a thermal camera embedded in a remotely piloted aircraft (RPA) and evaluate its relationship with the water potential (WP) and stomatal conductance (gs) of an experimental coffee plantation using geostatistical techniques. The experiment was conducted at the Federal University of Lavras, Minas Gerais, Brazil. A rotary-wing RPA with an embedded thermal camera flew autonomously at a height of 10 m and speed of 10 m/s. Images were collected on 26 November 2019 (rainy season), and 11 August 2020 (dry season), between 9:30 am and 11:30 am. Data on gs and WP were collected in the field. The thermal images were processed using FLIR Tools 5.13, and temperature analysis and spatialization were undertaken using geostatistical tools and isocolor maps by Kriging interpolation in R 4.3.2 software. Field data were superimposed on final crop temperature maps using QuantumGIS version 3.10 software. The study found that with decreasing WP, stomatal closure and reduction in gs occurred, increasing the temperature due to water deficit. The temperature distribution maps identified areas of climatic variations indicating water deficit.

High-resolution spatio-temporal risk mapping for malaria in Namibia: a comprehensive analysis

BackgroundNamibia, a low malaria transmission country targeting elimination, has made substantial progress in reducing malaria burden through improved case management, widespread indoor residual spraying and distribution of insecticidal nets. The country's diverse landscape includes regions with varying population densities and geographical niches, with the north of the country prone to periodic outbreaks. As Namibia approaches elimination, malaria transmission has clustered into distinct foci, the identification of which is essential for deployment of targeted interventions to attain the southern Africa Elimination Eight Initiative targets by 2030. Geospatial modelling provides an effective mechanism to identify these foci, synthesizing aggregate routinely collected case counts with gridded environmental covariates to downscale case data into high-resolution risk maps.MethodsThis study introduces innovative infectious disease mapping techniques to generate high-resolution spatio-temporal risk maps for malaria in Namibia. A two-stage approach is employed to create maps using statistical Bayesian modelling to combine environmental covariates, population data, and clinical malaria case counts gathered from the routine surveillance system between 2018 and 2021.ResultsA fine-scale spatial endemicity surface was produced for annual average incidence, followed by a spatio-temporal modelling of seasonal fluctuations in weekly incidence and aggregated further to district level. A seasonal profile was inferred across most districts of the country, where cases rose from late December/early January to a peak around early April and then declined rapidly to a low level from July to December. There was a high degree of spatial heterogeneity in incidence, with much higher rates observed in the northern part and some local epidemic occurrence in specific districts sporadically.ConclusionsWhile the study acknowledges certain limitations, such as population mobility and incomplete clinical case reporting, it underscores the importance of continuously refining geostatistical techniques to provide timely and accurate support for malaria elimination efforts. The high-resolution spatial risk maps presented in this study have been instrumental in guiding the Namibian Ministry of Health and Social Services in prioritizing and targeting malaria prevention efforts. This two-stage spatio-temporal approach offers a valuable tool for identifying hotspots and monitoring malaria risk patterns, ultimately contributing to the achievement of national and sub-national elimination goals.

Geostatistical Simulation of Daily Rainfall Fields—Performance Assessment for Extremes in West Africa

Abstract The spatial description of high-resolution extreme daily rainfall fields is challenging because of the high spatial and temporal variability of rainfall, particularly in tropical regions due to the stochastic nature of convective rainfall. Geostatistical simulations offer a solution to this problem. In this study, a stochastic geostatistical simulation technique based on the spectral turning bands method is presented for modeling daily rainfall extremes in the data-scarce tropical Ouémé River basin (Benin). This technique uses meta-Gaussian frameworks built on Gaussian random fields, which are transformed into realistic rainfall fields using statistical transfer functions. The simulation framework can be conditioned on point observations and is computationally efficient in generating multiple ensembles of extreme rainfall fields. The results of tests and evaluations for multiple extremes demonstrate the effectiveness of the simulation framework in modeling more realistic rainfall fields and capturing their variability. It successfully reproduces the empirical cumulative distribution function of the observation samples and outperforms classical interpolation techniques like ordinary kriging in terms of spatial continuity and rainfall variability. The study also addresses the challenge of dealing with uncertainty in data-poor areas and proposes a novel approach for determining the spatial correlation structure even with low station density, resulting in a performance boost of 9.5% compared to traditional techniques. Additionally, we present a low-skill reference simulation method to facilitate a comprehensive comparison of the geostatistical simulation approaches. The simulations generated have the potential to provide valuable inputs for hydrological modeling.

Quantitative assessment of interplay between urbanization dynamics and land surface temperature variations using generalized additive model coupled PDP for sustainable urban planning and management.

The mountainous region of Asir is experiencing rapid and unsystematic urbanization leading to an increase in land surface temperatures (LST), which poses a challenge to human well-being and ecological balance. Therefore, it is necessary to study the interaction between land use and land cover (LULC)-induced urbanization and LST using advanced geostatistical techniques. In addition, understanding the urbanization process and urban density is essential for effective urban planning and management. The aim of this study was to investigate the interaction between the urbanization process, urban density and the associated LST. Using the Random Forest Algorithm, LULC mapping was conducted for the years 1990, 2000 and 2020. Metrics such as land cover change rate (LCCR), land cover index (LCI), landscape expansion index (LEI), mean landscape expansion index (MLEI) and area-weighted landscape expansion index (AWLEI) were used to understand urbanization processes and LULC changes. Convolutional kernels were used to model urban density, and the mono-window algorithm was applied to analyse LST in the selected years. In addition, the study assessed the Surface Urban Heat Island (SUHI) contribution index to LULC and used Generalized Additive Models (GAMs) in conjunction with Partial Dependence Plots (PDPs) to understand the relationship between urbanization processes, urban density and LST. In a detailed 30-year study, the application of the RF algorithm showed significant shifts in LULC with an overall validation accuracy of over 85%. Urban areas grew dramatically from 69.40 km2 in 1990 to 338.74 km2 in 2020, while water areas decreased from 1.51 to 0.54 km2. Dense vegetation increased from 43.36 to 52.22 km2, indicating positive ecological trends. The LST analysis showed a general warming, with the mean LST increasing from 40.51°C in 1990 to 46.73°C in 2020 and the highest temperature category (50-60°C) increasing from 0.78 to 33.35 km2. The built-up area of cities tripled between 1990 and 2020, with the Landscape Expansion Index reflecting significant growth in suburban areas. The modeling of urban density shows increasing urbanization in the centre, which will expand significantly to the east by 2020. The contribution of LULC to LST and the Urban Heat Island (SUHI) effect was evident, with built-up areas showing a constant temperature increase. GAMs confirmed a statistically significant relationship between urban density and LST, with different effects for different types of urban expansion. This comprehensive study quantitatively sheds light on the complicated dynamics of urbanization, land cover change and temperature variation and provides important insights for sustainable urban development.

Combined mechanistic and machine learning method for construction of oil reservoir permeability map consistent with well test measurements

We introduce a novel method for estimating the spatial distribution of absolute permeability in oil reservoirs, consistent with well logging and well test measurements. The primary objective is to create a permeability map, incorporating the well test interpretation results and achieving hydrodynamic similarity to the actual permeability distribution around each well. This enhancement aims to improve the accuracy of reservoir modeling outcomes in reproducing real data. We utilize Nadaraya-Watson kernel regression to parameterize the two-dimensional spatial distribution of rock permeability. The kernel regression parameters are optimized by minimizing the discrepancies between actual and predicted values of permeability at well locations, the integral permeability of the reservoir domain around each well, and skin factors. This inverse optimization problem is addressed by repeatedly solving forward problems, where an artificial neural network (ANN) predicts the integral permeability of the formation surrounding a well and skin factor. The ANN is trained on a physics-based dataset generated through a synthetic well test procedure, which includes the numerical modeling of the bottomhole pressure decline curve in a reservoir simulator and its interpretation using a semi-analytical reservoir model. The proposed method is tested on the “Egg Model”, a synthetic reservoir with significant heterogeneity due to highly permeable channels. The permeability map created by our approach demonstrates hydrodynamic similarity to the original map. Numerical reservoir simulations, corresponding to the constructed and original permeability maps, yield comparable pore pressure and water saturation distributions at the end of the simulation period. Additionally, we observe a notable match in flow rates and total volumes of produced oil, water, and injected water between simulations. The developed approach outperforms kriging in terms of numerical reservoir modeling outcomes. This research advances existing geostatistical interpolation techniques by fusing well logging and well test data to build the reservoir permeability map through an optimization framework coupled with machine learning. Unlike traditional variogram-based geostatistical simulation algorithms, our method provides a permeability distribution that is hydrodynamically similar to the actual one, enhancing initial guess in the history matching process. The novel incorporation of well test interpretation results into the permeability map represents a significant improvement over existing methods, offering an innovative approach that can benefit the petroleum industry. We also provide recommendations for further development of the proposed algorithm to account for geological realism.

SPATIAL ANALYSIS OF BROWN SPIDER MITE (Oligonychus punicae Hirst) DAMAGE IN AVOCADO USING SPATIAL ANALYSIS BY DISTANCE INDICES (SADIE)

Avocado (Persea americana Mill.) is a perennial crop that belongs to the Lauraceae family. This crop is affected by various pests, the most important of which is the spider mite Oligonychus punicae Hirst (Acari: Tetranychidae), also known as the brown avocado spider. The damage begins with reddish spots that spread throughout the leaf, eventually tanning the foliage and staining the fruit, resulting in its rejection in the market. As a result, understanding its spatial distribution is critical for growers to plan and implement integrated pest management programs effectively, reducing their economic and environmental impacts. This study used spatial analysis by distance indices (SADIE) to determine the spatial distribution of damage to the Hass avocado crop caused by brown spider mite populations in Coatepec Harinas and Donato Guerra, two municipalities in the State of Mexico, Mexico. Spatial behavior allows for the development of preventive or corrective control measures for the use of precision farming techniques. Spatial distribution maps were created using the geostatistical technique of ordinary Krigeing, and the mean and variance of damage caused by the brown spider mite in avocado were examined. The findings revealed a clustered spatial distribution with several aggregation centers that shift over time and correlate with the plots’ terrain and climate. No homogeneous pattern was found in any plot, indicating the need to continue this type of research to better understand concentration conditions and implement specific actions in the field.

Evaluating Daily Water Stress Index (DWSI) Using Thermal Imaging of Neem Tree Canopies under Bare Soil and Mulching Conditions

Water stress on crops can severely disrupt crop growth and reduce yields, requiring the accurate and prompt diagnosis of crop water stress, especially in semiarid regions. Infrared thermal imaging cameras are effective tools to monitor the spatial distribution of canopy temperature (Tc), which is the basis of the daily water stress index (DWSI) calculation. This research aimed to evaluate the variability of plant water stress under different soil cover conditions through geostatistical techniques, using detailed thermographic images of Neem canopies in the Brazilian northeastern semiarid region. Two experimental plots were established with Neem cropped under mulch and bare soil conditions. Thermal images of the leaves were taken with a portable thermographic camera and processed using Python language and the OpenCV database. The application of the geostatistical technique enabled stress indicator mapping at the leaf scale, with the spherical and exponential models providing the best fit for both soil cover conditions. The results showed that the highest levels of water stress were observed during the months with the highest air temperatures and no rainfall, especially at the apex of the leaf and close to the central veins, due to a negative water balance. Even under extreme drought conditions, mulching reduced Neem physiological water stress, leading to lower plant water stress, associated with a higher soil moisture content and a negative skewness of temperature distribution. Regarding the mapping of the stress index, the sequential Gaussian simulation method reduced the temperature uncertainty and the variation on the leaf surface. Our findings highlight that mapping the Water Stress Index offers a robust framework to precisely detect stress for agricultural management, as well as soil cover management in semiarid regions. These findings underscore the impact of meteorological and planting conditions on leaf temperature and baseline water stress, which can be valuable for regional water resource managers in diagnosing crop water status more accurately.

Fuzzy and spatial analysis of cutaneous leishmaniasis in Pará State, Brazilian Amazon: an ecological and exploratory study.

This study sought to analyze the relationships between cutaneous leishmaniasis and its epidemiological, environmental and socioeconomic conditions, in the 22 microregions of Pará state, Brazil, for the period from 2017 to 2022. In this ecological and exploratory study, the microregions were used as spatial units because they are formed by contiguous municipalities with similar characteristics. The epidemiological, environmental, socioeconomic, and public health policy data employed were obtained from the official information systems at the Ministry of Health, National Institute for Space Research, and Brazilian Institute of Geography and Statistics. A fuzzy system was developed to identify risk factors for the disease, using Python programming language. The results were analyzed with the bivariate Global Moran spatial analysis technique. It was observed that the Altamira microregion had the highest risk percentage for the disease, while Breves had the lowest, with significant differences in the relevance of its conditioning factors, mainly related to land use and cover patterns, in addition to demography and living conditions index, education and public health policies. The fuzzy system associated with the geostatistical technique was satisfactory for identifying areas with health vulnerability gradients related to deforestation, pasture, poverty, illiteracy, and health services coverage, as its conditioning variables. Thus, it was demonstrated that deforestation was the main risk factor for the disease. The system can also be used in environmental and epidemiological surveillance.