Inverse Distance Weighting Interpolation Research Articles

Application of Multivariable Statistical and Geo-Spatial Techniques for Evaluation of Water Quality of Rudrasagar Wetland, the Ramsar Site of India

The water quality of Rudrasagar Lake, the second-largest natural reservoir of Tripura is of great ecological and economic importance as it serves a diverse range of purposes, including fishing, irrigation, aquaculture, domestic use, and recreation activities. This study investigates the water quality of the study area, an esteemed Ramsar site in North Eastern India, using a combined application of multivariable statistical and geospatial techniques. In this study, 24 water samples were designed based on their use and collected along the periphery and the inner areas of the lake employing the Latin Square Matrix. This research also examines the spatial variations of water quality involving quartile-based water quality categorization of parameters, with Pearson’s Correlation analysis, Principal Component Analysis (PCA), and Hierarchy Cluster Analysis (HCA) applied for dimension reduction. The analysis involved quartile-based water quality categorization of parameters, with PCA and HCA applied for dimension reduction. Meanwhile, the Inverse distance weighted (IDW) approach was used to interpolate the spatial distribution of the quartile score using the ArcGIS platform. The Bureau of Indian Standards (BIS) was followed for water quality assessment. The results revealed significant spatial variation, providing valuable insights for future water management strategies. PCA indicates 57.26% of the variance in the dataset, whereas samples were classified into three subgroups and two groups in a dendrogram representing the result of the HCA. This study demonstrates the utility of PCA, HCA, and IDW interpolation in water quality assessment, highlighting the effect of human-induced activities in the lake’s vicinity.

Distribution of Heavy Metals in Surface Sediments of a Tropical Mangrove Wetlands in Hainan, China, and Their Biological Effectiveness

The distribution and ecological risk of heavy metals in sediments were studied through the systematic collection and analysis of mangrove wetland sediments in Dongzhai Harbor, Hainan. The main insights obtained were as follows: (1) The distribution characteristics and influencing factors of heavy metals in wetland sediments were analyzed by using the inverse-distance weight interpolation method. In terms of spatial distribution, the contents of heavy metals As, Ba, Cd, Co, Cr, Cu, Hg, Mn, Ni, Pb, Sr, Ti, and Zn in the western part of the wetland were significantly higher than in the eastern part. The contents of heavy metals Cd, Co, Cr, Cu, Hg, Mn, Ni, Zn, and Ti near the anthropogenic area were significantly higher than at other points. (2) The pollution sources and ecological risks of heavy metals in wetland sediments were explored by using correlation analysis, cluster analysis, and potential ecological risk index analysis. The results showed that As, Ba, Pb, and Sr mainly originated from natural processes; Co, Cr, Cu, Mn, Ni, Ti, and Zn mainly originated from industry; and agricultural heavy metals mainly originated from Cd and Hg. The ecological risk analysis showed that there were obvious ecological risks of heavy metals in the western and southeastern corners of the wetland, which were both located in the vicinity of land far away from the coastline and near the human activities, and featured mangrove forests with dense vegetation characteristics.

Estimating the spatial distribution of soil volumetric water content in an agricultural field employing remote sensing and other auxiliary data under different tillage management practices

AbstractKnowledge of soil volumetric water content (VWC) on agricultural soils as influenced by different soil management practices is important, but the measurement outputs of frequently used traditional sampling techniques are restricted to point‐based measurements with limited spatial coverage. Remote sensing (RS) techniques are therefore being explored because of their greater spatial and temporal availability as well as their ability to cover large‐scale areas. But a general limitation for RS data is the presence of vegetation cover, cloud cover and the effect of topography. To minimize the effects of these factors during field sampling, the use of spectral indices and terrain attributes have proven successful in the estimation of several soil properties; however, the impact of these approaches for the estimation and mapping of soil VWC, an important factor in crop growth and development, especially where different soil management practices are employed, remains limited. To contribute to the knowledge base of RS under varying soil systems, this study explores the possibility of combining in‐situ measurements with remotely sensed (explanatory variables) data obtained under four different tillage practices to produce an estimated soil VWC that represents the entire study field. The tillage practices used include reduced till (RT), strategic till (ST), no‐till (NT) and conventional till (CT). From these tillage plots, three explanatory datasets, namely Sentinel‐2 (S2), spectral indices (SI) and terrain attributes (TA), were collected as predictors. In addition, each of the explanatory variables was structured into four groups, representing each of the four tillage methods. The eXtreme Gradient Boosting (XGBoost) model was used, and the best results were selected based on the root mean squared error (RMSE), the coefficient of determination (R2) and the mean absolute error (MAE). Prior to soil VWC prediction, the Pearson correlation matrix was used to determine the linear relationship between each of the selected explanatory variables (S2 bands, SI and TA) and soil VWC. Furthermore, spatial distribution maps of soil VWC were constructed using the inverse distance weighting (IDW) interpolation technique. For soil VWC estimation, the TA outperformed the SI and S2 datasets (R2 = 0.84). Similarly, the spatial distribution maps obtained from the TA data show a study area with a large concentration of VWC compared with the other datasets. The study also identified CT as the tillage approach that most impacts soil VWC estimation because all predicted results were poor without the addition of data from the CT plot. According to our findings, using TA data collected from various tillage management systems to estimate soil VWC is very promising because they can be used as predictors to improve soil VWC estimation and mapping. This study demonstrates conclusively that remote sensing data collected from different tillage management systems can be used as predictors to enhance the estimation and mapping of soil VWC, complementing the basis for the development of reliable and consistent precision irrigation management systems.

Contamination and source-specific health risk assessment of polycyclic aromatic hydrocarbons in soil from a mega iron and steel site in China

The iron and steel industry has always been a key and difficult point of environmental pollution control. In the present study, 493, 175, 153, 72, and 42 soil samples were collected from the soil depths of 0–0.5, 0.5–2, 2–3, 3–4, and 4–5 m (herein called the layers) of the Shougang Steel site, respectively. Compared with the evaluation criteria, the Shougang Steel surface soil was severely polluted by polycyclic aromatic hydrocarbons (PAHs). Inverse distance-weighted interpolation and the Kruskal-Wallis H test revealed that the soil PAH pollution in the iron-making area, especially the coking area, was severer than those in other areas. The PAH concentrations first decreased, and then, increased with the increase of depth. With the increase in depth, the contributions of 2- and 3-ring PAHs increased, while those of 4-, 5-, and 6-ring PAHs decreased. The bivariate local indicators of spatial association (LISA) analysis was used to identify the areas prone to soil PAH pollution due to atmospheric deposition of industrial waste gas and traffic emissions. The method could be used to analyze the impact of anthropogenic activities on soil's PAH pollution for other contaminated sites. Three main pollution sources of soil PAHs, the backfill source, the combustion of coal, and the traffic emissions, were identified based upon three diagnostic ratios, positive matrix factorization and the bivariate LISA analysis, and accounted for 53.8%, 23.5%, and 22.7%, respectively. The combination of bivariate LISA analysis and other source analysis methods could improve the accuracy of source analysis. Benzo[a]pyrene contributed the most to the total health risk among sixteen PAHs. The health risks related to the three pollution sources decreased in the order of backfill sources > coal combustion > traffic emissions. The incremental life-time carcinogenic risks were all below 10−4, indicating negligible or acceptable risks.

Water quality monitoring and assessment in coastal lagoons: Developing a protocol using a case study in Sainthamaruthu lagoon, Sri Lanka

AbstractSustainable water resources management requires monitoring and assessment of water quality in addition to quantity. Coastal lagoons play a critical role for the people and the ecosystem at large. Water quality monitoring and assessment for lagoons in Sri Lanka is important considering its services to the country. However, due to a lack of resources, most of the lagoons are not monitored and reported properly for further action. This study aims to develop a protocol in terms of monitoring sites, parameters, and frequency for water quality monitoring using a case study in a coastal lagoon, in Sainthamaruthu, Sri Lanka. The physicochemical parameters were measured weekly at eighteen monitoring sites for 4 months during the dry season from June to September 2022. Cluster analysis (CA) along with Inverse Distance Weighted (IDW) interpolation technique revealed that a minimum of three sites out of 18 are sufficient for monitoring water quality in the lagoon. In light of Principal Component Analysis (PCA) and Discriminant Analysis (DA), it was decided to retain temperature, pH, turbidity, Total Dissolved solids (TDS), Electrical Conductivity (EC), and Dissolved Oxygen (DO) in monitoring programs, since they cumulatively account for 79% of the variation in water quality in the lagoon spatially and temporally. Monitoring turbidity, TDS, and EC at weekly interval, while monitoring temperature, pH, and DO at biweekly or monthly is recommended. A further indication of the lagoon's water quality is that the mean values for turbidity (27 NTU), EC (754 μS/cm), and DO (4.6 ppm) higher than ambient standards, while TDS (344 ppm) and pH (6.98) are within. Therefore, we conclude that the lagoon requires remediation measures to be suitable for environmental purposes during dry periods.

Analysis of severe ozone-related human health and weather influence over China in 2019 based on a high-resolution dataset.

Ozone pollution in 2019 in China is particularly severe posing a tremendous threat to the health of Chinese inhabitants. In this study, we constructed a more reliable and accurate 1-km gridded dataset for 2019 with as many sites as possible using the inverse distance weight interpolation method to analyze spatiotemporal ozone pollution characteristics and health burden attributed to ozone exposure from the perspective of different diseases and weather influence. The accuracy of this new dataset is higher than other public datasets, with the coefficient of determination of 0.84 and root-mean-square error of 8.77 ppb through the validation of 300 external sites which were never used for establishing retrieval methods by the datasets mentioned-above. The averaged MDA8 (the daily maximum 8 h average) ozone concentrations over China was 43.5 ppb, and during April-July, 83.9% of total grids occurred peak-month ozone concentrations. Overall, the highest averaged exceedance days (60 days) and population-weighted ozone concentrations (55.0 ppb) both concentrated in central-eastern China including 9 provinces (only 11.4% of the national territory); meanwhile, all-cause premature deaths attributable to ozone exposure reached up to 142,000 (54.9% of national total deaths) with higher deaths for cardiovascular and respiratory, and the provincial per capita premature mortality was 0.27~0.44‰. The six most polluted weather types in the central-eastern China are in order as follows: westerly (SW and W), cyclonic, northerly, and southerly (NW, N, and S) types, which accounts for approximately 73.2% of health burden attributed to daily ozone exposure and poses the greatest public health risk with mean daily premature deaths ranging from 466 to 610. Our findings could provide an effective support for regional ozone pollution control and public health management in China.

Spatial Distribution of Water Quality in Welang, Gembong and Rejoso Rivers, Pasuruan, East Java, Indonesia

A river is a naturally formed freshwater stream that traverses land and eventually flows into a lake, sea, or another body of water. River provides fresh water for human activities such as irrigation for their paddy fields, aquaculture, industrial purposes, and many other purposes. At the same time, there exists an inherent disparity in the demand, availability, and quality of river water, often giving rise to significant challenges and issues. Environmental experts, commonly use a multivariate statistical method such as Principal Component Analysis (PCA), Storage and Retrieval (STORET), and cluster analysis for water quality analysis. However, those methods are numerical and limited in spatial visualization. Inverse Distance Weighting (IDW) interpolation, Voronoi, and Kriging were applied to obtain the spatial representation of water quality distribution Welang, Gembong, and Rejoso rivers in Pasuruan as study. The objectives are to locate on a map any river segments that experienced poor water quality throughout the observation period. We successively combined STORET with those spatial interpolation. The result shows that IDW interpolation, Voronoi, and Kriging can visualize and map river segments that had poor water quality during the observation time. However, due to the limited input data, the interpolation results exhibit variability. For instance, at a measured location with a STORET value of -28, IDW yielded -28, Voronoi -28, and Kriging -27. Beyond the measurement points, each interpolation method began to produce less accurate values. This study involves interpolating dynamic objects with limited measurements data in narrow channels, which differs from interpolating elevation in broader area, in terms of the accuracy of representation or visualization obtained from this spatial analysis still remain unresolved in this study.

Agroclimate zone analysis for the suitability of rice plantation in Donggala District

As one of the supporting districts for regional food security, the productivity of food crops in Donggala Regency needs to be continuously improved. Climate type information is closely related to business development in agriculture, especially rice plants. This study aims to determine the type and distribution of agro-climatic zones for rice land suitability in Donggala Regency. This study uses the climate classification method according to Oldeman by calculating the number of wet months and dry months for one year which is then mapped using the Inverse Distance Weighted (IDW) method. The data used in this study are rainfall data for 10 years (2011-2020) from 15 points of BMKG rain observation posts as well as the Planting Calendar for rice plants from the Ministry of Agriculture. The results showed that Donggala Regency has 3 Oldeman climate types, namely type A in Rio Pakava District with the suitability of continuous rice cultivation but low production, B in Banawa Selatan and Sojol Districts with the suitability of continuous rice planting with high production when harvested during drought season. Type E in the Districts of Central Banawa, Banawa, Tanantovea, Labuan, Sindue, Sindue Tobata, Sindue Tombusabora, Balaesang, Balaesang Tanjung, North Sojol and Dampelas which is too dry, so that only one planting period of crops is possible. In areas with climate type E it is recommended to optimize irrigation to meet the water needs of rice plants. The agro-climate zone map made using the IDW interpolation method can describe the distribution of climate types as a whole, especially in areas where there are no rain gauge posts such as in Pinembani District.

Soil pH Variability in Mangu, Bokkos and Pankshin Local Government Areas of Plateau State

This paper investigates soil pH variability in three local government areas (LGAs) in Plateau State, Nigeria: Bokkos, Mangu, and Pankshin. Soil pH is a crucial factor influencing soil biogeochemical properties, which in turn affect crop yield, soil nutrient levels, and microbial activity. The study utilized Stratified Random Sampling to collect soil samples, and employed IDW (Inverse Distance Weighted) interpolation for analysis. The results revealed a pH range of 4.5 to 7.07, categorized into five classes: very strongly acidic (4.5 - 5.05), strongly acidic (5.05 - 5.35), moderately acidic (5.35 - 5.58), slightly acidic (5.58 - 5.83), and neutral (5.83 - 7.07). Slightly acidic and moderately acidic soils were predominant across the entire study area, covering 1643.96 Km² and 1444.41 Km² respectively. Noteworthy variations in pH were observed between the LGAs, with Bokkos and Mangu exhibiting more acidic soils, while Pankshin had soils tending towards neutrality. These variations were attributed to the local topography and geology. The pH variations also play a significant role in determining suitable crops for cultivation. Bokkos and Mangu are conducive for extensive farming of maize and Irish potatoes, while Pankshin is better suited for millet and sorghum cultivation. Given the global implications of events like the Russia-Ukrainian war on food supply, the study recommends that relevant government agencies identify areas with similar soil characteristics and invest in extensive cultivation of crops well-suited to those soils. This could contribute significantly to enhancing food security on a global scale]

Generating Water Quality Maps of Klang River Based on Geographic Information System (GIS) and Water Quality Index (WQI)

Water quality index (WQI) and Geographic Information Systems (GIS) play a critical role in managing and modelling a variety of water resource issues, including urban drainage, point and non-point source pollution. Historically, the evaluation of water quality has been a domain reserved for experts, necessitating laborious and time-consuming in situ sampling and laboratory analysis. However, the integration of WQI and GIS has democratized this information, making it accessible to non-experts, thereby enhancing the comprehension of Klang River's water quality. The objective is to employ WQI and GIS to create comprehensive water quality maps. While WQI offers a straightforward numerical evaluation, the incorporation of graphical data provides a nuanced understanding of river pollution. Therefore, hourly WQI data observed at every week in 2 months from October to November 2021 over four stations (Kampung Medan, Kampung Lombong, Taman Pengkalan Batu and Jeti Sungai Udang) in Malaysia was acquired from the Selangor Maritime Gateway (SMG) website and the Malaysian National Water Quality Standard (NWQS). Adopting Inverse Distance Weighting (IDW) interpolation method, WQI parameters at unsampled locations were estimated based on values of nearby sampled points. Database was built to depict the water quality of the Klang River, particularly during the two-month monitoring. Mapping provides a clear indication of the river's water quality. The WQI mapping outcome fall between class II and class IV. The findings indicate varying water quality classes along the Klang River, revealing potential pollution sources in industrial and development areas. It was concluded from the study that the water pollution may be due to its proximity to industrial and development regions.

Lake water quality assessment using spatial interpolation and the development of the WQI on an educational campus, Assam, India

Abstract Monitoring water quality metrics is essential for managing water quality and safeguarding aquatic life. The NIT Silchar Lake has been a place for many aquatic plants and migratory birds. The lake water is typically used on campus for horticultureal purposes. However, the peasants living close to campus use the lake water for drinking during situations like floods or famine. At four separate locations of the lake, the samples were collected for evaluation of seven water quality indicators. Temperature between 22.4 and 30.5 °C and dissolved oxygen concentrations between 8 and 13 mg/l were found, which were optimal conditions for aquatic life. The water quality index (WQI), which gives an overall evaluation of the state of the water quality, was created from the measured values (six parameters). The lake regions that ranged from good to excellent were identified using ‘weighted arithmetic water quality index’ (WAWQI) technique. Further we have compared the WAWQI with another method. A comparative analysis has been done by developing simple codes with the help of python programming language. The inverse distance weighted (IDW) interpolation in GIS was applied for spatial distribution of water quality parameters and WQI.

Pollution Characteristics and Sources of Heavy Metals in Soil of a Typical Pyrite Concentrated Mining Area in Anhui Province

To identify the characteristics and sources of heavy metal pollution in the surrounding soil of a typical pyrite concentrated mining area in Anhui Province, the pH value and the concentrations of Zn, Cu, Cd, Pb, Cr, Ni, Hg, and As in 42 surface soil samples and 16 soil samples from two vertical sections in the study area were collected and measured. The distribution characteristics, pollution assessment, and source analysis of heavy metal elements were conducted using the ArcGis inverse distance weight interpolation method, Nemero comprehensive index method, principal component analysis method, and absolute factor score-multiple linear regression (APCS-MLR) receptor model. The results showed that the average concentration of the eight heavy metals was 1.03-13.14 times the background value in the Tongling area. The local enrichment of Zn, Cu, Cd, Pb, Hg, and As was obvious, and the spatial distribution was basically consistent with the location of man-made mining activities. The single-factor pollution index evaluation showed that Zn, Cu, Cd, Pb, and As had different degrees of pollution risk, and the pollution degree of Cd and Cu was the most serious, accounting for 47.62% and 42.86% of moderate pollution, respectively. The Nemerow comprehensive pollution index evaluation showed that 61.90% of the soil samples in the study area were moderately polluted. The ground accumulation index evaluation showed that the pollution degree of Zn, Cu, Cd, Pb, Hg, and As in the study area was mainly from light pollution to strong pollution. In the vertical soil profile variation, heavy metals were easily enriched in the surface layer of the soil but migrated slowly to the deep layer, with concentrations mainly above 1 m. The results of source analysis showed that the high geological background and mining contribution rates of Zn, Pb, and As were 37.82%, 43.49%, and 46.63%, respectively. The natural contribution rates of weathering of parent material were 34.02%, 40.88%, and 38.52%, respectively. The sources of Cr and Ni were mainly natural sources of weathering of the parent material, with contribution rates of 91.95% and 73.68%, respectively. Geological high background and mining activities contributed 41.91% of the Cu sources, and atmospheric sedimentation and agricultural comprehensive sources contributed 41.30%. There were many sources of Hg. The natural source of weathering of soil parent material contributed 35.60%, geological high background and mining activities contributed 29.87%, and unknown sources contributed 34.05%. The main source of Cd was atmospheric sedimentation and agricultural comprehensive sources, contributing 81.81%.

Temporal and spatial distribution of soil water repellency in grassland soils and its relation to soil moisture, hydrophobic matter, and particle size

In grassland soils, soil water repellency (SWR) may be one of the triggers of soil erosion and degradation as it can reduce water infiltration and penetration into the soil. Few studies were focusing on the evaluation of soil hydro-physical properties, such as hydrophobicity, and their relation to soil moisture, hydrophobic matter, and particle size in grassland soils. In this study, 800 soil samples were collected from the Xilingol grassland in Inner Mongolia, China, using the water droplet penetration time (WDPT) test to evaluate water repellency and we aimed to investigate the temporal and spatial distribution of SWR in grassland soils using the Kriging and Inverse Distance Weighting (IDW) interpolation methods and determine the physical-chemical properties that trigger the SWR. The results showed that the grassland soils in the studied area were slightly water-repellent and a few portions of the area exhibited strong water-repellency. In April, areas of soils with a depth of 0–5 cm and slight to strong SWR accounted for 80 % of the total studied area, of which 5 % had strong water repellency. Moreover, in August, 90 % of the studied area consisted of soils with slight to strong SWR, of which 60 % accounted for soils with strong SWR. With a soil water content of 10.95 %, the SWR reached its peak, with an average value of 60.32 s. The SWR was positively correlated with total N, available N, and soil organic matter (SOM) contents, and therein the hydrophobic acid matter and the hydrophobic basic matter content had a positive contribution to SWR, and the hydrophilic basic matter and the hydrophilic acidic matter had a negative contribution on SWR. In addition, SWR was found to be negatively related to the soil particle size (r = −0.672). A slight SWR was also observed in the majority of the studied area, particularly in the topsoil and fine soils, especially during the monsoon period; hence, SWR must be also considered to reduce the risk of occurrence of soil erosion and degradation in grasslands.

Machine Learning for Predicting Forest Fire Occurrence in Changsha: An Innovative Investigation into the Introduction of a Forest Fuel Factor

Affected by global warming and increased extreme weather, Hunan Province saw a phased and concentrated outbreak of forest fires in 2022, causing significant damage and impact. Predicting the occurrence of forest fires can enhance the ability to make early predictions and strengthen early warning and responses. Currently, fire prevention and extinguishing in China’s forests and grasslands face severe challenges due to the overlapping of natural and social factors. Existing forest fire occurrence prediction models mostly take into account vegetation, topographic, meteorological and human activity factors; however, the occurrence of forest fires is closely related to the forest fuel moisture content. In this study, the traditional driving factors of forest fire such as satellite hotspots, vegetation, meteorology, topography and human activities from 2004 to 2021 were introduced along with forest fuel factors (vegetation canopy water content and evapotranspiration from the top of the vegetation canopy), and a database of factors for predicting forest fire occurrence was constructed. And a forest fire occurrence prediction model was built using machine learning methods such as the Random Forest model (RF), the Gradient Boosting Decision Tree model (GBDT) and the Adaptive Augmentation Model (AdaBoost). The accuracy of the models was verified using Area Under Curve (AUC) and four other metrics. The RF model with an AUC value of 0.981 was more accurate than all other models in predicting the probability of forest fire occurrence, followed by the GBDT (AUC = 0.978) and AdaBoost (AUC = 0.891) models. The RF model, which has the best accuracy, was selected to predict the monthly forest fire probability in Changsha in 2022 and combined with the Inverse Distance Weight Interpolation method to plot the monthly forest fire probability in Changsha. We found that the monthly spatial and temporal distribution of forest fire probability in Changsha varied significantly, with March, April, May, September, October, November and December being the months with higher forest fire probability. The highest probability of forest fires occurred in the central and northern regions. In this study, the core drivers affecting the occurrence of forest fires in Changsha City were found to be vegetation canopy evapotranspiration and vegetation canopy water content. The RF model was identified as a more suitable forest fire occurrence probability prediction model for Changsha City. Meanwhile, this study found that vegetation characteristics and combustible factors have more influence on forest fire occurrence in Changsha City than meteorological factors, and surface temperature has less influence on forest fire occurrence in Changsha City.

Characterization of water status and vegetation cover change in a watershed in Northeastern Brazil

The existing water scarcity in the Brazilian semiarid region, associated with anthropic activities, imply in the degradation of the watersheds present in the Caatinga. Therefore, the objective of this study was to characterize water and vegetation cover changes in the Ipanema River Watershed (IRW). IRW is inserted in municipalities that make up the dairy basins of the states of Alagoas and Pernambuco, one of the main economic activities in the Northeast of Brazil (NEB). Land use and land cover (LULC) maps, Modified Normalized Difference Water Index (MNDWI) and rainfall product via TerraClimate were used in the study. The MNDWI was based on the annual mean between 2010 and 2020 for the dry and rainy periods. For rainfall patterns and variability, pixel values were extracted and submitted to the Inverse Distance Weighted (IDW) interpolation method. We highlight the inverse relationship between the categories Forestry, Agriculture and Cattle Raising, with a decrease in the area of Caatinga due to agriculture and cattle raising. In the dry period via MNDWI there was higher water availability in 2010, 2017, 2018, 2019 and 2020, the exceptions were the years 2013–2016. In the rainy season, clouds interfered in the MNDWI analyses, with lower water availability in 2013, the exceptions were 2017 and 2018. Comparatively, the images from 2010 to 2020 via MNDWI, showed a water reduction in the IRW, mainly in the southern portion. For the dry period, the years 2011, 2012, 2016 and 2019 showed less than 138 mm of rainfall, especially in 2016 (<64 mm). In the rainy season, the years 2012, 2013, 2016, 2018 and 2019 also showed less than 482 mm of rainfall, with 2012 and 2016 standing out (<101 mm), mainly, the months with the highest rainfall record at IRW. The results obtained in this study show that droughts in the NEB continue to the present day, followed by an increase in water deficit. Anthropic action implies environmental degradation in one of the most threatened ecosystems (Caatinga), affecting mainly the water resource, an important factor in the survival of the local population and in the territorial dynamics.

MAPPING RAINFALL [ISOHYET] IN AREAS WITH A SHORTAGE OF RAIN STATIONS USING MANUAL STATION TECHNIQUE AND PRECTOTCORR MERRA-2 DATA ANALYSIS

Rainfall observation data from each rain station in an area is so important to measure the intensity of rainfall in that region. The number of rain stations that are not ideal will affect the process of acquiring rainfall data which is the primary data required for regional rainfall mapping. This study uses the manual station technique and analysis of satellite precipitation data as an alternative aid for mapping annual rainfall with the Isohyet method for areas constrained by a shortage of rain stations like the Nabire Regency area. The purpose of the research was to study the intensity of rainfall in the Nabire Regency area based on data PRECTOTCORR, bias-corrected precipitation by MERRA-2 for the last ten years of precipitation data from 2014-2023 taken from the POWER-NASA climate map and managed through ArcGIS software. The area of ​​Nabire Regency with ​​12.075,00 km2 includes the category of place with high rainfall intensity where the average rainfall ranges from 2000 mm – 5000 mm per year. The results of rainfall mapping with IDW (Inverse Distance Weighting) interpolation and Kriging interpolation show that there are several areas with the highest rainfall, Napan, Siriwo, Kimi, Menou, and Dipa, while the lowest rainfall areas are in the coastal areas, Teluk Umar and the northern region of Wapoga.

Inverse Distance Weighting as an alternative interpolation method to create radiometric maps of natural radionuclide concentrations using QGIS

Abstract. A previous study by the authors illustrated the distribution of naturally occurring radionuclides, i.e., potassium (K40), thorium (Th232), and uranium (U238), in the sediment of the Berg River estuary. The study also described the Delta Underwater Gamma detection System (DUGS) and its usage to measure the concentrations of these natural radionuclides. It also proposed a novel radiometric mapping technique with QGIS and, more importantly, highlighted the geospatial process through Kernel Density Estimation (KDE) to create radiometric maps. The present study used the same data and a similar design but proposed the use of the Inverse Distance Weighted (IDW) interpolation method in QGIS to display natural radionuclide concentrations. The radiometric maps created in the previous study using the KDE technique created smooth and visually attractive maps. However, the IDW method is an exact interpolation method that predicts a value at a sampling location that is identical to the observed value. This is a requirement for the analysis of natural radionuclide concentrations in sediments. However, the effectiveness of the interpolation methods was evaluated using SPSS statistics software. First, probability-probability (P-P) plots were produced for each interpolation method. Secondly, descriptive regression statistics, including 'goodness-of-fit', Analysis-of-Variance (ANOVA), coefficients, and residuals were evaluated for both the IDW and KDE interpolation methods. This was done to assess which method was more effective for calculating the radionuclide concentrations (actual vs predicted values) in the Berg River area. The results showed that both methods experienced problems to predict unknown values. However, IDW consistently performed better than KDE across most of the interpolation tests. Natural radionuclides are useful predictors to track sedimentation and the results of this paper can serve as a benchmark for future work in tidal and non-tidal coastal environments.

Inverse distance weighting interpolation-based immersed boundary velocity correction method for incompressible flows

In this work, the inverse distance weighting (IDW) interpolation is introduced into the implicit velocity correction-based immersed boundary method (IBM) for simulation of incompressible flows. In the original implicit velocity correction-based IBM, the solid body must be immersed in a uniform mesh region due to the use of the smooth Dirac delta function, which is utilized to associate Lagrangian points with their surrounding Eulerian points and only works with uniform meshes. The IDW method has the advantage that the interpolation range can be set flexibly. The introduction of the IDW interpolation can extend the application of the IBM to non-uniform meshes while reducing the number of Lagrangian points. The numerical test by the decaying vortex problem proves that the IDW interpolation does not significantly affect the overall accuracy of the IBM. In addition, numerical experiments for the flows around a circular cylinder and a NACA0012 airfoil demonstrate the advantages of the proposed method, including allowing fewer Lagrangian points while ensuring no streamline penetration to the solid body, as well as its adaptability to non-uniform meshes which can improve the computational efficiency due to the use of fewer mesh points. Finally, the simulation of the flow past a stationary sphere illustrates that the proposed method can effectively simulate the three-dimensional flow.

GM(1,1)-Based Weighted K-Nearest Neighbor Algorithm for Indoor Localization

Along with the IoT technology, the importance of indoor positioning is increasing, but the accuracy of the traditional fingerprint positioning algorithm is negatively affected by the complex indoor environment. This issue of low indoor spatial geolocation localization accuracy when the signal is collected away from the present stage occurs due to the signal instability of the iBeacon in the traditional fingerprint localization algorithm, which generates a variety of factors such as object blocking and reflection, multipath effect, etc., as well as the scarcity of reference fingerprint data points. In response, this study proposes an inverse distance-weighted optimization WKNN algorithm for indoor localization based on the GM(1,1) model. By implementing GM(1,1) model pre-process leveling, the original fingerprint library was reconstructed into a large-capacity fingerprint database using the inverse distance-weighted interpolation method. The local inverse distance-weighted interpolation was used for interpolation, combined with the WKNN algorithm to complete the coordinate solution in real time. This effectively solved the issue of low localization accuracy caused by the large fluctuation of the received signal strength (RSS) sampling measurement data and the existence of few reference fingerprint datapoints in the fingerprint database. The results show that this algorithm reduced the average positioning error by 5.9% compared with ordinary kriging (OK) interpolation leveling and reduced the average positioning error by 18.2% compared with the indoor spatial location accuracy of the original fingerprint database, which can effectively improve the positioning accuracy and provide technical support for indoor location and navigation services.

GEOTURISTIC POTENTIAL OF GEOLOGICAL MONUMENTS OF THE DNIPROPETROVS`K REGION

Geotourism as a modern trend in the tourism industry is gaining more and more popularity worldwide. It is the development of knowledge about the geological history of our planet, the course of processes of the formation of various rocks and minerals.&#x0D; The territory of Dnipropetrovs’k region is rich in unique, rare objects of the geological environment, including and world importance. These objects have been preserved on the earth's surface in the form of rock outcrops, which most clearly characterize the geological structure and history of the development of our planet, and are of great scientific and educational importance.&#x0D; The most unique objects of the natural heritage of Dnipropetrovs’k region, worthy of international attention, are geological monuments. Today, about 38 geological monuments of nature can be classified as geological monuments within the Dnipropetrovs’k region, among which 13 are included in the nature reserve fund of Ukraine and approved by the relevant decisions of state institutions.&#x0D; In order to determine the geotourism potential of the regions of the region, a comprehensive, point-based, monitoring assessment of geological monuments was carried out according to the following indicators: informational uniqueness, physical condition, state of improvement, transport accessibility, demand, seasonality of visits, ecological condition, recreational attractiveness, safety of inspection, density of accommodation, nature conservation status. The total number of points demonstrates the tourist attractiveness of the object.&#x0D; According to the specified criteria, the number of points for evaluating the tourist attractiveness of geological monuments varies from 15 to 28 points. Geological monuments assigned to the nature reserve fund received the highest points.&#x0D; The assessment of the spatial differentiation of indicators of tourist attractiveness of the geological monuments of the region was performed using the inverse distance weighting (IDW) interpolation method in the QGIS program. As a result, a map of the geotourism potential of the regions of the Dnipropetrovs’k region was built. Four types of zones are distinguished: with high (western part of the region, most of Kryvyi Rih region), medium (southern part of the region), low geotourism potential (the central part and a separate zone in the south of the region), and the territory where there are no geological monuments (north and east region).&#x0D; The analysis of the map makes it possible to come to the conclusion that, at present, geological monuments located in the Kryvyi Rih district are of interest for the formation of geotours. Other geological objects of Dnipropetrovs’k region should be considered as a supplement to tours of another direction, for example, based on a combination of natural, archaeological and cultural objects. This is facilitated by the fact that the majority of geological monuments are territorially connected with historical and cultural monuments, recreational locations, religious routes.&#x0D; In order to popularize geotourism sites in the region, an interactive map "Geological sights of the Dnipropetrovs’k region, classified as a nature reserve" was created on the Google Maps platform.&#x0D; An excursion route "Geological monuments of Kryvorizhye" has also been created. The excursion consists of 9 objects that reveal the entire range of the geological history of Kryvorizhye and covers the age range from the Paleoarchean to the Neoproterozoic, which is about 2 billion years, imprinted in numerous outcrops along the banks of the Ingulets and Saksagan rivers and the slopes of the streams. The tour is designed as a GPS mobile multimedia guide on the Izi.travel platform.&#x0D; Keywords: geological reminder, geotourism potential, geological tourism, Dnipropetrovsk region.