Inverse Distance Weighting Method Research Articles

Spatio-Temporal Trend Analysis of Annual and Seasonal Rainfall in Ho Chi Minh City

Spatio-temporal variations and trends in climatic variables play an essential role in the adaptation and mitigation of climate change policy. This study analyzed spatial and temporal variability of annual and seasonal rainfall in Ho Chi Minh City based on non-parametric statistical trend tests and spatial interpolation techniques. In particular, Sen’s Slope Estimator and Inverse Distance Weighting (IDW) methods were applied to construct the spatial distribution of annual and seasonal rainfall over the city. The outcomes show that annual and seasonal rainfall experienced significant increasing trends. Generally, these findings draw out an overall picture of rainfall variability in terms of space and time, which makes it possible for local decision-makers to promote more promising action plans in order to respond to natural disasters in the context of climate change.

A spatiotemporal comprehensive constraint interpolation method for missing values in monthly composite Suomi-NPP VIIRS Cloud Mask images

ABSTRACT The Earth’s nighttime light imaging data collected by the Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) are severely lost in summer, hindering the further application of VIIRS DNB data. In this article, a spatiotemporal comprehensive constraint interpolation method (SCIM) that accounts for the relative stationarity of temporal nighttime light, spatial correlations and heterogeneity among pixels is designed. Meanwhile, the method considers the seasonal fluctuations in nighttime light radiation, introduces spatial autocorrelation coefficients and spatial heterogeneity coefficients, and solves the problem of missing VIIRS DNB data. First, 8 widely used temporal interpolation methods (TIM) are selected to perform temporal interpolation for missing image data. Second, the existing VIIRS monthly images are used to construct the constraint relationship between time and space, and the pixels that do not meet the constraint conditions in the TIM simulation image are removed. The inverse distance weight (IDW) method is applied to the removed null pixels to obtain the image simulated by the SCIM. Finally, the accuracy of the TIM and SCIM is evaluated. The results show that the accuracy of the SCIM-simulated image is better than that of the TIM-simulated image. The proposed approach improves the quality of the VIIRS DNB dataset.

Spatial distribution of soil erodibility factors in erosion-prone areas in Umuahia, Southeast, Nigeria

This study evaluates the spatial distribution of soil erodibility factor (K) in Umuahia, Abia State, Nigeria using the Universal Soil Loss Equation (USLE) nomograph. The soils used for the study was sampled from 14 erosion prone areas in Umuahia, Southeast, Nigeria. The topsoil samples collected at depths of 0–10 cm, 10–20 cm, and 20–30 cm around the middle of each location identified with the aid of a GPS. The percentages of sand, silt, clay, moisture content, saturated hydraulic conductivity, and organic matter (OM) were all examined. The Gaussian ordinary kriging model for the determination of K-factor was compared with the Inverse Distance Weighting method. The K-factor’s coefficient of variation (CV) was 0.29 and the K-factor value of the nuggets to sill ratio (0.44) indicates a moderate spatial distribution. The Gaussian semi-variogram approach yielded the best estimate accuracy and model fitting effects, meaning that the Gaussian ordinary Kriging model is better for K-factor estimation. The root mean squared error (RMSE) was 0.0079 and the mean squared deviation ratio (MSDR) was 0.89, implying that the Gaussian model was unbiased and adequately captured the experimental variation. The K-factor values were lower in the north of the research region ranging from 0.0250 to 0.0197 Mg h MJ−1 mm−1 compared to east with the K-factor ranging from 0.0399 to 0.0423 Mg h MJ−1 mm−1. The estimated K-factor was relatively unbiased since the root mean square error was extremely small and the mean error was nearly equal to 0. The determination of soil erodibility (k-factor) influenced by factors such as soil texture, structure, and organic matter is crucial in assessing the vulnerability of land area to soil erosion. The spatial distribution of these factors affects the k-factor at different locations in a landscape, which enables accurate estimation of k-factor. Nutrient levels impact soil erodibility and distribution. Low nitrogen limits growth, favoring erosion. Phosphorus aids stability. Optimal potassium benefits growth and erosion control. Spatial distribution is vital, emphasizing precise nutrient management for effective soil health.

Methodology of Applying Inverse Distance Weighting Interpolation Method in Determining Normal Heights

This article explores the application of the Inverse Distance Weighting (IDW) interpolation method in determining normal heights, utilizing a network of points with measured ellipsoidal and normal heights by the use of GNSS/levelling method. The IDW method, which assumes that points closer to each other are more alike, is employed to locate points within the correct quadrant and calculate their normal height based on the determined average height anomaly across the entire area. The accuracy of this method is validated using check points. The article further discusses the methodology of normal height determination at a country level by using generated coefficients, representing the average difference between various height-systems, which could be used as an integral to the interpolation methods used for generating a refined Digital Elevation Model (DEM) across the country. This study contributes to the ongoing discourse on the effective use of interpolation methods in geospatial analysis and DEM generation. It provides valuable insights into the practical application of the IDW method and its potential for enhancing the accuracy of normal height determination and DEM refinement. Importantly, such methods hold significant value for countries lacking gravimetric data, as they provide a viable means of determining normal heights in the absence of a local quasi geoid model.

Assessment of Particle Matter Pollution during Post-Earthquake Debris Removal in Adiyaman City

Severe earthquakes in the world and in Turkey can cause great loss of life and property, environmental problems and health problems. In addition to the loss of life and property, earthquakes are closely related to ecosystems, air, water, and soil pollution. Particularly in post-earthquake debris removal, very large amounts of particulate matter are released and may have negative effects on the health of the local population. This study aimed to detect two types of particle matter pollution during debris removal in 25 different locations in Adiyaman City using a CEM DT 9880 particle matter measuring device in May and August 2023. PM2.5 and PM10 measurements, which have a significant impact on human health, were conducted in the field. The obtained data were analyzed using the Inverse Distance Weighting (IDW) method in ArcGIS 10.7 software. Results showed that the highest particle matter pollution was identified in neighborhoods with extensive demolition. The highest PM2.5 pollution values were recorded in Barbaros Hayrettin, Hoca Omer, Yesilyurt, and Varlik neighborhoods, while the lowest was observed in Altinsehir and Yunus Emre neighborhoods with minimal demolition. As for PM10 pollution, the highest levels were observed in Alitasi, Barbaros Hayrettin, Sumerevler, and Hoca Omer neighborhoods, while the lowest was detected in Yunus Emre and Yesilyurt neighborhoods. Green areas and their surroundings in the field exhibited lower levels of PM pollution compared to other areas.

Hazard-mapping and health risk analysis of iron and arsenic contamination in the groundwater of Sylhet district.

This study investigates groundwater contamination by arsenic and iron and its health implications within the Sylhet district in Bangladesh. Utilizing geographic information system (GIS) and inverse distance weighting (IDW) methods, hazard maps have been developed to evaluate contamination risk across various upazilas. The findings show significant arsenic and iron pollution, particularly in the northwestern part of the district. In about 50% of the area, especially in Jaintiapur, Zakiganj, Companiganj, and Kanaighat where arsenic levels surpass 0.05 mg/L which is the standard limit of Bangladesh. Iron levels peak at 13.83 mg/L, severely impacting 45% of the region, especially in Gowainghat, northeastern Jaintiapur, Zakigonj, and Golabganj. The study employs USEPA health risk assessment methods to calculate the hazard quotient (HQ) and hazard index (HI) for both elements via oral and dermal exposure. Results indicate that children face greater noncarcinogenic and carcinogenic risks than adults, with oral HI showing significant risk in Balagonj and Bishwanath. Dermal adsorption pathways exhibit comparatively lower risks. Cancer risk assessments demonstrate high carcinogenic risks from oral arsenic intake in all areas. This comprehensive analysis highlights the urgent need for effective groundwater management and policy interventions in the Sylhet district to mitigate these health risks and ensure safe drinking water.

Land suitability studies for paddy cultivation in Dadahup Swampy Irrigation Area

The Dadahup Swampy Irrigation Area is crucial in the government’s Food Estate initiative to enhance food security in Indonesia. Following extensive infrastructure improvements, including network rehabilitation and gate construction between 2020 and 2022, a pilot project was initiated in block A5. However, rice yields in this area still need to improve, primarily due to inadequate land management practices. This study evaluates and maps land suitability for paddy cultivation in the Dadahup Swamp Irrigation Area, employing a combination of laboratory data and field research integrated with geographic information system (GIS) technology. Various parameters were considered, such as climate factors (temperature and water availability), soil characteristics (drainage, texture, depth), peatland attributes (thickness, maturity), nutrient content (CEC, base saturation, pH, C-organic), available nutrients (nitrogen, phosphorus, potassium), salinity, alkalinity, pyrite depth, and inundation levels during planting. Then, thematic raster mapping was conducted using the Inverse Distance Weighted (IDW) method, followed by reclassification based on Food and Agriculture Organization (FAO) standards for land evaluation. The result categorized Block A5 into three suitability classes: S1 (highly suitable), S2 (moderately suitable), and S3 (marginally suitable). Approximately 22.5% of Block A5 is highly suitable (S1), 39.5% is moderately suitable (S2), and around 38% is marginally suitable (S3).

Spatial Variability of Selected Soil Chemical Parameters in Low-yielding Paddy Production Block in Mahaweli System H, Sri Lanka

Although water availability for paddy production in Mahaweli System H is abundant, paddy yield is lower in Nochchiyagama (278 km2), chosen for soil assessment. Paddy yield data were used to identify the low-yielding division using the area-weighted average. Twenty-five random locations were generated, and soil samples were collected for pH, soil conductivity (EC), salinity, and total dissolved solids (TDS) analysis. ArcGIS software was used to create spatial distribution maps and related geostatistical analyses for each parameter. Vector files were created with their associated properties, and thematic maps were generated using spatial interpolation techniques, such as universal kriging (UK), ordinary kriging (OK), and inverse distance weighted (IDW) methods of interpolation techniques to identify the best interpolation method for soil chemical parameters mapping. The entire Nochchiyagama land was observed to have a slightly acidic pH (5.6-5.9) range that may have affected rice crop growth due to nutrient mobility and uptake issues. The spatial interpolation evaluation suggests that at least two-thirds of the area observed for lower TDS levels (591-654 mg/L) is potentially unsafe paddy production. Elevated levels of EC (3.1-7.24 dS/m) along with TDS may lead to physiological drought due to interferences in ion uptake. In overall, spatial interpolation evaluation indicators suggest that the UK method was observed with a lower mean relative error (MRE) than the other two interpolations. However, EC distribution showed low MRE in both IDW and OK interpolation techniques. The IDW method was observed to have a lower RMSE (Root mean square error). The UK spatial interpolation performed better for TDS and salinity predictions than other methods. This study found consistent regional differences in low paddy yields in Mahaweli system H using the UK method for analyzing soil chemical parameters. 
 Keywords: Electrical conductivity, Interpolation errors, Salinity, Spatial interpolation

Evaluating lake water quality with a GIS-based MCDA integrated approach: a case in Konya/Karapınar

Considering water quality is an essential requirement in terms of environmental planning and management. To protect and manage water resources effectively, it is necessary to develop an analytical decision-support system. In this study, a systematic approach was suggested to evaluate the lake water quality. The methodology includes the prediction of the values in different locations of the lakes from experimental data through inverse distance weighting (IDW) method, creation of maps by using Geographic Information System (GIS) integrated with analytic hierarchy process (AHP) from multi-criteria decision analysis (MCDA), reclassification into five class, combining the time-related spatial data into a single map to predict the whole lake water quality from the data of sampling points, and finally overlapping the final maps with topography/geology and land use. The proposed approach was verified and presented as case study for Meke and Acigol Lakes in Konya/Turkey which were affected by human and natural factors although they have ecological, hydromorphological, and socio-economic importance. In the proposed approach, categorizing water quality parameters as “hardness and minerals,” “substrates and nutrients,” “solids content,” “metals,” and “oil-grease” groups was helpful for AHP with the determined group weights of 0.484, 0.310, 0.029, and 0.046, respectively. Assigning weights within each group and then assigning weights between groups resulted in creating accurate final map. The proposed approach is flexible and applicable to any lake water quality data; even with a limited number of data, the whole lake water quality maps could be created for assessment.

Widespread Resistance to Temephos in Aedes aegypti (Diptera: Culicidae) from Mexico.

Organic synthetic insecticides continue to be part of the arsenal for combating vector-borne diseases in Mexico. Larvicides are a fundamental part of the process in programs for mosquito control, temephos being one of the most widely used in Mexico. In the present study, we analyzed the frequency of temephos resistance in twenty-three Aedes aegypti populations using the discriminating concentration (DC) of 0.012 mg/L. We also tested 5× DC (0.6 mg/L) and 10× DC (0.12 mg/L) of temephos. The resistance distribution to temephos was interpolated to unsampled sites using the inverse distance weighting (IDW) method. The populations of Ae. aegypti showed a high frequency of resistance (1× DC) with mortality rates below 93% in 22 of the 23 populations analyzed. Moderate resistance intensity (5× DC) was found in 78% of the populations, and high intensity (10× DC) in 30%. Predicted mortality was below 60% in the populations of the Pacific Coast, along the Gulf of Mexico, and in the state of Coahuila in Northeastern Mexico in relation to 1× DC; the Pacific Coast and Northeast patterns hold for 5× and 10× DC. The results suggest the need for rotation of the larvicide to effectively control the larval populations of the vector in the country.

Analysis of Spatial distribution of Peatland Depth in Keub Village

Peat is an ecosystem that is formed due to biomass production that exceeds the decomposition process. The area of peat land in Aceh Province reaches 338,164 ha. Improper use of peatlands results in peatland degradation so that the thickness and depth of the peat soil decreases. This research aims to determine the map of changes in the distribution of peat depth classes and determine changes in the peat ecosystem classification map in Keub Village by comparing the peat depth map published by the Ministry of Environment and Forestry in 2019 with interpolation results using the inverse distance weight method. The results of this research showed that there was a change in the peat depth class and the area of distribution of the peat depth class between the peat depth class map published by the Ministry of Environment and Forestry and the interpolation results in Keub Village. Based on a comparison of the KLHK peat depth map with the interpolated map, it was also found that there was a change in the area of protected functional areas in the form of an increase in the number of areas.

Prediction of atmospheric PM2.5 level by machine learning techniques in Isfahan, Iran

With increasing levels of air pollution, air quality prediction has attracted more attention. Mathematical models are being developed by researchers to achieve precise predictions. Monitoring and prediction of atmospheric PM2.5 levels, as a predominant pollutant, is essential in emission mitigation programs. In this study, meteorological datasets from 9 years in Isfahan city, a large metropolis of Iran, were applied to predict the PM2.5 levels, using four machine learning algorithms including Artificial Neural |Networks (ANNs), K-Nearest-Neighbors (KNN), Support Vector |Machines (SVMs) and ensembles of classification trees Random Forest (RF). The data from 7 air quality monitoring stations located in Isfahan City were taken into consideration. The Confusion Matrix and Cross-Entropy Loss were used to analyze the performance of classification models. Several parameters, including sensitivity, specificity, accuracy, F1 score, precision, and the area under the curve (AUC), are computed to assess model performance. Finally, by introducing the predicted data for 2020 into ArcGIS software and using the IDW (Inverse Distance Weighting) method, interpolation was conducted for the area of Isfahan city and the pollution map was illustrated for each month of the year. The results showed that, based on the accuracy percentage, the ANN model has a better performance (90.1%) in predicting PM2.5 grades compared to the other models for the applied meteorological dataset, followed by RF (86.1%), SVM (84.6%) and KNN (82.2%) models, respectively. Therefore, ANN modelling provides a feasible procedure for the managerial planning of air pollution control.

Estimated Sulfur Dioxide Pollutant Concentrations In Medan City Using Ordinary Kriging and Inverse Distance Weighting Approaches

Sulfur dioxide (SO2) pollution is a serious problem that negatively affects air quality and human health. So2 is generated by various human activities, especially the combustion of fossil fuels, as well as from natural sources such as volcanic eruptions. The emission of SO2 in urban areas, including Medan, Indonesia, has raised concerns as it is associated with respiratory diseases and the formation of acid rain. This research aimed to estimate comparative SO2 concentrations in Medan City using geostatistical methods, specifically ordinary kriging and inverse distance weighted (IDW). Air quality monitoring data from the Dinas Lingkungan Hidup Kota Medan were collected during a certain period. The collected data were analyzed and then interpolated using ordinary kriging and IDW methods. Furthermore, the Ordinary Kriging method involves testing the OK assumption, calculation of the experimental semivariogram, calculation of the theoretical semivariogram, structural analysis, and calculation of Root Mean Square Error (RMSE). Meanwhile, the Inverse Distance Weighted method involves calculating the Euclidean distance, determining the weights based on the power parameter, calculating the RMSE value, and estimating the SO2 concentration. The comparison results show that the OK method is more accurate in determining SO2 concentration compared to the IDW method in Medan City. In the estimation of sulfur dioxide (SO2) concentration, the OK method used the best theoretical semivariogram model, namely the exponential model, and in the estimation process of the IDW method, the power parameter 1 was used.

Investigation of Depth-Area-Duration (DAD) Curves for Kuantan River Basin (KRB)

Depth-area-duration (DAD) analysis of rainfall is performed to evaluate the maximum amount of rainfall over areas of various sizes within different durations. It is frequently used to characterize precipitation extremes for the specification of so-called rainfall, with DAD curves resembling the correlation between rainfall depth and area. Kuantan River Basin (KRB) is one of the major rivers in Pahang that typically experiences heavy precipitation during the northeast monsoon season from November through March. In this study, DAD curves for KRB were developed for the duration of 1-day to 7-day. Daily rainfall data between 2008 to 2019 from eight stations in KRB were used to develop the DAD curves. DAD curve development initially requires an isohyet map’s illustration that can be constructed through the interpolation of Inverse Distance Weighting (IDW) method. The isohyet maps were developed in the ArcGIS software to purposely obtain the average maximum rainfall depths for each rainfall duration. The maximum rainfall amount of 1-day up to 7-day durations increased from 394.45 mm to 857.61 mm. The results indicated that the maximum amount of rainfall will increase along with the duration from 1-day to 7-day. The gap between the final DAD curve of 6-day and 7-day was less compared to other duration curves; however, the gap for both curves increased from the 800 km2 to 1000 km2 areas where the gap differences between the curves accelerated from 12 mm to 34 mm. Final DAD curves of KRB were produced from the combination of initial DAD curves with durations ranging from 1 to 7 days. The DAD curves produced in this study can provide further information on the maximum rainfall depth under different storm durations for hydrological planning purposes, particularly in KRB.

A Comparison of Spatial Interpolation Methods for Regionalizing Maximum Daily Rainfall Data in South Sulawesi, Indonesia

The aim of this research is to compare between the Inverse Distance Weighted (IDW) and Ordinary Kriging (OK) interpolation methods for regionalization of areas within the South Sulawesi province based on maximum daily rainfall. The data utilized consists of maximum daily rainfall data from 56 rain stations within the South Sulawesi from 1986 to 2021. The spatial interpolation methods applied include the power 2 IDW, and OK. Various semivariogram models, namely Spherical, Gaussian, and Exponential, are employed within the OK method. The selection of the best method is based on the smallest Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values. The findings of this research reveal that the optimal method for regionalization of maximum daily rainfall is the OK method with a Gaussian semivariogram model. The RMSE values for this method are 57.45, and the MAE values are 46.49. The results of the spatial interpolation demonstrate that the South Sulawesi is divided into four zones characterized by maximum daily rainfall (in mm) as follows: Zone I: less than 230 mm (Eastern and Southeastern regions), Zone II: 230-260 mm (Northern region), Zone III: 260-280 mm (Western region), and Zone IV: more than 280 mm (Southwestern region).

Real-time flood inundation monitoring in Capital of India using Google Earth Engine and Sentinel database

This study focuses on researching flood inundation and vulnerable areas in Delhi NCT using Remote Sensing (RS) and GIS techniques during flood from July 8 to July 15, 2023, with the entire analysis conducted through satellite and cloud-based processing methods, specifically employing the Google Earth Engine (GEE). Leveraging high-temporal satellites that gather data enables the identification of flooded zones in real-time, during floods, and in the aftermath. Analyzing data collected at different stages of a flood provides valuable insights for pinpointing affected areas. Water naturally flows from high to low elevation areas, and based on elevation data, lowest elevation regions, particularly along the Yamuna riverbank under Delhi NCT, are highly susceptible to flooding, are considered flood-prone areas and flood water inundated. A thorough comprehension and flood-prone area mapping, along with a map illustrating highly inundated zones. After obtaining flood inundation maps and overlaying them with Land Use and Land Cover (LULC) classified maps, the study identified specific areas that experienced flood inundation. The analysis generated a flood zone map, indicating that the flooded area encompasses approximately 110 km², within a total study area of 1488.4 km². The affected areas have elevations ranging from 200 to 210 meters, whereas the maximum elevation in the study area is approximately 326 meters. The GEE platform is employed for processing, utilizing a Supervised classification algorithm for LULC mapping, and an Inverse Distance Weight method for mapping temperature and rainfall. This study utilized the GEE platform to create pre- and post-flood maps based on Sentinel 1 satellite datasets. Generated DEM, and employed it to create various surface estimation maps, including a stream order map. The GIS is employed to enhance the efficiency of monitoring and managing flood disasters, with the high temporal and spatial resolution data playing a pivotal role in flood monitoring.

Understanding the local dynamics

This study addresses the dynamics of the Neolithic expansion in the Central Balkans by reconstructing its direction and speed of spread. The Inverse Distance Weighting (IDW) method is applied to the earliest radiocarbon dates from Starčevo culture sites in Serbia. Local speed estimates are derived from the earliest dates within specified spatial bins. The results confirm the assumed south-to-north direction, revealing rapid progression, particularly in the southernmost region. The possibility of pioneering scouting groups was also discussed. The results confirm the arrhythmicity of the Neolithization processes and higher local spread rates while offering new insights into Starčevo culture mobility.

Screening and optimization of interpolation methods for mapping soil-borne polychlorinated biphenyls

There is yet no scientific consensus, and for now, on how to choose the optimal interpolation method and its parameters for mapping soil-borne organic pollutants. Take the polychlorinated biphenyls (PCBs) for instance, we present the comparison of some classic interpolation methods using a high-resolution soil monitoring database. The results showed that empirical Bayesian kriging (EBK) has the highest accuracy for predicting the total PCB concentration, while root mean squared error (RMSE) in inverse distance weighting (IDW) is among the highest in these interpolation methods. The logarithmic transformation of non-normally distributed data contributed to enhance considerably the semivariogram for modeling in kriging interpolation. The increasing of search neighborhood reduced IDW's RMSE, but slightly affected in ordinary kriging (OK), while both of them resulted in over smooth of prediction map. The existence of outliers made the difference between two points increase sharply, and thereby weakening spatial autocorrelation and decreasing the accuracy. As predicted error increased continuously, the prediction accuracy of different interpolation methods reached unanimity gradually. The attempt of the assisted interpolation algorithm did not significantly improve the prediction accuracy of the IDW method. This study constructed a standardized workflow for interpolation, which could reduce human error to reach higher interpolation accuracy for mapping soil-borne PCBs.

GROUND WATER QUALITY AND ITS IMPACT ON HUMAN HEALTH IN DUNGARPUR DISTRICT OF RAJASTHAN, INDIA

Abstract. Dungarpur, one of the most backward districts of India, is a predominantly tribal region of Rajasthan state. Ground water is the major source of drinking water in the region. High concentrations of Fluoride (F) and Iron (Fe) have been reported in the region which poses high risk of rampant water-borne diseases. This study evaluates the ground water quality for drinking purpose in terms of 10 hydro-geochemical and two biological parameters in context of causative factors like land use and geology of the region. The occurrence of water borne diseases in resident population has been examined in association with quality of drinking water and priority areas for policy intervention have been identified. Water samples have been collected from 173 drinking water sources and 346 households consuming water from the selected sources have been surveyed for prevalence of water borne diseases. Water quality surfaces have been mapped in terms of F, Total Dissolved Solids (TDS), Hardness, Fe, alkalinity, Faecal Coliform (FC), E. Coli, and Water Quality Index (WQI) computed on the basis of 10 geochemical parameters, using Inverse Distance Weighted (IDW) method. Results reveal that the north-eastern part comprising Aspur, Sabla and part of Sagwara tehsils predominantly has ‘very poor’ to ‘unsuitable’ water quality. The western part has excessive faecal contamination. Entire district has high levels of TDS and hardness, while excessive Fe and F occur in specific regions. Disease incidence closely corresponds to the geochemical and microbial composition of water. Higher values of WQI correspond with occurrence of Phyllites and Mica Schists.

Implementation of Inverse Distance Weighting (IDW) and Kriging method for distribution pattern humidity and temperature data on weather changes in the Bangka Islands

Weather is one indicator of climate change on Earth that affects various aspects of life. The role and influence of the weather are also very influential on the elements that influence it. Moreover, the air temperature on Bangka Island has changed from previous years. This research is used to look at the pattern of temperature and humidity distribution in the Bangka Island region by implementing the IDW and Kriging methods. This research provides information on temperature and relative humidity changes from 2019 to 2021. Based on the results of the IDW and the Kriging method, an increase in temperature on Bangka Island occurs in the downtown area to the south of Bangka Island. As for relative humidity, there tend to be more changes from the northeast to the southwest of Bangka Island. In general, there is no significant difference in the implementation of both methods, but if we look at the prediction errors, it can be said that the kriging is better than the IDW method. With this research, it is hoped that it can become a consideration for related parties related to climate change in the Bangka Island Region.