Interpolation Maps Research Articles

Identification of suitable location to cultivate grape based on disease infestation using multi-criteria decision-making (MCDM) and remote sensing

This study aims to identify suitable regions for grape cultivation and the areas where diseases are likely to occur in Fars Province, Iran. The study employs a multi-criteria decision-making (MCDM) model based on the Analytic Hierarchy Process (AHP) and fuzzy logic methods, taking into account the ecological requirements of grape plants. Fourteen essential parameters for grape cultivation are prepared, and interpolation maps are created for each parameter. Fuzzy maps are generated by defining membership functions, and AHP is utilized to compare and determine the importance of each factor to produce a final map of grape cultivation land suitability. The study reveals that grape cultivation is most suitable in the southern regions of Fars Province. Furthermore, the research identifies the areas where Powdery mildew, Gray mold, and GFLV diseases may occur based on rainfall and temperature levels. The study recommends against grape cultivation in the southern region due to the possibility of these diseases developing. The study employs a highly accurate regression method (R2 = 0.968 and F = 0.00001) and principal component analysis (PCA) to determine that land suitability class strongly correlates with Tmin, NDVI, LST, Total disease, Tmean, and RH. By identifying the areas suitable for grape cultivation and where pests may flourish, the necessary management can be implemented to ensure maximum harvest in the best suitable locations for grape planting.

Hydrogeochemical characterisation and geospatial analysis of groundwater for drinking water quality in Ludhiana district of Punjab, India

This study characterises the quality of groundwater for the Ludhiana district of Punjab, India by analysing water samples collected from 152 locations spread across 3767 km2. The samples were analysed for 18 parameters consisting of pH, EC, TDS, TA, TH, major anions and cations. The parameter values have been used to calculate the drinking water quality index of the study area which suggests that 2.6, 57.9, 32.9, 4 and 2.6% of the samples fall under the excellent, good, poor, very poor and unsuitable categories, respectively. The sequence of abundance for ions (in meq/l) as revealed from the laboratory tests is Na+ (37.1%) > Ca2+ (30.8%) > Mg2+ (29.1%) > K+ (2.8%) for cations and HCO3- (80%) > Cl¯ (8.9%) > CO32- (6.5%) > SO42- (3.9%) > NO3-, F-, PO43- (< 1%) for anions. The spatial variability of these parameters has been depicted through the use of interpolation maps. Evaluation of different ionic ratios indicates that carbonate weathering and silicate weathering are both significantly affecting the groundwater chemistry with a slight dominance of carbonate weathering. Also, the ion exchange process is taking place in the area as confirmed by CAI index values. In terms of saturation index, the groundwater is undersaturated with respect to halite, fluorite and sylvite, whereas it is supersaturated for calcite, dolomite and aragonite minerals. The principal components in PCA explained 75.4% of the total variance with 29.1 and 28.3% contributions from PC1 and PC2. Both of these components indicate towards the geogenic and anthropogenic influence on groundwater mineralization of the area. The analysis suggests that groundwater for the study area is suitable for drinking in most of the region expect in a few places. Such a study could be used to understand the current status of groundwater quality in the area, the results of which can be used to prevent further contamination and sustain the resource for the future.

Japanese encephalitis transmission trends in Gansu, China: A time series predictive model based on spatial dispersion

ObjectiveThis study serves to ascertain trends of space and time for Japanese encephalitis (JE) transmission at the township-level and develop an innovative time series predictive model to predict the geographical spread of JE in Gansu Province, China. MethodsWe collected weekly data on JE from 2005 to 2019 at the township-level. Kriging interpolation maps were used to visualize the trend of the epidemic spread of JE, and linear regression models were used to calculate the monthly changes in minimum longitude and maximum latitude of emerging towns with JE to assess the speed of the epidemic's spread to the northwest. Additionally, we utilized a time series Seasonal Autoregressive Integrated Moving Average (SARIMA) model to dynamically predict the ongoing weekly number of JE emerging townships. ResultsThe Kriging difference map revealed a significant trend of JE spread towards the northwest. Our regression model indicated that the rate of decrease in minimum longitude was approximately 0.64 km per month, while the rate of increase in maximum latitude was approximately 1.00 km per month. Furthermore, the SARIMA pattern (2,0,0)(2,0,1)52 exhibited a better goodness-of-fit for predicting JE transmission, with an overall agreement of 93.27% to 94.23%. ConclusionOur study highlights the expansion of JE cases towards the northwest of Gansu, indicating the need for ongoing surveillance and control efforts. The use of the SARIMA model provides a valuable tool for predicting the trend of JE spatial dispersion, thereby improving early warning systems. Our findings suggest that the number of emerging townships can be used to predict the trend of JE spatial dispersion, providing crucial insights for future research on JE incidence.

CLASSIFICATION OF STUNTING USING GEOGRAPHICALLY WEIGHTED REGRESSION-KRIGING CASE STUDY: STUNTING IN EAST JAVA

Geographically Weighted Regression Kriging (GWRK) is a special case of Geographically Weighted Regression (GWR) model, which is modeling with the effect of spatial autocorrelation on the GWR model error. The purpose of this research is to obtain a GWRK model between the factors that affect stunting density for each site viewed from the district center point in East Java Province and to make a prediction map based on the GWRK modeling. The data used was obtained from Basic Health Research (RISKESDAS) and the East Java Health Profile Book for 2021. The units of observation in this study were 38 districts in East Java.. Based on the GWR modeling results, it was found that the GWR model error contained spatial autocorrelation so that GWR model can be formed. From the GWRK modeling using stunting prevalence data in East Java in 2021, it was found that the GWR model was better than the global regression. Through prediction and prediction mapping formed from the GWR-Kriging modeling, it could be seen that stunting in regencies in East Java was evenly distributed . The interpolation map showed that the stunting forecasting values using the Kriging GWR interpolation ranged from 27% to 46%.

Spatial Variability of Soil Erodibility at the Rhirane Catchment Using Geostatistical Analysis

Soil erodibility is one of the most crucial factors used to estimate soil erosion by applying modeling techniques. Soil data from soil maps are commonly used to create maps of soil erodibility for soil conservation planning. This study analyzed the spatial variability of soil erodibility by using a digital elevation model (DTM) and surface soil sample data at the Rhirane catchment (Algeria). A total of 132 soil samples were collected of up to 20 cm in depth. The spatial distributions of the K-value and soil physical properties (permeability, organic matter, and texture) were used to elaborate ordinary Kriging interpolation maps. Results showed that mean values of soil organic matter content were statistically different between Chromic Cambisols (M = 3.4%) vs. Calcic Cambisols (M = 2.2%). The analysis of variance of the organic matter provided a tool for identifying significant differences when comparing means between the soil types. The soil granulometry is mainly composed of silt and fine sand. The soil erodibility showed values varying between 0.012 and 0.077 with an average of 0.034, which was greater in soils with calcic horizons. Statistical evaluation by using Pearson’s correlation revealed positive correlations between erodibility and silt (0.63%), and negative correlations with sand (−0.16%), clay (−0.56%), organic matter (−0.32%), permeability (−0.41%), soil structure (−0.40%), and the soil stability index (−0.26%). The variability analysis of the K-factor showed moderate spatial dependency with the soil erodibility map indicating moderate to highly erodible risk in cropland and sparse grassland land uses. Overall, the study provides scientific support for soil conservation management and appropriate agricultural food practices for food supply.

P‐4.2: An Image Pre‐distortion Algorithm for Near‐eye Display

With the development of near-eye display technology, optical distortion is often introduced into the design of near-eye optical system to meet people's requirements for high definition and large field of view. However, the larger the field of view, the greater the distortion of the projected image. In order to correct distorted images, this paper proposes an image pre-distortion algorithm based on nonlinear fitting and forward mapping interpolation. The radial basis function fitting algorithm is used to get a more precise mapping relationship between the original image and the pre-distorted image. The compactly supported radial basis function (CSRBF) interpolation algorithm based on forward mapping is used for image gray reconstruction to obtain better correction effect of image distortion. The experimental results show that the fitting error of the radial basis function fitting algorithm can be reduced to less than 1 pixel, reaching sub-pixel level. The pre-distortion algorithm has a good correction effect on the non-rotational symmetry distortion produced by the near-eye optical system, which makes the display image clear and complete.

Estimated Worldwide Prevalence of the PI*ZZ Alpha-1 Antitrypsin Genotype in Subjects With Chronic Obstructive Pulmonary Disease

IntroductionThe prevalence of α1-antitrypsin PI*ZZ genotypes in patients with COPD is only partially known. We aimed to estimate this prevalence worldwide. MethodA systematic review of the literature was conducted for studies investigating the prevalence of COPD and the prevalence of severe alpha-1-antitrypsin deficiency (AATD) PI*ZZ genotype. Results are shown in tables and on a whole world interpolation map. ResultsStudies from 48 countries with available data (21 from Europe, 9 from the Americas, 5 from Africa, 11 from Asia and 2 from Australasia) were selected. About 235,000 individuals with PI*ZZ genotypes were accounted: 50% in Europe, 37% in America, 9% in Asia, 3% in Australasia and 1% in Africa. The estimated crude prevalence of COPD in adults older than 40 years was 12.45% in Europe, 13.51% in America, 13.22% in Africa, 11.70% in Asia and 11.86% in Australasia. The highest PI*ZZ weighted average prevalence among COPD subjects (expressed as 1/x [95% confidence intervals]) were found in Northern Europe (395 [252–576]) followed by Western (797 [538–1165]), Southern (944 [600–1475]) and Central Europe (1096 [687–1738]). Outside Europe, high values were found in Australia–New Zealand (1007 [684–1509]), Saudi Arabia (1276 [563–2961]), United States (1298 [1094–1540]), Canada (1482 [1057–2083]) and Thailand (1807 [717–4692]). In the rest of the world, prevalence was significantly lower, especially in vast regions of Asia and Africa where the PI*Z gene is practically non-existent. ConclusionsSevere AATD is associated with a significant number of cases of COPD, especially in Europe, USA, Canada, New Zealand and Australia.

Characteristics of Soil Heavy Metal Pollution and Health Risk Assessment in Urban Parks at a Megacity of Central China

In this study, we compared the concentrations of the heavy metals Cd, Cr, Cu, Zn, Ni, and Pb in the surface soils of urban parks in Wuhan, Hubei Province, with those in the surface soils of urban parks worldwide. The soil contamination data were assessed using enrichment factors and spatial analysis of heavy metals using inverse distance weighting and quantitative analysis of heavy metal sources with a positive definite matrix factor (PMF) receptor model. Further, a probabilistic health risk assessment of children and adults using Monte Carlo simulation was performed. The average Cd, Cr, Cu, Zn, Ni, and Pb concentrations in the surface soils of urban parks were 2.52, 58.74, 31.39, 186.28, 27.00, and 34.89 mg·kg-1, respectively, which exceeded the average soil background values in Hubei. From the inverse distance spatial interpolation map, heavy metal contamination was primarily observed to be present to the southwest of the main urban area. The PMF model resolved four sources: mixed traffic and industrial emission, natural, agricultural, and traffic sources, with relative contributions of 23.9%, 19.3%, 23.4%, and 33.4%, respectively. The Monte Carlo health risk evaluation model demonstrated negligible noncancer risks for both adult and child populations, whereas the health effects of Cd and Cr on children were a concern for cancer risks.

이미지 생성을 위한 특징 선택 기반 이진 가중치 보간맵

실제 환경에서의 얼굴 인식 시스템은 개인별로 오직 한 장의 이미지만을 사용할 수 있다. 본 논문에서는 무표정한 중립 이미지로부터 다양한 변이를 포함하는 새로운 영상을 생성하기 위한 방법을 제안한다. 가중치 보간맵은 BDA 특징 공간에서 중립 이미지와 변이 이미지들을 분류하는데 기여도가 높은 위치의 특징들만을 선별함으로써 생성된다. 이미지 생성 단계에서는 중립 이미지에서 변이와 관련되는 픽셀 위치의 값을 보조 데이터 셋의 픽셀값으로 대체한다. 결과적으로 생성된 이미지는 중립 이미지의 고유한 특성을 최대한 유지하는 동시에 변이를 포함한다. 실험에서는 각각의 이미지 생성 방법들로부터 생성된 이미지들을 동일한 얼굴 인식 모델의 학습 데이터로 사용했다. 실험결과, 제안한 방법은 기존의 방법들과 비교하여 우수한 얼굴 인식 성능을 나타냈다.

Pollution and Risk Evaluation of Toxic Metals and Metalloid in Water Resources of San Jose, Occidental Mindoro, Philippines

Clean and safe drinking water is an integral part of daily living and is considered as a basic human need. Hence, this study investigated the suitability of the domestic water (DW) and groundwater (GW) samples with respect to the presence of metals and metalloid (MMs) in San Jose, Occidental Mindoro, Philippines. The MMs analyzed in the area of study for DW and GW were Arsenic (As), Barium (Ba), Copper (Cu), Chromium (Cr), Iron (Fe), Lead (Pb), Manganese (Mn), Nickel (Ni), and Zinc (Zn). The results revealed that Pb has the mean highest concentration for DW, while Fe is in GW resources in the area. Quality evaluation of DW and GW was performed using Metal Pollution Index (MPI), Nemerow’s Pollution Index (NPI), and Ecological Risk Index (ERI). The mean NPI value calculated for DW was 135 times greater than the upper limit of the unpolluted location category. The highest NPI observed was 1080 times higher than the upper limit of the unpolluted site category. That of the ERI observed in the area was 23.8 times higher than the upper limit for a “low” ERI category. Furthermore, the health risk assessment (HRA) of the GW and DW of the study area revealed non-carcinogenic health risks of the MMs analyzed in GW samples, and potential carcinogenic health risks from As, Cr, Pb, and Ni in DW. The use of machine learning geostatistical interpolation (MLGI) mapping to illustrate the PI and health risk (HR) in the area was an efficient and dependable evaluation tool for assessing and identifying probable MMs pollution hotspots. The data, tools, and the process could be utilized in carrying out water assessment, the evaluation leading to a comprehensive water management program in the area and neighboring regions of similar conditions.

Assessment of Scotopic Function in Rod-Cone Inherited Retinal Degeneration With the Scotopic Macular Integrity Assessment.

The scotopic macular integrity assessment (S-MAIA) can perform scotopic assessment to detect localized changes to scotopic rod and cone function. This study is an exploratory investigation of the feasibility of using the S-MAIA in a rod-cone dystrophy population to identify the pattern of loss in scotopic photoreceptor function. Twenty patients diagnosed with a rod-cone dystrophy underwent visual acuity testing, full-field stimulus threshold assessment, and multiple S-MAIA tests after dark adaptation periods of 20 minutes and 45 minutes performed separately. Only right eyes were tested. Three tests were performed following a learning test. A Bland-Altman analysis was used to assess repeatability and agreement between tests after the two time periods. Spatial interpolation maps were created from the group plots to display the pattern of rod and cone loss. Learning effects took place between testing sessions 1 and 2 but not 2 and 3. Limits of agreement were larger in the patient eyes than control eyes, but within previously reported values. Using longer adaptation time of 45 minutes did not offer a significant advantage over 20 minutes. Patterns for the cyan and red sensitivities were different, indicating different patterns of loss for rods and cones. A dark adaptation time of 20 minutes before testing is sufficient for thresholding. The S-MAIA is suitable for use in patients with a logarithm of the minimum angle of resolution vision of at least 0.7 and provides a viable outcome measure for patients with rod-cone dystrophies and preserved central vision. The spatial information about scotopic function from the S-MAIA provides information about disease processes and progression. There is a need for scotopic measures for use in clinical trials. Scotopic microperimetry works well in patients with early disease, allowing the extension of recruitment criteria for novel therapies of rod-cone dystrophies.

Analysis of Uncertainty in the Depth Profile of Soil Organic Carbon

The soil organic carbon (SOC) depth profile provides information for many applications, including monitoring climate change, carbon sequestration, reforestation, and land erosion. Models of the SOC profile support data interpolation, trend analysis, and carbon mapping, and can be used in larger pedometric models in support of carbon farming. Model errors may be due to statistical variability in discrete data and the limited sample size available for model calibration. Uncertainties in the model can arise from a process of iterative parameter adjustment and can be estimated by gradient-based methods or probabilistic methods. A comparison between Frequentist and Bayesian approaches to the construction of regression-based models revealed that the results were very similar when used for calibrating a model for the SOC profile. The model was applied to four representative regional sites in Victoria.

Coronavirus disease 2019 risk assessment of adults and geospatial mapping in selected communities of Southwest Nigeria.

Coronavirus disease 2019 (COVID-19) remained a worldwide public health problem. Risk assessment and mapping can be deployed to assist in the control and management of disease outbreaks. The aim of this study was to conduct COVID-19 risk assessment and mapping in selected communities of Southwest Nigeria. This was a cross-sectional study of adults, 18 years and above, involving the use of multi-stage sampling. Data collection was done with a pre-tested, structured, interviewer-administered questionnaire. The Statistical Package for the Social Sciences version 23 and Environmental Systems Research Institute ArcGIS desktop version 10.5 were used for data analysis and spatial mapping, respectively. The threshold for statistical significance was set at P < 0.05. The respondents' mean age was 40.6 ± 14.5 years. Self-reported vulnerability factors identified included hypertension, diabetes mellitus, working in hospital facility, cigarette smoking and age ≥60 years amongst others. About a quarter (20.2%) had a high risk of COVID-19 following risk quantification. The risk cuts across geographical locations and socio-economic status. Education was significantly associated with COVID-19 risk. The spatial interpolation map revealed that the farther a community was from the high-burden area, the lower the risk of COVID-19. There was a high prevalence of self-reported COVID-19 risk. Identified communities with COVID-19 high-risk burden in the risk mapping and those with stratified proximity to these areas need to be targeted by the government for a public health awareness campaign.

Inversion and Monitoring of the TP Concentration in Taihu Lake Using the Landsat-8 and Sentinel-2 Images

Eutrophication is a significant factor that damages the water ecosystem’s species balance. The total phosphorus (TP) concentration is a vital water quality indicator in assessing surface water eutrophication. This paper predicts the spatial distribution of TP concentration using remote sensing, measured data, and the partial least squares regression (PLSR) method. Based on the correlation analysis, the models were built and tested using the TP concentration and Sentinel-2 Multispectral Instrument (MSI) and Landsat-8 Operational Land Imager (OLI) image spectra. The results demonstrated that the best technique based on band combinations of the Sentinel-2 and Landsat-8 images achieved good precision. The coefficient of determination (R2), root mean square error of prediction (RMSEP), and residual prediction deviation (RPD) were 0.771, 0.023 mg/L, and 2.086 for Sentinel-2 images and 0.630, 0.032 mg/L, and 1.644 for Landsat-8 images, respectively. The TP concentration maps were interpolated using the inverse distance weighting method, and the inversion results obtained from the images were in good agreement. The western and northwestern regions of Taihu Lake, where significant cyanobacterial blooms occurred, had TP concentrations greater than 0.20 mg/L; nevertheless, the central and eastern regions had amounts ranging from 0.05 to 0.20 mg/L. In order to prove the extensibility of the model, the optimal algorithm was applied to the Sentinel-2 and Landsat-8 images in 2017. The optimal algorithm based on Landsat-8 images has a better verification effect (RMSEP = 0.027 mg/L, and R = 0.879 for one Landsat-8 image), and the optimal algorithm based on Sentinel-2 images has moderate verification effect (RMSEP = 0.054 mg/L and 0.045 mg/L, and R = 0.771 and 0.787 for two Sentinel-2 images). The interpolation and inversion maps are in good agreement, indicating that the model is suitable for the Landsat-8 and Sentinel-2 images, which can be complementary for higher temporal resolutions. Monitoring water quality using multiple remote sensing images can provide the scientific basis for water quality dynamic monitoring and prevention in China.

Integrating GIS techniques and lab analyses to assess the effect of sediments on the physical and chemical properties of soil and water in Um Naaj marsh, southern Iraq

A study for the evaluation of some physical and chemical properties of soil and water of certain sites in southern Iraqi marsh called Umm Naaj, a part of Al-Hawizeh Marsh, has been carried out using the integration between fieldwork, lab analyses, and GIS techniques. Sixteen water and soil samples were collected, then transferred to the lab. In the lab, some chemical properties of water samples were determined, while soil samples are subjected to some chemical and physical analysis. IDW method in GIS software had been utilized for interpolation of spatial distribution of chemical and physical properties of soil and water through the study area. Results of analysis and interpolation maps showed that the clay fraction was the dominant in soil samples, followed by silt and sand. Regarding chemical properties, all soil samples are an alkaline reaction. The percent of minerals for silt fraction indicate that calcite was the dominant mineral followed by quartz and illite. The x-ray diffraction analysis results indicated the presence of regular Mica-Smectite mixed layer mineral. The interpolation maps indicated that there is an effect of sediments on some properties in different levels where some properties were highly affected and some moderately and some have no effect on soil and water quality.

Simulation of strain induced abnormal grain growth in aluminum alloy by coupling crystal plasticity and phase field methods

A mesoscale modeling methodology is proposed to predict the strain induced abnormal grain growth in the annealing process of deformed aluminum alloys. Firstly, crystal plasticity finite element (CPFE) analysis is performed to calculate dislocation density and stored deformation energy distribution during the plastic deformation. A modified phase field (PF) model is then established by extending the continuum field method to consider both stored energy and local interface curvature as driving forces of grain boundary migration. An interpolation mapping approach is adopted to transfer the stored energy distribution from CPFE to PF efficiently. This modified PF model is implemented to a hypothetical bicrystal firstly for verification and then the coupled CPFE-PF framework is further applied to simulating the 2D synthetic polycrystalline microstructure evolution in annealing process of deformed AA3102 aluminum alloy. Results show that the nuclei with low stored energy embedded within deformed matrix tend to grow up, and abnormal large grains occur when the deformation is close to the critical plastic strain, attributing to the limited number of recrystallized nuclei and inhomogeneity of the stored energy.

Estimation of groundwater quality using an integration of water quality index, artificial intelligence methods and GIS: Case study, Central Mediterranean Region of Turkey

Groundwater is one of the most important natural resources in the world and is widely used for irrigation purposes. Groundwater quality is affected by various natural heterogeneities and anthropogenic activities. Consequently, monitoring groundwater quality and assessing its suitability are crucial for sustainable agricultural irrigation. In this study, the suitability of groundwater for irrigation was determined by using sodium adsorption ratio (SAR), residual sodium carbonate (RSC), Kelly index (KI), percentage of sodium (Na%), magnesium ratio (MR), potential salinity (PS) and permeability index (PI). The groundwater samples were collected and analyzed from 37 different sampling stations for this purpose. Along with suitability analysis, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models were used to predict irrigation water quality parameters. The models were evaluated by comparing the measured values and the predicted values using the statistical criteria [coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE) and Nash–Sutcliffe efficiency (NS)]. In the estimation of all irrigation water quality parameters, the ANN model has performed much higher compared with the ANFIS model. Spatial distribution maps were generated for measured and ANN model-estimated irrigation water quality indices using the IDW interpolation method. Spatial distributions of groundwater quality indices revealed that MR was higher than the allowable limits in most of the study areas and the other quality criteria were within the permissible limits. It has been determined that the interpolation maps obtained as a result of artificial intelligence methods have appropriate sensitivity when compared with the observed maps. Based on the present findings, ANN models could be used as an efficient tool for estimating groundwater quality indices in unsampled sections of the study area and the other regions with similar conditions.

Current radioactive fallout contamination along a trans-European gradient assessed using terricolous lichens.

Lichens are considered to be good indicators of contamination of the terrestrial environment. In this study, we investigated the level of 137Cs and 4K accumulated by Cladonia arbuscula and Stereocaulon alpinum along a longitudinal gradient from northern Norway, across Sweden to southern Poland. Additionally, we compared isotope contents between the selected lichen species, and investigated the correlation of the 137Cs content accumulated by C. arbuscula with 137Cs fallout after the Chernobyl disaster. The activity of 137Cs varied from 3.58 Bq kg-1 to 559 Bq kg-1 for S. alpinum, and from 1.18 Bq kg-1 to 130 Bq kg-1 for C. arbuscula. The activity of 4K ranged from 114 Bq kg-1 to 341 Bq kg-1 for S. alpinum and from 27.2 Bq kg-1 to 314 Bq kg-1 for C. arbuscula. The 137Cs content did not differ between C. arbuscula and S. alpinum; however, the difference between species was significant for 4K accumulation. The activity of 137Cs in C. arbuscula was significantly correlated with deposition from 1986. Based on our findings we created a spatial map of 137Cs activity in lichens measured 30 years after the event that was the primary source of this isotope. We showed that C. arbuscula can be used to assess contamination and create interpolation maps of radionuclide deposition, even if the primary deposition took place many years ago.

Enhanced Radio Map Interpolation Methods Based on Dimensionality Reduction and Clustering

The received signal strength (RSS) based Wi-Fi fingerprinting method is one of the most potential and easily deployed approaches for a reliable indoor positioning system. However, due to the labor intensive and time-consuming radio map construction process, interpolation is often incorporated. To ensure the interpolated radio map is robust against environmental noise and RSS fluctuations, we propose two novel interpolation methods, termed as DimRed and DimRedClust, for an improved radio map construction. The former performs dimensionality reduction prior to the interpolation while the latter employs both the dimensionality reduction and clustering before interpolating the radio map. For dimensionality reduction, principal component analysis (PCA) or truncated singular value decomposition (TSVD) is adopted to profoundly extract essential features from the RSS data while the K-means algorithm is used to partition the reference points (RPs) into several clusters. Subsequently, the RSS for all virtual points are interpolated via inverse distance weighting (IDW). Numerical results based on the real-world multi-floor multi-building dataset confirm the supremacy of the proposed schemes over the baseline IDW interpolation. Compared to the baseline IDW, the proposed PCA-K-means-IDW, TSVD-K-means-IDW, PCA-IDW, and TSVD-IDW could attain a performance gain in terms of average positioning error of up to 30.17%, 30.93%, 19.33%, and 21.61%, respectively.

Impact of Climate and Land-Use Change on Groundwater Resources, Study of Faisalabad District, Pakistan

Groundwater depletion has become a major concern all over the world. Recently, the rapid population growth and need for water and food have placed a massive strain on land and water resources. In this study, groundwater depletion resulting from land-use and climate change was investigated in the Faisalabad district, Pakistan, from 2000 to 2015. A Pearson correlation analysis between climatic parameters and land-use indices with groundwater was conducted to explore the major influencing factors. Interpolation maps of groundwater were generated using the inverse distance weighting interpolation (IDW) method. The Normalized Difference Built-up Index (NDBI) of five-year intervals demonstrated a strong increasing trend, whereas the Normalized Difference Vegetation Index (NDVI) presented a declining trend. The results also indicated a significant declining trend in groundwater levels in the region, with the annual average groundwater level decreasing at a rate of approximately 0.11 m/year. Climatic parameters (i.e., precipitation and temperature) further reveal an insignificant increasing trend estimated using the Mann–Kendall test and Sens’s slope. Overall, spatial analysis results showed a statistically significant positive trend in the groundwater level of the Faisalabad district, where the NDBI ratio is high and the NDVI is low, owing to the extensive extraction of groundwater for domestic and industrial use. These findings may be useful for a better understanding of groundwater depletion in densely populated areas and could also aid in devising safety procedures for sustainable groundwater management.