Geographic Information System Interpolation Research Articles

Study and Analysis on Groundwater Modelling in Watershed Area of the Wainganga River Gondia District Using GIS Techniques

Currently, Geographic information systems (GIS) are the most significant mapping and modelling tool for groundwater analysis.The determination of the groundwater level of In a few years, the availability of groundwater will be a problem in Kati Village, Gondia District, Maharashtra, India, a case study Wainganga River watershed area pre-monsoon and post-monsoon analysis.The monsoon rains in Gondia District are concentrated in the four months of June to September, with 90.81% rainfall, 1.86% post-monsoon, 4.83% pre-monsoon, and 2.48% winter. The annual rainfall distribution in Gondia is very irregular. The main river is Wainganga, and the tributaries are Bagh.The water has a neutral to alkaline pH range of 6.6 to 8.92 and a high TDS range of 140 to 2184 ppm. The suitability of the groundwater level must be established before using GIS interpolation model techniques to evaluate it. Groundwater level monitoring, hydrology, and other fields increasingly make use of geospatial techniques for Geographic information systems (GIS). Using this method of groundwater level monitoring to gather information on water level potential position conditions in the study area is crucial for effective analysis, prediction, and validation. The result is reached through statistically significant analysis of physicochemical parameters and groundwater modelling using GIS. This study's objective is to use GIS techniques to analyse groundwater modelling in the Wainganga River Gondia District.The information can be used in the future for studies on area management, resource conservation, and restoration.

Spatial Analysis for Surface Water Quality Assessment of the Ikpoba River Using Geographic Information System

The aim of this study is to assess the water quality of the surface water body in the study area (Ikpoba River) in Benin City using the Geographic Information System (GIS). The present research involved the spatial analysis of water quality for four stations along the Ikpoba River in Benin City. The four stations were delineated using satellite images of Google Earth. At the same time, water samples were collected at these stations and the parameters such as Temperature, Turbidity, pH, Total Dissolved solids (TDS), Total Suspended Solids (TSS), Dissolved Oxygen (DO), COD, Biochemical BOD, Electrical Conductivity (EC), Zn, Cl, NH4N, Ca, Pb, SO4, Fe, Mg, NO3, PO4, and Total Hardness were analyzed in the laboratory. The analytical result of water samples was compared with WHO (World Health Organization) maximum permissible limits for the parameters tested and it was found that DO, TDS, EC, pH, NH4N, Zn, Ca, Mg, NO3, and Cl all fell below the WHO standard at all four points along the Ikpoba river. Inverse Distance Weighted (IDW) was employed for the spatial analysis using ArcGIS software. The database for all spatial data was created using GIS interpolation according to the IDW method to obtain the spatial distribution of each selected water quality parameter. It was estimated and concluded that the quality of water from the four different stations on the river varies comparatively with the permissible limits given by WHO Standards, with only a few parameters falling below the acceptable limit and the spatial extent of water quality is changing with respect to the parameter and its values.

Identifying Exposure of Urban Area to Certain Seismic Hazard Using Machine Learning and GIS: A Case Study of Greater Cairo

The 1992 Cairo earthquake, with a moment magnitude of 5.8, is the most catastrophic earthquake to shock the Greater Cairo (GC) in recent decades. According to the very limited number of seismological stations at that time, the peak ground acceleration (PGA) caused by this event was not recorded. PGA calculation requires identification of nature of the earthquake source, the geologic setting of the path between the source and site under investigation and soil dynamic properties of the site. Soil dynamic properties are acquired by geotechnical investigations and/or geophysical survey. These two methods are costly and are not applicable in regional studies. This study presents an adaptive and reliable PGA prediction model using machine learning (ML) along with six standard geographic information system (GIS) interpolation methods (IDW, Kriging, Natural, Spline, TopoToR, and Trend) to predict the spatial distribution of PGA caused by this event over the GC. The model is employed to estimate the exposure of the urban area and population in the GC based on the available geotechnical and geophysical investigations. The exposure (population) data is from free and easy-access sources, e.g., Landsat images and the Global Human Settlement Population Grid (GHS-POP). The results show that Natural, Spline, and Trend are not suitable GIS interpolation techniques for generating seismic hazard maps (SHMs), while the Kriging Method shows sufficient prediction. Interestingly, with an accuracy of 96%, the ML model outperforms the classical GIS methodologies.

Forecasting future climate boundary maps (2021–2060) using exponential smoothing method and GIS

Future-oriented forecasts have an important place in making forward-looking decisions and planning. At the beginning of these studies is the monitoring and detection of climate change. The climate is very variable. Therefore, by making predictions about the climate, preliminary information about how and to what extent the climate will change can be obtained, and accordingly, necessary precautions can be taken quickly. This study aims to produce predictive climate boundary maps using Geographic Information Systems (GIS), in which climate classification methods and time series methods are evaluated to monitor and determine the changes caused by the climate in 13 selected provinces in Turkey. The triple exponential smoothing method and the Erinc climate classification method were discussed. The data were obtained from the General Directorate of Meteorology (GDM) between 1930 and 2020, and each year's precipitation efficiency index (Im) of the Erinc climate classification method was calculated. It is divided into two classes for forecasting and testing current indices: test Im indices (1930–2014) and forecast test Im indices (2015–2020). MAD, MSE, and MAPE criteria were calculated to determine whether the Im estimates were meaningful. However, the accuracy of the estimates was ensured by considering the MAPE criteria for this study. After this stage, the analyses were performed again with test Im indices (1930–2020) and forecast Im indices (2021–2060), and Im indices predictions for the future were made. Finally, the obtained forecast indices were subjected to GIS interpolation analyses (Kriging and IDW), and future climate boundary maps were produced. Thanks to the outputs obtained from the study, how the climate classes of any region will be in the future and to what extent they will change will be provided by evaluating the climate classification and time series methods together. It will contribute to different studies in this field with its innovative analysis approach.

Use of GIS based Inverse Distance Weighted interpolation to assess surface water quality: Case of Wadi El Bey, Tunisia

Sustainable management of water resources involves inventory, conservation, efficient utilization, and quality management. Although, activities relating to quantity assessment and management in terms of river discharge and water resources planning are given attention at the pollution level of the watershed, water quality assessment are still being done at specific locations of major concern. The use of Geographical Information System (GIS) based on water quality information system and spatial analysis with Inverse Distance Weighted (IDW) interpolation enabled the mapping of water quality indicators in watershed of Wadi El Bey, Tunisia. Using 13 sampling locations, water quality indicators were monitored from 2012 to 2016; covering full hydrological season. Maps of spatio-temporal variations in some physical parameters (Temperature, pH), chemical (chemical oxygen demand, Biochemical oxygen demand, total suspended solids...) and microbiological ones ( Fecal​ coliform, Escherichia coli, Staphylocoques ...) were used to evaluate the water quality across monitoring stations and analyze the sources of water pollution along the river courses. The production of water quality maps will improve monitoring, enforce the standards and regulations towards better pollution management and control. Patterns were clearly defined; methods were statistically valid and the IDW predictions were made to exhibit high prediction. Thus, the spatial distribution maps show that the agricultural activities, domestic, and industrial discharges are fundamental causes of water pollution in the study area specially the industrial zone of Grombalia. The results of IDW helped us to identify key areas requiring control in the Wadi el Bey River, which pointed the way for further delicacy management of the river. • The present investigation highlights the assessment of Wadi El Bey water quality. • Spatiotemporal variation of pollutants was assessed using GIS and IDW interpolation. • Models were statistically valid and the IDW was observed to exhibit high prediction. • Study area was highly contaminated by the Grombalia industrial discharges. • Water quality maps enforce regulations for better pollution management and control.

GIS interpolation is key in assessing spatial and temporal bioremediation of groundwater arsenic contamination

Arsenic (As) contamination in groundwater is a global crisis that is known to cause cancers of the skin, bladder, and lungs, among other health issues, and affects millions of people around the world. Due to the time and financial constraints associated with establishing in-depth monitoring programs, it is difficult to monitor and map arsenic concentrations over time and across large areas. The goal of this study was to determine the most accurate Geographic Information Systems (GIS) interpolation method for mapping the effects of bioremediation on groundwater arsenic sequestration across a local-scale study area in northwest Florida (~900 m2) over the duration of a nine-month period (pre-injection, one-month post-injection, and nine-months post-injection). We used groundwater data collected from 2018 to 2019 to visualize arsenic contamination over time. Measured arsenic concentrations from 23 wells were grouped into three categories: (1) decreasing, (2) fluctuating, or (3) largely unaffected by the bioremediation procedure. The accuracy of three interpolation methods was also investigated: Inverse Distance Weighted (IDW), Ordinary Kriging (OK), and Empirical Bayesian Kriging (EBK). Statistical results using the leave-one-out cross validation (LOOCV) process showed that OK consistently provided the most accurate predictions of arsenic concentrations across space and time ([Root Mean Square Error (RMSE) = 0.265] and accurately predicted regulatory arsenic concentrations below 0.05 mg/L in nine of 11 wells, while IDW and EBK only accurately predicted four and five wells, respectively. While it was shown that OK tends to underpredict arsenic maxima, this did not affect the overall accuracy of the interpolation compared to results from EBK (RMSE = 0.297) and IDW (RMSE = 0.272). Overall, these interpolations aided in the interpretation of the extent of bioremediation, revealing the need for repeated injections to continuously remove arsenic from the groundwater. The study will provide guidance and evaluation methods for international and governmental organizations, industrial companies, and local communities on how to understand spatial and temporal distributions of arsenic contamination and inform bioremediation efforts at various scales in the future.

Analysis of Criminal Spatial Events in GIS for predicting hotspots

The crime rate increasing in developing countries cause of the unequal distribution of psychological, economic situation. This research aims to identify the crime mapping and investigate the hotspots and analyzing the spatial crime dataset and the predict of Spatio-temporal hotspot in Baltimore city for a period from 2012 to 2018. Analyzing crime data using data mining algorithms and The Geographic Information System (GIS) of Geographic dataset visualize and it possible for law enforcement to detect spatial crime patterns map easy and flexible and different analysis to identify the crime hotspot region efficiently. analysis crime hotspot using GIS is a useful way to the recognition for crime pattern and predicting hotspot over spatial correlation, analysis spatial data and revile crime pattern future detection. using spatial correlation, the G* statistic has been done with hotspot analysis the Getis-Ord Gi* to find the result of the spatial statistics pattern. analysis the crime to predict hotspot uses spatial variation and density crimes for clarifying the positions of statistically significant crime predict hotspots and cold spots and GIS interpolation method is used for more efficient visualization. This research using Grid network hotspots are applied to the crime data of Baltimore, Maryland state to recognize the hotspots for crime data like Shooting, Homicide and Assault by threat.

Geospatial Distributions of Groundwater Quality in Gedaref State Using Geographic Information System (GIS) and Drinking Water Quality Index (DWQI).

The observation of groundwater quality elements is essential for understanding the classification and distribution of drinking water. Geographic Information System (GIS) and remote sensing (RS), are intensive tools for the performance and analysis of spatial datum associated with groundwater sources control. In this study, groundwater quality parameters were observed in three different aquifers including: sandstone, alluvium and basalt. These aquifers are the primary source of national drinking water and partly for agricultural activity in El Faw, El Raha (Fw-Rh), El Qalabat and El Quresha (Qa-Qu) localities in the southern part of Gedaref State in eastern Sudan. The aquifers have been overworked intensively as the main source of indigenous water supply in the study area. The interpolation methods were used to demonstrate the facies pattern and Drinking Water Quality Index (DWQI) of the groundwater in the research area. The GIS interpolation tool was used to obtain the spatial distribution of groundwater quality parameters and DWQI in the area. Forty samples were assembled and investigated for the analysis of major cations and anions. The groundwater in this research is controlled by sodium and bicarbonate ions that defined the composition of the water type to be Na HCO3. However, from the plots of piper diagram; the samples result revealed (40%) Na-Mg-HCO3 and (35%) Na-HCO3 water types. The outcome of the analysis reveals that several groundwater samples have been found to be suitable for drinking purposes in Fa-Rh and Qa-Qu areas.

Assessment of spatial distribution of aridity indices in Raya valley, northern Ethiopia

Aridity is a risk with the threat of desertification because of erratic precipitation and prolonged droughts. Aridity indices are often used for the climate-based land classification and monitoring droughts. Hence, this study aimed at analysis of the spatial distribution of aridity indices in Raya valley, northern Ethiopia. Meteorological data from eight meteorological stations located throughout the study area were used to compute the three aridity indices employed: the De Martonne aridity index (IDM), the Pinna combinative index (IP), and the Food and Agriculture Organization aridity index (IFAO). Each aridity index value was computed at each station. Then, once the statistical properties of each aridity index at each station were assessed, inverse distance-weighted geographic information system interpolation technique was used for the spatial estimation of the aridity indices. The three indices indicated a high coefficient of determination which in common revealed the area prone to dry and semi-dry risk. However, IDM was found more appropriate for the considered area since it defines more the climate of each location using seven classes, and it can be computed at specific season and month. The annual, seasonal, and monthly aridity indices of IDM showed high spatial variability of aridity index. The spatial distribution of the IDM underlined irrigation requirements in the northern east part of the study area. This analysis may be helpful for the utilization of water resources and irrigation systems of the study area.

Spatial Model of Sky Brightness Magnitude in KUSZA Observatory, UniSZA

Light pollution is the by-product of anthropogenic activities result from the increases in advanced economic and urbanization. Light pollution limits the ability to view the night sky by astronomers, on biodiversity, and ecosystems. This technology nowadays has led us to increase the convenient method to observe the brightness of night sky to determine how much the night sky polluted by artificial light. The study used SQM device and monitored the sky brightness of the KUSZA Observatory in Merang, Setiu, Terengganu, Malaysia. The results obtained were used to compare with the results obtained by simulation data generated from Earth Observation Group (EOG) of National Oceanic and Atmospheric Administration (NOAA), National Geophysical Data Center. It is the first world atlas of the artificial night sky brightness using GIS Interpolation tools. The result showed that the Kusza Observatory Merang is still appropriate to be the place of observation of celestial objects to either optical or radio astronomers. Hence, we recommended that through the data obtained from Defense Meteorological Satellite Program (DMSP) satellites, the value of sky brightness from all over the world can be used to make further analysis and to increase the precision of data recorded through observations. This study provides a clear view mapping of the accessing the sky condition using Geographic Information System (GIS) analysis in the study area.

Developing a GIS-based Fuzzy AHP Model for Selecting Solar Energy Sites in Shodirwan Region in Iran

The objective of this study is to use a Fuzzy Analytic Hierarchy Process (Fuzzy AHP) and geographical mapping models using Geographical Information System (GIS) to locate the most appropriate sites for solar energy farms in Shodirwan region in Iran. GIS interpolation showed that annual solar insolation in Shodirwan is very good and can be used for potential solar farm locations. The average of solar insolation in the region is 5.12 kWh/m2/day annually. Results showed that 18.25% of the Shodirwan area is exploitable as solar farms. With a conversion efficiency of 10% and area factor of 70%, annual electricity production for the exploitable area is roughly 16100 GWh. Land suitability analysis for solar farms implementation was carried out and overlay results obtained from the analysis of the resultant maps showed that 13.98% and 3.79% of the total land area demonstrate high and good suitability levels, respectively. The total electricity generation potential from both highly and good suitability levels in Shodirwan region was about 15,690 GWh annually.

항공 전투 시뮬레이션의 합성전장환경 구축을 위한 환경 데이터 관리 및 공급 방안

In this paper, there is a research for providing environmental data to reflect environmental effects to the simulation for the aviation weapon systems by the construction of the synthetic battlefield. The results of the aviation engagement simulation are able to differ by environmental effect. This paper analyzes the real aviation battlefield and designs the synthetic battlefield based on the analysis. In order to construct the designed synthetic battlefield, we collects the real environmental data for the atmosphere and structures the collected data using GIS (Geographic information system interpolation). The main objective of this paper is to design the synthetic battlefield based on the derived environmental factors from the analysis of the real aviation battlefield, and it constructs the designed synthetic battlefield by the collection of real atmosphere data. The constructed synthetic battlefield provides the environmental data which are requested from the distributed simulation system, and it makes the system reflect environmental effects to the simulation.

English

The scientific evidence on rainfall variability with its significant impacts on crop yield is now stronger than ever. It is even more so on maize that serves as staple food in most parts of Sub-Sahara Africa. Hence, this study aim at examines and map spatio-temporal impacts of rainfall variability on water availability for maize yield using Geographic Information System (GIS). Major regions where maize is highly produced in Nigeria were selected and rainfall data for 30 years (1970 to 2000) were used for the Geospatial analysis. GIS Interpolation and other geospatial Analysis techniques were carried out to map the impacts of rainfall variability on maize yield. The results of the analysis and the maps produced show that inter-annual rainfall variability consequently affect the rate of maize yield in Nigeria.

Changes in the spatial distribution of subtidal macrobenthos due to predation by white shrimp (Litopenaeus setiferus)

Manipulative caging experiments were conducted in North Inlet, South Carolina, to measure the predatory effect of juvenile penaeid white shrimp,Litopenaeus setiferus, on their subtidal macrobenthic prey. We used the natural neighbor interpolation procedure within a Geographic Information System (GIS) to map macrobenthos distributions at both the start and end of the cage deployments. Moran’s I, a commonly used index of spatial autocorrelation, provided a quantitative metric for evaluating the statistical significance of the observed changes. We tested the hypothesis that juvenile white shrimp are optimal foragers by assessing whether their predatory behavior was targeted at higher density macrobenthos patches inside the enclosures, resulting in a more homogeneous distribution of prey after seven days. Since large changes in patchiness could occur over seven days without incurring a significant change in index value, we treated each index as a continuous measure of patchiness, and examined whether the value increased or decreased consistently among treatment replicates. Using Moran’s I, the abundance and spatial distribution of macrobenthos inside control, partial, open, and shrimp inclusion treatments varied in their response. After seven days, decreased patchiness was consistently observed in the high density shrimp treatment replicates, and increased patchiness in the open plots. The GIS natural neighbor interpolation created a succinct visual representation of dramatic changes in prey spatial distribution and prey densities throughout each cage. The GIS interpolation conveyed the dynamic nature of the spatial variability that would not have been evident by calculation of Moran’s I alone. Although we could only weakly support our hypothesis, the combination of visual interpolation methods with index calculations has great potential for gaining further insights into the role of different factors as they affect changes in spatial distribution of benthic infauna.

Rice production and climate change: design and development of a GIS database to complement simulation models

A cooperative project between the International Rice Research Institute in Los Banos, Philippines, and the U.S. EPA Environmental Research Laboratory in Corvallis, Oregon, was initiated to estimate how rice yield in Asia might be affected by future climate change and enhanced UV-B irradiance following stratospheric ozone depletion. A radiative transfer model was used to estimate daily UV-B irradiance levels using remotely sensed ozone and cloud cover data for 1274 meteorological stations. A rice yield model using daily climatic data and cultivar-specific coefficients was used to predict changes in yield under given climate change scenarios. This paper gives an overview of the data required to run these two models and describes how a geographical information system (GIS) was used as a data pre- or postprocessor. Problems in finding reliable datasets such as cloud cover data needed for the UV-B radiation model and radiation data needed for the rice yield model are discussed. Issues of spatial and temporal scales are also addressed. Using simulation models at large spatial scales helped identify weaknesses of GIS data overlay and interpolation capabilities. Even though we focussed our efforts on paddy rice, the database is not intended to be system specific and could also be used to analyze the response of other natural systems to climatic change.