scholarly journals Comparison of interpolation methods based on Geographic Information System (GIS) in the spatial distribution of seawater intrusion

2020 ◽  
Vol 20 (2) ◽  
pp. 24-30
Author(s):  
MAULINA TANJUNG ◽  
SAUMI SYAHREZA ◽  
MUHAMMAD RUSDI
Keyword(s):  
Electrical Conductivity ◽  
Spatial Distribution ◽  
Seawater Intrusion ◽  
Coastal Area ◽  
Polynomial Interpolation ◽  
Interpolation Method ◽  
Coefficient Of Determination ◽  
Interpolation Methods ◽  
Empirical Bayesian Kriging ◽  
Banda Aceh

The study of monitoring seawater intrusion and groundwater quality in a coastal area needs to be done regularly to prevent the clean water crisis problems in the future. Accurate and reliable interpolation of seawater intrusion over a region is the requirement of an efficient monitoring. In this study, different interpolation methods were investigated and compared to determine the best interpolation method for predicting the spatial distribution of seawater intrusion in the coastal area of Banda Aceh. Groundwater electrical conductivity (EC) was analyzed to identify the contamination of seawater intrusion into the coastal aquifers. Four interpolation methods such as Empirical Bayesian Kriging (EBK), Global Polynomial Interpolation (GPI), Inverse Distance Weighting (IDW), and Local Polynomial Interpolation (LPI), were used to create the spatial distribution of the groundwater electrical conductivity. The accuracy of interpolation methods was evaluated by using a cross-validation technique through the coefficient of determination (R2) and the Root Mean Square Error (RMSE). The results showed that IDW performed the most accurate prediction values and the best surface which were indicated by the least RMSE and the highest R2 value. It can be concluded that IDW interpolation method is the best method for interpolating the groundwater electrical conductivity associated with seawater intrusion in the coastal area of Banda Aceh.

scholarly journals Spatial Analysis of Seasonal Precipitation Using Various Interpolation Methods in the Euphrates Basin, Turkey

2021 ◽  
Author(s):  
Okan Mert Katipoğlu
Keyword(s):  
Spatial Distribution ◽  
Geographical Information Systems ◽  
Polynomial Interpolation ◽  
Estimation Method ◽  
Summer Precipitation ◽  
Winter Precipitation ◽  
Geographical Information ◽  
Seasonal Precipitation ◽  
Interpolation Methods

Abstract It is vital to accurately map the spatial distribution of precipitation, which is widely used in many fields such as hydrology, climatology, meteorology, ecology, and agriculture. In this study, it was aimed to reveal the spatial distribution of seasonal long-term average precipitation in the Euphrates Basin by using various interpolation methods. For this reason, Simple Kriging (SK), Ordinary Kriging (OK), Universal Kriging (UK), Ordinary CoKriging (OCK), Empirical Bayesian Kriging (EBK), Radial Basis Functions (Completely Regularized Spline (CRS), Thin Plate Spline (TPS), Multiquadratic, Inverse Multiquadratic (IM), Spline with Tensor (ST)), Local Polynomial Interpolation (LPI), Global Polynomial Interpolation (GPI), Inverse Distance Weighting (IDW) methods have been applied in the Geographical Information Systems (GIS) environment. Long-term seasonal precipitation averages between 1966 and 2017 are presented as input for the prediction of precipitation maps. The accuracy of the precipitation prediction maps created was based on root mean square error (RMSE) values obtained from the cross-validation tests. The method of precipitation by interpolation yielding the lowest RMSE was selected as the most appropriate method. As a result of the study, OCK in spring and winter precipitation, LPI in summer precipitation, and OK in autumn precipitation were determined as the most appropriate estimation method.

scholarly journals Comparison of spatial interpolation methods for estimating the precipitation distribution in Portugal

2021 ◽  
Author(s):  
Alexandru Antal ◽  
Pedro M. P. Guerreiro ◽  
Sorin Cheval
Keyword(s):  
Spatial Distribution ◽  
Spatial Interpolation ◽  
Polynomial Interpolation ◽  
Annual Precipitation ◽  
Mean Annual Precipitation ◽  
Precipitation Distribution ◽  
Interpolation Methods ◽  
Mainland Portugal ◽  
The Mean ◽  
Spatial Interpolation Methods

Abstract Precipitation has a strong and constant impact on different economic sectors, environment, and social activities all over the world. An increasing interest for monitoring and estimating the precipitation characteristics can be claimed in the last decades. However, in some areas the ground-based network is still sparse and the spatial data coverage insufficiently addresses the needs. In the last decades, different interpolation methods provide an efficient response for describing the spatial distribution of precipitation. In this study, we compare the performance of seven interpolation methods used for retrieving the mean annual precipitation over the mainland Portugal, as follows: local polynomial interpolation (LPI), global polynomial interpolation (GPI), radial basis function (RBF), inverse distance weighted (IDW), ordinary cokriging (OCK), universal cokriging (UCK) and empirical Bayesian kriging regression (EBKR). We generate the mean annual precipitation distribution using data from 128 rain gauge stations covering the period 1991 to 2000. The interpolation results were evaluated using cross-validation techniques and the performance of each method was evaluated using mean error (ME), mean absolute error (MAE), root mean square error (RMSE), Pearson’s correlation coefficient (R) and Taylor diagram. The results indicate that EBKR performs the best spatial distribution. In order to determine the accuracy of spatial distribution generated by the spatial interpolation methods, we calculate the prediction standard error (PSE). The PSE result of EBKR prediction over mainland Portugal increases form south to north.

scholarly journals Continuous field soil moisture content mapping by means of apparent electrical conductivity (ECa) measurement

2013 ◽  
Vol 61 (4) ◽  
pp. 305-312 ◽  
Author(s):  
Viliam Nagy ◽  
Gábor Milics ◽  
Norbert Smuk ◽  
Attila József Kovács ◽  
István Balla ◽  
...  
Keyword(s):  
Electrical Conductivity ◽  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Interpolation Method ◽  
Moisture Distribution ◽  
Coefficient Of Determination ◽  
Volumetric Moisture Content ◽  
Sampling Points ◽  
Apparent Electrical Conductivity

Abstract A soil moisture content map is important for providing information about the distribution of moisture in a given area. Moisture content directly influences agricultural yield thus it is crucial to have accurate and reliable information about moisture distribution and content in the field. Since soil is a porous medium modified generalized Archie’s equation provides the basic formula to calculate moisture content data based on measured ECa. In this study we aimed to find a more accurate and cost effective method for measuring moisture content than manual field sampling. Locations of 25 sampling points were chosen from our research field as a reference. We assumed that soil moisture content could be calculated by measuring apparent electrical conductivity (ECa) using the Veris-3100 on-the-go soil mapping tool. Statistical analysis was carried out on the 10.791 ECa raw data in order to filter the outliers. The applied statistical method was ±1.5 interquartile (IRQ) distance approach. The visualization of soil moisture distribution within the experimental field was carried out by means of ArcGIS/ArcMAP using the inverse distance weighting interpolation method. In the investigated 25 sampling points, coefficient of determination between calculated volumetric moisture content data and measured ECa was R2 = 0.87. According to our results, volumetric moisture content can be mapped by applying ECa measurements in these particular soil types.

scholarly journals Métodos de Interpolação Matemática no Mapeamento de Chuvas do Estado do Mato Grosso ( Interpolation Methods in Mathematics of Rainfall Mapping of the State of Mato Grosso)

2012 ◽  
Vol 4 (4) ◽  
pp. 793 ◽  
Author(s):  
Francisco Marcuzzo ◽  
Lucas Andrade ◽  
Denise Melo
Keyword(s):  
Spatial Distribution ◽  
Interpolation Method ◽  
The State ◽  
Monthly Precipitation ◽  
Mato Grosso ◽  
Interpolation Methods ◽  
Correct Analysis ◽  
Data Source ◽  
Monthly Precipitation Data ◽  
Tension Spline

Uma correta análise da distribuição espacial das precipitações pluviométricas é de suma importância para o planejamento dos recursos hídricos de bacias hidrográficas, além de dar suporte a estudos climatológicos e meteorológicos. O objetivo deste trabalho foi o de estudar detalhadamente os métodos de interpolação matemática que geram regionalização de pontos por isolinhas, visando descobrir analiticamente o melhor método para espacialização de pontos com dados pluviométricos. Foram utilizados dados de precipitação mensal de 76 Estações Pluviométricas distribuídas no território do estado do Mato Grosso. Os dados foram obtidos da Agência Nacional de Águas, correspondendo à série histórica de 1977 a 2006. Os dados, depois de tratados e consistidos, foram submetidos a diversas metodologias de interpolação matemática com o intuito de verificar qual deles é mais adequado a espacialização de chuvas. Como resultados são apresentados mapas da distribuição espacial das chuvas no estado de Mato Grosso feitos usando os métodos de interpolação matemática IDW, Krigagem, Spline de tensão e Topo-to-Raster.Conclui-se que, para o estado do Mato Grosso, os melhores resultados foram obtidos através do método de interpolação Topo-to-Raster.Palavras-chave: Pluviometria, espacialização de chuvas, precipitação pluviométrica.  Interpolation Methods in Mathematics of Rainfall Mapping of the State of Mato Grosso  ABSTRACTA correct analysis of the spatial distribution of pluviometric precipitation is critical for planning water resources in hydrographic basins, and supporting meteorological and climatological studies. The objective of this paper was to study in detail the methods of mathematical interpolation that are used to generate regionalization of points per contour, seeking out analytically the best method for spatialization of the points with pluviometric data. As data source were used monthly precipitation data from 76 pluviometric stations distributed on the territory of the state of Mato Grosso. The data were obtained from the National Water Agency, corresponding to a time series from 1977 to 2006. The data, after treatment and consisted, were subjected to various methods of mathematical interpolation in order to see which one is best suited to the spatialization of rainfall. Results are presented as maps of the spatial distribution of rainfall in the state of Mato Grosso made using the methods of mathematical interpolation IDW, Kriging, Tension Spline and Topo-to-Raster. We conclude that, for the state of Mato Grosso, the best results were obtained by the interpolation method Topo-to-Raster. Key-words: Pluviometry, Spatialization of rainfall, pluviometric precipitation.

Comparison of Spatial Interpolation Methods for Mapping Daily Air Temperature 

2021 ◽  
Author(s):  
Lasyamayee L Sahoo ◽  
Subashisa Dutta
Keyword(s):  
Minimum Temperature ◽  
Evaluation Method ◽  
Heat Waves ◽  
Interpolation Method ◽  
Climatic Variability ◽  
Labour Productivity ◽  
The State ◽  
Coefficient Of Determination ◽  
Maximum Temperature ◽  
Interpolation Methods

<p>The sparsely distributed meteorological centers fails to provide enough information regarding spatial patterns. Even at places where dense meteorological stations are available, it is difficult to develop realistic gridded data due to the complex topography and climatic variability. Some of the climate as well as hydrological model require spatially continuous datasets as inputs. It is possible to obtain a continuous surface of raster datasets with the help of interpolation methods where each value is assigned based on surrounding values using specific mathematical formulas. For present study, various interpolation methods, like Inverse distance weighted, ordinary krigging, thin plate smoothing spline; has been compared for maximum and minimum temperature. Error in the interpolated data was analyzed by independent cross validation method, in which measurements like root mean square error (RMSE), mean squared relative error (MSRE), coefficient of determination (r<sup>2</sup>) and coefficient of efficiency (CE) were adopted for performance evaluation. Method with minimum error was chosen for developing the final map. It provides an effective way for mapping the meteorological variables in a topographically diverse region. In this case, an Indian state Odisha is chosen as study area. The state consists of 10 different agro-climatic zones and sees several weather systems across the year. The area suffers with floods, drought, heat waves and costal erosion almost every year with variable intensity. Strong heat waves in summer affect the human health, agriculture, construction efficiency and labour productivity. As three-fourth of the state is filled with mountains and high lands, monitoring network is sparsely distributed. Despite small latitudinal difference, temperature changes considerably with respect to both space and time. Here interpolation method plays a vital role to avoid uncertainty in modelling. Based on the generated maps, vulnerable areas on the basis of maximum temperature in summer and minimum temperature in winter is identified. Several indicators and vulnerability indices has been used.</p>

scholarly journals Assessment of Optimum Interpolation Method to Estimate Groundwater Chemical Data

2021 ◽  
Vol 1 (2) ◽  
pp. 41-47
Keyword(s):  
Polynomial Interpolation ◽  
Interpolation Method ◽  
Absolute Error ◽  
Analysis Data ◽  
Khuzestan Province ◽  
Interpolation Methods ◽  
Deep Wells ◽  
Distance Weighted ◽  
Northern Regions ◽  
Predicted Values

Abstract: Nowadays, the interpolation methods have become an important technology on the groundwater research. Many geographic information system software based on different interpolator tools have been developed and used widely such as ArcGIS, MapInfo and ArcView. This study was conducted to evaluate interpolation tools for the prediction of HCO3, Cl, SO4, Ca and Na distribution in groundwater of northern regions of Khuzestan province. Inverse distance weighted, kriging, radial basis functions, local and global polynomial interpolation were five interpolation methods that used for this subject. 98 deep wells was selected and chemical analysis data was collected in summer 2008. Predicted values of contaminants were compared to observed data by RMSE, MAE (Mean Absolute Error) and MSDR (Mean Squared Standardized Deviation Ratio) indexes to select the optimum interpolator technique. The results show that the kriging method has the highest interpolation accuracy among five interpolation methods for mapping Ca, SO4 and HCO3 by RMSE equal to 0.56, 0.9 and 0.6 respectively. Also, RBF and IDW Methods have acceptable estimations for Cl and Na ions.

Spatial Heterogeneity Analysis of Soil Heavy Metals in Chongqing City Based on Different Interpolation Methods

2021 ◽  
Author(s):  
Wende Chen ◽  
Yankun Cai ◽  
Jun Wei ◽  
Kun Zhu
Keyword(s):  
Heavy Metals ◽  
Spatial Distribution ◽  
Spatial Correlation ◽  
Interpolation Method ◽  
Theory Model ◽  
Important Indicator ◽  
The Sustainable Development ◽  
Grid Sampling ◽  
Interpolation Methods ◽  
Local Point

Abstract Heavy metal pollution in urban soil is an important indicator of environmental pollution, which is of great significance to the sustainable development of cities. Choosing the best interpolation method can accurately reflect the distribution characteristics and pollution characteristics of heavy metals in soil, which is conducive to effective management and implementation of protection strategies. In this study, the grid sampling with a depth of 40cm was carried out in the whole study area based on the principle of uniform sampling, and the characteristics of As, Cu and Mn elements in the soil of the main urban area of Chongqing were investigated. The interpolation accuracy and difference of results of four interpolation methods, namely ordinary Kriging (OK), inverse distance weighting (IDW), local polynomial (LPI) and radial basis function (RBF), were analyzed and compared. The results showed that the average values of As (5.802 mg kg-1), Cu (23.992 mg kg-1) and Mn (573.316 mg kg-1) in the soil of the study area were lower than the background values of heavy metals in Chongqing. Coefficient of variation showed that As (55.71%), Cu (35.73%) and Mn (32.21%) all belonged to moderate variation. The parameters of semi-variance function theory model show that Cu element belongs to moderate spatial correlation, and As and Mn element have strong spatial correlation. The spatial distribution of the three elements was further predicted by using OK method, IDW method, LPI method and RBF method, which showed that LPI and OK method had strong smoothing effect and could not reflect the information of local point source pollution, while the interpolation results of IDW method and RBF method greatly retained the maximum and minimum information of element content, which reflected the necessity of using different methods when studying the spatial distribution of soil properties.

scholarly journals Statistical analysis of the best GIS interpolation method for bearing capacity estimation in An-Najaf City, Iraq

2021 ◽  
Vol 80 (20) ◽  
Author(s):  
Sohaib Kareem Al-Mamoori ◽  
Laheab A. Al-Maliki ◽  
Ahmed Hashem Al-Sulttani ◽  
Khaled El-Tawil ◽  
Nadhir Al-Ansari
Keyword(s):  
Bearing Capacity ◽  
Interpolation Method ◽  
Field Investigation ◽  
Statistical Analyses ◽  
Coefficient Of Determination ◽  
Interpolation Methods ◽  
Geostatistical Methods ◽  
Backward Elimination ◽  
New Construction ◽  
Soil Behaviour

AbstractThe presence of an economical solution to predict soil behaviour is essential for new construction areas. This paper aims to investigate the ultimate interpolation method for predicting the soil bearing capacity of An-Najaf city-Iraq based on field investigation information. Firstly, the engineering bearing capacity was calculated based on the in-site N-SPT values using dynamic loading for 464 boreholes with depths of 0–2 m, using the Meyerhof formula. The data then were classified and imported to the GIS program to apply the interpolation methods. Four deterministic and two geostatistical interpolation methods were applied to produce six bearing capacity maps. The statistical analyses were performed using two methods: the common cross-validation method by the coefficient of determination (R2) and root mean square error (RMSE), where the results showed that ordinary kriging (OK) is the ultimate method with the least RMSE and highest R2. These results were confusing so, the backward elimination regression (BER) procedure was applied to gain the definite result. The results of BER show that among all the deterministic methods, the IDW is the optimal and most significant interpolation method. The result of geostatistical methods shows that EBK is the best method in our case than the OK method. BER also applied to all six methods and shows that IDW is the ultimate significant method. The results indicate no general ultimate interpolation method for all cases and datasets type; therefore, the statistical analyses must be performed for each case and dataset.

Filling Missing Data Using Interpolation Methods: Study on the Effect of Fitting Distribution

2013 ◽  
Vol 594-595 ◽  
pp. 889-895 ◽  
Author(s):  
M.N. Noor ◽  
A.S. Yahaya ◽  
N.A. Ramli ◽  
Abdullah Mohd Mustafa Al Bakri
Keyword(s):  
Missing Data ◽  
Human Error ◽  
Goodness Of Fit ◽  
Missing Values ◽  
Mean Squared Error ◽  
Interpolation Method ◽  
Coefficient Of Determination ◽  
Time Data ◽  
Data Set ◽  
Interpolation Methods

The presence of missing values in statistical survey data is an important issue to deal with. These data usually contained missing values due to many factors such as machine failures, changes in the siting monitors, routine maintenance and human error. Incomplete data set usually cause bias due to differences between observed and unobserved data. Therefore, it is important to ensure that the data analyzed are of high quality. A straightforward approach to deal with this problem is to ignore the missing data and to discard those incomplete cases from the data set. This approach is generally not valid for time-series prediction, in which the value of a system typically depends on the historical time data of the system. One approach that commonly used for the treatment of this missing item is adoption of imputation technique. This paper discusses three interpolation methods that are linear, quadratic and cubic. A total of 8577 observations of PM10 data for a year were used to compare between the three methods when fitting the Gamma distribution. The goodness-of-fit were obtained using three performance indicators that are mean absolute error (MAE), root mean squared error (RMSE) and coefficient of determination (R2). The results shows that the linear interpolation method provides a very good fit to the data.

scholarly journals Spatial and Temporal Variability of Soil Salinity in the Yangtze River Estuary Using Electromagnetic Induction

2021 ◽  
Vol 13 (10) ◽  
pp. 1875
Author(s):  
Wenping Xie ◽  
Jingsong Yang ◽  
Rongjiang Yao ◽  
Xiangping Wang
Keyword(s):  
Electrical Conductivity ◽  
Spatial Distribution ◽  
Soil Salinity ◽  
Yangtze River ◽  
Laboratory Data ◽  
River Estuary ◽  
Yangtze River Estuary ◽  
Soil Electrical Conductivity ◽  
The Yangtze River ◽  
The Yangtze River Estuary

Soil salt-water dynamics in the Yangtze River Estuary (YRE) is complex and soil salinity is an obstacle to regional agricultural production and the ecological environment in the YRE. Runoff into the sea is reduced during the impoundment period as the result of the water-storing process of the Three Gorges Reservoir (TGR) in the upper reaches of the Yangtze River, which causes serious seawater intrusion. Soil salinity is a problem due to shallow and saline groundwater under serious seawater intrusion in the YRE. In this research, we focused on the temporal variation and spatial distribution characteristics of soil salinity in the YRE using geostatistics combined with proximally sensed information obtained by an electromagnetic induction (EM) survey method in typical years under the impoundment of the TGR. The EM survey with proximal sensing method was applied to perform soil salinity survey in field in the Yangtze River Estuary, allowing quick determination and quantitative assessment of spatial and temporal variation of soil salinity from 2006 to 2017. We developed regional soil salinity survey and mapping by coupling limited laboratory data with proximal sensed data obtained from EM. We interpreted the soil electrical conductivity by constructing a linear model between the apparent electrical conductivity data measured by an EM 38 device and the soil electrical conductivity (EC) of soil samples measured in laboratory. Then, soil electrical conductivity was converted to soil salt content (soil salinity g kg−1) through established linear regression model based on the laboratory data of soil salinity and soil EC. Semivariograms of regional soil salinity in the survey years were fitted and ordinary kriging interpolation was applied in interpolation and mapping of regional soil salinity. The cross-validation results showed that the prediction results were acceptable. The soil salinity distribution under different survey years was presented and the area of salt affected soil was calculated using geostatistics method. The results of spatial distribution of soil salinity showed that soil salinity near the riverbanks and coastlines was higher than that of inland. The spatial distribution of groundwater depth and salinity revealed that shallow groundwater and high groundwater salinity influenced the spatial distribution characteristics of soil salinity. Under long-term impoundment of the Three Gorges Reservoir, the variation of soil salinity in different hydrological years was analyzed. Results showed that the area affected by soil salinity gradually increased in different hydrological year types under the impoundment of the TGR.

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