scholarly journals APLIKASI GEOSPASIAL MENGGUNAKAN ARCGIS 10.3 DALAM PEMBUATAN PETA DAYA HANTAR LISTRIK DI CEKUNGAN AIRTANAH SUMOWONO

2017 ◽  
Vol 14 (1) ◽  
pp. 15
Author(s):  
Thomas Triadi Putranto ◽  
Kevin Alexander
Keyword(s):  
Radial Basis Functions ◽  
Basis Functions ◽  
Inverse Distance Weighting ◽  
Empirical Bayesian ◽  
Bayesian Kriging ◽  
Empirical Bayesian Kriging ◽  
Distance Weighting ◽  
Radial Basis ◽  
Inverse Distance

Air tanah sebagai air bersih merupakan salah satu kebutuhan primer manusia yang dimanfaatkan dalam berbagai kepentingan manusia serta untuk air minum. Airtanah memiliki kualitas dimana salah satu parameter fisiknya adalah daya hantar listrik (DHL). Dalam suatu Cekungan Airtanah (CAT), airtanah memiliki keberagaman nilai daya hantar listrik yang dipengaruhi oleh faktor infiltrasi dan lingkungan. Nilai DHL dapat dijadikan suatu acuan mengenai kelayakan suatu airtanah sebagai air minum. Sebagai salah satu sumber yang paling diminati masyarakat, maka masyarakat juga perlu untuk mengetahui kualitas dari airtanah tersebut melalui parameter daya hantar listrik sehingga peta daya hantar listrik daerah CAT Sumowono dapat menjadi suatu informasi bagi masyarakat yang menggunakan airtanah dari CAT Sumowono tersebut. Maka dari itu perlu adanya pembuatan peta daya hantar listrik daerah CAT Sumowono agar masyarakat merasa nyaman dan aman dalam memanfaatkan airtanah. Metode interpolasi data DHL menggunakan analisis geostatistik yang terdapat pada perangkat lunak ArcGIS 10.3. Metode interpolasi yang digunakan adalah Inverse Distance Weighting (IDW), Radial Basis Functions (RBF) dan Empirical Bayesian Kriging (EBK). Dari keseluruhan data yang terinterpolasi, didapatkan dua kelas kualitas airtanah berdasarkan nilai DHL, yaitu Sangat Baik (<250 μS/cm) dan Baik (250-750 μS/cm). Metode interpolasi yang dinilai paling seimbang adalah metode RBF. Melalui peta DHL hasil interpolasi metode RBF diketahui persebaran daerah dengan kelas sangat baik pada daerah CAT Sumowono mencakup 52,8% dari luas CAT dan 47,2% masuk ke dalam kelas baik.

scholarly journals Global optimization via inverse distance weighting and radial basis functions

2020 ◽  
Vol 77 (2) ◽  
pp. 571-595 ◽  
Author(s):  
Alberto Bemporad
Keyword(s):  
Global Optimization ◽  
Objective Function ◽  
Radial Basis Functions ◽  
Basis Functions ◽  
Inverse Distance Weighting ◽  
Bayesian Optimization ◽  
Distance Weighting ◽  
Radial Basis ◽  
Feasible Space ◽  
Inverse Distance

Abstract Global optimization problems whose objective function is expensive to evaluate can be solved effectively by recursively fitting a surrogate function to function samples and minimizing an acquisition function to generate new samples. The acquisition step trades off between seeking for a new optimization vector where the surrogate is minimum (exploitation of the surrogate) and looking for regions of the feasible space that have not yet been visited and that may potentially contain better values of the objective function (exploration of the feasible space). This paper proposes a new global optimization algorithm that uses inverse distance weighting (IDW) and radial basis functions (RBF) to construct the acquisition function. Rather arbitrary constraints that are simple to evaluate can be easily taken into account. Compared to Bayesian optimization, the proposed algorithm, that we call GLIS (GLobal minimum using Inverse distance weighting and Surrogate radial basis functions), is competitive and computationally lighter, as we show in a set of benchmark global optimization and hyperparameter tuning problems. MATLAB and Python implementations of GLIS are available at http://cse.lab.imtlucca.it/~bemporad/glis.

scholarly journals Assessment of Spatial Prediction Techniques Accuracy for Elevation Determination in Akure South Local Government, Ondo State

2020 ◽  
Vol 5 (5) ◽  
pp. 550-553
Author(s):  
Victor Ayodele Ijaware ◽  
Adebayo T. Adeboye
Keyword(s):  
Polynomial Interpolation ◽  
Inverse Distance Weighting ◽  
Universal Kriging ◽  
Empirical Bayesian ◽  
Bayesian Kriging ◽  
Empirical Bayesian Kriging ◽  
Local Polynomial ◽  
Distance Weighting ◽  
Elevation Data ◽  
Inverse Distance

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is a cooperative effort between NASA and Japan's Ministry of Economy Trade and Industry (METI), with the collaboration of scientific and industry organizations in both countries. The ASTER instrument provides a more robust remote sensing imaging capability when compared to the older Landsat Thematic Mapper. This paper deals with the accuracy assessment of elevation data obtained using ASTER from each of the eleven (11) selected extrapolation/interpolation algorithms: Inverse Distance Weighting, Natural Neighbor, Spline Regular, Spline Tension, Universal Kriging, Empirical Bayesian Kriging, Topo to Raster, global (trend surface), local polynomial, kernel interpolation with barriers and radial basis functions in Digital Elevation Model (DEM) surface creation. The data were compared with reference to ground control points of differential GPS measurements in the study area. The error statistics were generated between DGPS measurements and Extracted elevation data from each selected interpolation method. It was observed that Spline Regular Interpolation shown the best overall accuracy of ±11.520m when elevation data extracted from Inverse distance weighting, Natural Neighbour, Spline T, Topo to Raster, Universal Kriging, Empirical Bayesian kriging, Global polynomial interpolation (GPI), local polynomial interpolation (LPI), Radial basis function and Kernel interpolation of ±15.170, ±14.340, ±12.336, ±13.551, ±14.707, ±13.711, ±15.363, ±13.964, ±13.590 and ±15.376 respectively when compared with elevation values from GPS method. The study recommends capacity building in the form of workshop, training, and flexible integration of point elevation data to DEM.

Biharmonic navigation using radial basis functions

Robotica ◽  
2021 ◽  
pp. 1-12
Author(s):  
Xu-Qian Fan ◽  
Wenyong Gong
Keyword(s):  
Finite Difference ◽  
Boundary Conditions ◽  
Radial Basis Functions ◽  
Real World ◽  
Biharmonic Equation ◽  
Finite Difference Methods ◽  
Basis Functions ◽  
Large Size ◽  
Radial Basis ◽  
Difference Methods

Abstract Path planning has been widely investigated by many researchers and engineers for its extensive applications in the real world. In this paper, a biharmonic radial basis potential function (BRBPF) representation is proposed to construct navigation fields in 2D maps with obstacles, and it therefore can guide and design a path joining given start and goal positions with obstacle avoidance. We construct BRBPF by solving a biharmonic equation associated with distance-related boundary conditions using radial basis functions (RBFs). In this way, invalid gradients calculated by finite difference methods in large size grids can be preventable. Furthermore, paths constructed by BRBPF are smoother than paths constructed by harmonic potential functions and other methods, and plenty of experimental results demonstrate that the proposed method is valid and effective.

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