Inverse distance weighting method optimization in the process of digital terrain model creation based on data collected from a multibeam echosounder

Abstract

This paper presents the optimization of the inverse distance weighting method (IDW) in the process of creating a digital terrain model (DTM) of the seabed based on bathymetric data collected using a multibeam echosounder (MBES). There are many different methods for processing irregular measurement data into a grid-based DTM, and the most popular of these methods are inverse distance weighting (IDW), nearest neighbour (NN), moving average (MA) and kriging (K). Kriging is often considered one of the best methods in interpolation of heterogeneous spatial data, but its use is burdened by a significantly long calculation time. In contrast, the MA method is the fastest, but the calculated models are less accurate. Between them is the IDW method, which gives satisfactory accuracy with a reasonable calculation time. In this study, the author optimized the IDW method used in the process of creating a DTM seabed based on measurement points from MBES. The goal of this optimization was to significantly accelerate the calculations, with a possible additional increase in the accuracy of the created model. Several variants of IDW methods were analysed (dependent on the search radius, number of points in the interpolation, power of the interpolation and applied smoothing method). Finally, the author proposed an optimization of the IDW method, which uses a new technique of choosing the nearest points during the interpolation process (named the growing radius). The experiments presented in the paper and the results obtained show the true potential of the IDW optimized method in the case of DTM estimation.

Assessment of the performance of CORDEX regional climate models in simulating rainfall and air temperature over southwest Ethiopia

“…This is a suitable method for interpolating average rainfall using latitude, longitude and the average rainfall recorded at a gauging station. IDW interpolation gives accurate results with a reasonable calculation based on the temporal and spatial structure (Maleika, 2020;Ryu et al, 2020;Yang et al, 2020). The spatial interpolation of the extreme rainfall data, using IDW algorithms, has given good results (Edalat et al, 2019;Tsangaratos et al, 2019).…”

Modeling electricity consumption patterns during the COVID-19 pandemic across six socioeconomic sectors in the State of Qatar

“…To define the spatial patterns of electricity consumption among different socioeconomic sectors, we employed the Inverse Distance Weighting (IDW) and Kriging for spatial pattern analysis [ [41] , [42] , [43] ]. IDW is a deterministic method that incorporates information on the geographical position for multivariate interpolation with a known scattered set of points [ 44 , 45 ].…”

“…Qatar is the second highest electricity consumer among the Gulf Cooperation Council (GCC) nations, which include, in addition to Qatar, the countries of Saudi Arabia, United Arab Emirates, Kuwait, Bahrain, and the Sultanate of Oman [ 42 , 44 , 52 ]. In Qatar, the electricity market tries to fulfill the needs of various sectors of the Qatari economy and hence can be considered as a demand-oriented market.…”

Methodology for Developing a Combined Bathymetric and Topographic Surface Model Using Interpolation and Geodata Reduction Techniques

“…Today, various interpolation methods can be used to calculate heights in a GRID or TIN (Triangulated Irregular Network) structure. These methods have been the subject of many studies, involving datasets characterized by different densities or spatial distributions [8,[17][18][19][20][21]. In the literature, we can find numerous examples of authors studying the influences of different aspects, such as the interpolation method, resolution, data density, and slope, on the construction of a numerical terrain model or a numerical bottom model.…”