Scale-Invariance and Fuzzy Techniques Explain the Empirical Success of Inverse Distance Weighting and of Dual Inverse Distance Weighting in Geosciences

Việc tính toán các chỉ số sinh kế góp phần nắm bắt sự khác biệt về sinh kế của các hộ nông dân trên một khu vực nghiên cứu nhất định. Tuy nhiên, công tác điều tra sinh kế sẽ bị giới hạn bởi nhiều yếu tố như chi phí, nhân công, khoảng cách khiến cho các điểm điều tra không thể bao trọn cả vùng nghiên cứu. Các phương pháp thống kê không gian mà cụ thể là phương pháp nội suy cho phép tính toán giá trị tại một vị trí thông qua các giá trị tại những vị trí đã biết bao quanh nó. Nghiên cứu áp dụng phương pháp IDW (Inverse Distance Weighting) để tính toán chỉ số tài sản sinh kế LAI (Livelihood Asset Index) cho toàn bộ khu vực gồm 3 huyện Tam Nông, Tháp Mười và Tân Hồng. Kết quả cho thấy, có sự phân bố không đồng đều về các nguồn vốn và chỉ số tài sản sinh kế giữa các xã cũng như các huyện trong khu vực nghiên cứul; đồng thời, còn chứng minh rằng, phương pháp IDW là một công cụ hữu hiệu trong thống kê không gian với độ chính xác cao. Hơn nữa, kết quả của nghiên cứu có thể được dùng để đánh giá hiện trạng sinh kế, góp phần tạo sự liên kết giữa các vùng trong khu vực nghiên cứu và hướng đến phát triển bền vững.

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Static Reservoir Modeling Comparing Inverse Distance Weighting to Kriging Interpolation Algorithm in Volumetric Estimation. Case Study: Gullfaks Field

10.4043/30919-ms ◽

2021 ◽

Author(s):

Daniel Asante Otchere ◽

David Hodgetts ◽

Tarek Arbi Omar Ganat ◽

Najeeb Ullah ◽

Alidu Rashid

Keyword(s):

Power Index ◽

Inverse Distance Weighting ◽

Kriging Interpolation ◽

Reservoir Modeling ◽

Interpolation Algorithm ◽

Significant Difference ◽

Distance Weighting ◽

Porosity And Permeability ◽

Well Data ◽

Inverse Distance

Abstract Understanding and characterizing the behaviour of the subsurface by combining it with a suitable statistical method gives a higher level of confidence in the reservoir model produced. Interpolation of porosity and permeability data with minimum error and high accuracy is, therefore, essential in reservoir modeling. The most widely used interpolation algorithm, kriging, with enough well data is the best linear unbiased estimator. This research sought to compare the applicability and competitiveness of inverse distance weighting (IDW) method using power index of 1, 2 and 4 to kriging when there is sparse data, due to time and budget constraints, to calculate hydrocarbon volumes in a fluvial-deltaic reservoir. Interpolation results, estimated from descriptive statistics, were insignificant and showed similar prediction accuracy and consistency but IDW with power index of 1 indicated the least error estimation and higher accuracy. The assessment of hydrocarbon volume calculations also showed a marginal difference below 0.08 between IDW power index of 1 and kriging in the reservoir zones. Reservoir segments cross-validation and correlation analysis results indicate IDW to have no significant difference to kriging with absolute errors of 3% for recoverable oil and 0.7% for recoverable gas. Grid upscaling, which usually causes a loss of geological features and extreme porosity values, did not impact the results but rather complemented the robustness of IDW in both fine and coarse grid upscale. With IDW exhibiting least errors and higher accuracy, the volumetric and statistical results confirm that when there are fewer well data in a fluvial-deltaic reservoir, the suitable spatial interpolation choice should be IDW method with a power index of 1.

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Inverse distance weighting interpolated soil properties and their related landslide occurrences

MATEC Web of Conferences ◽

10.1051/matecconf/201819503013 ◽

2018 ◽

Vol 195 ◽

pp. 03013 ◽

Cited By ~ 2

Author(s):

Purwanto B. Santoso ◽

Yanto ◽

Arwan Apriyono ◽

Rani Suryani

Keyword(s):

Internal Friction ◽

Slope Stability ◽

Soil Properties ◽

Hard Rock ◽

Friction Angle ◽

Inverse Distance Weighting ◽

Central Java ◽

Soil Cohesion ◽

Distance Weighting ◽

Inverse Distance

The causes of landslides can be categorized into three factors: climate, topographic, and soil properties. In many cases, thematic maps of landslide hazards do not involve slope stability analyses to predict the region of potential landslide risks. Slope stability calculation is required to determine the safety factor of a slope. The calculation of slope stability requires the soil properties, such as soil cohesion, the internal friction angle and the depth of hard-rock. The soil properties obtained from the field and laboratory investigation from the western part of Central Java were interpolated using Inverse Distance Weighting (IDW) to estimate the unknown soil properties in the gridded area. In this research, the IDW optimum parameter was determined by validation toward the percent bias. It was found that the IDW interpolation using higher weighting factor corresponds with a higher percent bias in case of the depth of hard-rock and soil cohesion, while the opposite was found for the internal friction angle. Validation to landslide incidents in western parts of Central Java shows that the majority of landslide incidents occur at depths of hard rock of 6 m-8 m, at soil cohesions of 0.0 kg/cm2-0.2 kg/cm2, and at internal friction angles of 30°-40°.

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A Modified Inverse Distance Weighting Method for Interpolation in Open Public Places Based on Wi-Fi Probe Data

Journal of Advanced Transportation ◽

10.1155/2019/7602792 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11 ◽

Cited By ~ 2

Author(s):

Da-wei Wang ◽

Lu-ning Li ◽

Cheng Hu ◽

Qiang Li ◽

Xiang Chen ◽

...

Keyword(s):

Path Selection ◽

Inverse Distance Weighting ◽

Location Based Services ◽

New Method ◽

Absolute Deviation ◽

Public Places ◽

Main Challenge ◽

Distance Weighting ◽

Selection Behavior ◽

Inverse Distance

Urban open places with a public service function (e.g., urban parks) are likely to be populated in peak hours and during public events. To mitigate the risk of overcrowding and even events of stampedes, it is of considerable significance to realize a real-time full coverage estimate of the population density. The main challenge has been the limited deployment of crowd surveillance detectors in open public spaces, leading to incomplete data coverage and thus impacting the quality and reliability of the density estimation. To remedy this issue, this paper proposes a modified inverse distance weighting (IDW) method, named the inverse distance weighting based on path selection behavior (IDWPSB) method. The proposed IDWPSB method adjusts the distance decay effect according to visitors’ path selection behavior, which better characterizes the human dynamics in open spaces. By implementing the model in a real-world road network in the Shichahai scenic area in Beijing, China, the study shows a decrease in the absolute deviation by 17.62% comparing the results between the new method and the traditional IDW method, justifying the effectiveness of the new method for spatial interpolation in open public places. By considering the behavioral factor, the proposed IDWPSB method can provide insights into public safety management with the increasing availability of data derived from location-based services.

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