scholarly journals Evaluation of an inverse distance weighting method for patching daily and dekadal rainfall over the Free State Province, South Africa

Water SA ◽  
2016 ◽  
Vol 42 (3) ◽  
pp. 466 ◽  
Author(s):  
Mokhele Edmond Moeletsi ◽  
Zakhele Phumlani Shabalala ◽  
Gert De Nysschen ◽  
Sue Walker
Keyword(s):  
South Africa ◽  
Inverse Distance Weighting ◽  
Free State ◽  
Weighting Method ◽  
Free State Province ◽  
Distance Weighting ◽  
Inverse Distance

scholarly journals Increasing Efficiency of Field Water Re-Injection during Water-Flooding in Mature Hydrocarbon Reservoirs: A Case Study from the Sava Depression, Northern Croatia

2020 ◽  
Vol 12 (3) ◽  
pp. 786 ◽  
Author(s):  
Tomislav Malvić ◽  
Josip Ivšinović ◽  
Josipa Velić ◽  
Jasenka Sremac ◽  
Uroš Barudžija
Keyword(s):  
Water Injection ◽  
Inverse Distance Weighting ◽  
Hydrocarbon Reservoirs ◽  
Water Flooding ◽  
Injection Wells ◽  
Weighting Method ◽  
Hydrocarbon Production ◽  
Recovery Method ◽  
Distance Weighting ◽  
Inverse Distance

The authors analyse the process of water re-injection in the hydrocarbon reservoirs/fields in the Upper Miocene sandstone reservoirs, located in the western part of the Sava Depression (Croatia). Namely, this is the “A” field with “L” reservoir that currently produces hydrocarbons using a secondary recovery method, i.e., water injection (in fact, re-injection of the field waters). Three regional reservoir variables were analysed: Porosity, permeability and injected water volumes. The quantity of data was small for porosity reservoir “L” and included 25 points; for permeability and injected volumes of water, 10 points each were measured. This study defined selection of mapping algorithms among methods designed for small datasets (fewer than 20 points). Namely, those are inverse distance weighting and nearest and natural neighbourhood. Results were tested using cross-validation and isoline shape recognition, and the inverse distance weighting method is described as the most appropriate approach for mapping permeability and injected volumes in reservoir “L”. Obtained maps made possible the application of the modified geological probability calculation as a tool for prediction of success for future injection (with probability of 0.56). Consequently, it was possible to plan future injection more efficiently, with smaller injected volumes and higher hydrocarbon recovery. Prevention of useless injection, decreasing number of injection wells, saving energy and funds invested in such processes lead to lower environmental impact during the hydrocarbon production.

scholarly journals A POD-selective inverse distance weighting method for fast parametrized shape morphing

2018 ◽  
Vol 117 (8) ◽  
pp. 860-884 ◽  
Author(s):  
Francesco Ballarin ◽  
Alessandro D'Amario ◽  
Simona Perotto ◽  
Gianluigi Rozza
Keyword(s):  
Inverse Distance Weighting ◽  
Weighting Method ◽  
Shape Morphing ◽  
Distance Weighting ◽  
Inverse Distance

scholarly journals Development and evaluation of an extended inverse distance weighting method for streamflow estimation at an ungauged site

2015 ◽  
Vol 47 (2) ◽  
pp. 333-343 ◽  
Author(s):  
Muhammad Waseem ◽  
Muhammad Ajmal ◽  
Ungtae Kim ◽  
Tae-Woong Kim
Keyword(s):  
Weighted Average ◽  
Inverse Distance Weighting ◽  
General Idea ◽  
Coefficient Of Determination ◽  
Weighting Method ◽  
Ungauged Sites ◽  
Distance Weighting ◽  
Leave One Out ◽  
Mean Square Errors ◽  
Inverse Distance

In spatial interpolation, one of the most widely used deterministic methods is the inverse distance weighting (IDW) technique. The general idea of IDW is primarily based on the hypothesis that the attribute value of an ungauged site is the weighted average of the known attribute values within the neighborhood, and the ‘weights’ are merely associated with the horizontal distances between the gauged and ungauged sites. However, here we propose an extended version of IDW (hereafter, called the EIDW method) to provide ‘alternative weights’ based on the blended geographical and physiographical spaces for estimation of streamflow percentiles at ungauged sites. Based on the leave-one-out cross-validation procedure, the coefficient of determination had a value of 0.77 and 0.82 for the proposed EIDW models, M1 and M2, respectively, with low root mean square errors. Moreover, after investigating the relationship between the prediction efficiency and the distance decay parameter (C), the better performance of the M1 and M2 resulted at C = 2. Furthermore, the results of this study show that the EIDW could be considered as a constructive way forward to provide more accurate and consistent results in comparison to the traditional IDW or the dimension reduction technique-based IDW.

scholarly journals Prediction of electricity sales using neural based inverse distance weighting method

2018 ◽  
Vol 7 (2.2) ◽  
pp. 65 ◽  
Author(s):  
Bustani . ◽  
Sunu Pradana ◽  
Mulyanto . ◽  
Nurjanana .
Keyword(s):  
Interpolation Method ◽  
Back Propagation ◽  
Inverse Distance Weighting ◽  
Data Availability ◽  
Sampled Data ◽  
Weighting Method ◽  
Distance Weighting ◽  
Som Algorithm ◽  
Z Value ◽  
Inverse Distance

Prediction of electricity sales becomes important for State Electricity Company of Indonesia (PLN) in order to estimate the Statement of Profit and Loss in next year. To obtain good predictive results may require many variables and data availability. There are many available methods that do not require so many variables to get predicted results with a good approximation. The aim of this study was to predict electricity sales by using an interpolation method called IDW (Inverse Distance Weighting). Several data samples are mapped into Cartesian coordinates. The data samples used are power connected to the household (X-axis), to industry (Y-axis), and electricity sales (Z value). Firstly, the sampled data clustered by using SOM algorithm. The Z value in each cluster is predicted by using the IDW method. The prediction results of IDW method are then optimized using ANN-BP (Artificial Neural Network Back Propagation). The trained net structure is then used to predict the electricity sale in next year.  

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