Efficiently assign drainage directions over flat surfaces in digital elevation models
Windows port of Simple Sparse Bundle Adjustment (SSBA was created by Christopher Zach under LGPL license)
Time series forecasting (TSF) is the task of predicting future values of a given sequence using historical data. Recently, this task has attracted the attention of researchers in the area of machine learning to address the limitations of traditional forecasting methods, which are time-consuming and full of complexity. With the increasing availability of extensive amounts of historical data along with the need of performing accurate production forecasting, particularly a powerful forecasting technique infers the stochastic dependency between past and future values is highly needed. In this research, we applied machine learning approach capable to address the limitations of traditional forecasting approaches and show accurate predictions and showed comparison of different machine learning models. For evaluation purpose, a case study from the petroleum industry domain is carried out using the production data of an actual gas field of Bangladesh. Toward a fair evaluation, the performance of the models were evaluated by measuring the goodness of fit through the coefficient of determination (R2 ) and Root Mean Square Error (RMSE), Mean Squared Error (MSE) , Mean Absolute Error(MAE) and model Accuracy
This is Ridecell (Auro) Coding Challenge
Collection of cryosphere related python functions I use everyday. Feel free to use and/or contribute!
A toolbox for the OLCI and SLSTR instruments on board of ESA's Sentinel-3 satellite
Macros to compute and visualize the background on a Low Earth Orbit