Abstract
During the last decade, substantial resources have been invested to exploit massive amounts of boreholes data collected through groundwater extraction. Furthermore, boreholes depth can be considered one of the crucial factors in digging borehole efficiency. Therefore, a new solution is needed to process and analyze boreholes data to monitor digging operations and identify the boreholes shortcomings. This research study presents a boreholes data analysis architecture based on data and predictive analysis models to improve borehole efficiency, underground safety verification, and risk evaluation. The proposed architecture aims to process and analyze borehole data based on different hydrogeological characteristics using data and predictive analytics to enhance underground safety verification and planning of borehole resources. The proposed architecture is developed based on two modules; descriptive data analysis and predictive analysis modules. The descriptive analysis aims to utilize data and clustering analysis techniques to process and extract hidden hydrogeological characteristics from borehole history data. The predictive analysis aims to develop a bi-directional long short-term memory (BD-LSTM) to predict the boreholes depth to minimize the cost and time of the digging operations. Furthermore, different performance measures are utilized to evaluate the performance of the proposed clustering and regression models. Moreover, our proposed BD-LSTM model is evaluated and compared with conventional machine learning (ML) regression models. The R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score of the proposed BD-LSTM is 0.989, which indicates that the proposed model accurately and precisely predicts boreholes depth compared to the conventional regression models. The experimental and comparative analysis results reveal the significance and effectiveness of the proposed borehole data analysis architecture. The experimental results will improve underground safety management and the efficiency of boreholes for future wells.
Highlights
The revolution in industrial development paved the way towards increasing the urban population rapidly
It is evident that performance of ensemble prediction model using Mean is up to the mark due to its ability to map temporal correlations and handle long term dependencies
The XGBoost model performed accurately compared to the Random Forest (RF) and support vector regression (SVR); the R2 score of the XGBoost model is 0.954, which shows that the XGBoost model slightly improves the prediction performance compared to other conventional regression models
Summary
The revolution in industrial development paved the way towards increasing the urban population rapidly. Clustering lies under the umbrella of unsupervised learning, called data exploration for identifying similar patterns in data [11] Another method for big data analytics is based on ML algorithms composed of learning modules that are VOLUME 9, 2021 proven to be the backbone of the intelligent systems providing a platform for the analysis of complex and dynamic non-linear systems, such as big data of groundwater wells [12]. The most widely used methods for depth rate prediction include Artificial Neural Network (ANN) that are efficient are handling complex non-linear patterns of time series boreholes groundwater data [15]. The core contribution of the proposed research study is to utilize data and predictive analysis models to cluster borehole data samples into homogeneous groups based on hidden hydrogeological characteristics and predict boreholes depth for enhancing boreholes efficiency and underground safety verification management.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
