Application of Clustering System to Analyze Geological, Geotechnical and Hydrogeological Data Base according to HC-System Approach

Widodo, Lilik Eko; Cahyadi, Tedy Agung; Notosiswoyo, Sudarto; Widijanto, Eman

doi:10.31227/osf.io/mdtp4

Cited by 5 publications

(2 citation statements)

References 4 publications

(14 reference statements)

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“…Multivariate clustering methods were used to cluster water chemistry data into distinct groups [51]. Partitional clustering analysis utilized boreholes data to identify the relationship between hydrogeological, lithology, and geotechnical [52]. In [53], the authors applied clustering techniques to multivariate timeseries data to cluster geochemical anomalies based on underlying and hidden characteristics.…”

Section: Literature Reviewmentioning

confidence: 99%

Boreholes Data Analysis Architecture Based on Clustering and Prediction Models for Enhancing Underground Safety Verification

et al. 2021

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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 2 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.

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Section: Literature Reviewmentioning

confidence: 99%

Boreholes Data Analysis Architecture Based on Clustering and Prediction Models for Enhancing Underground Safety Verification

et al. 2021

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“…Hydraulic conductivity value of each Lithology(Widodo et al 2016) Figure3shows the land use land cover changes over Chhattisgarh state from year 2000 to 2020. The LULC map is made in a systematic manner using the Colab environment.…”

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confidence: 99%

Forecasting of Groundwater Level Variation Under Changing Climate in Chhattisgarh State Using Deep Learning Technique

Dey,

Singh

2024

Preprint

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Groundwater is the most precious natural resource in modern days. India is the largest consumer of groundwater globally, with over 25% of the world's groundwater extraction. Climate change affects the groundwater level both in direct and indirect ways. Recently developed machine learning approaches have led to the consideration of selected climate variables that can govern the groundwater dynamic. The inclusion of indirect key drivers such as anthropogenic activities and lithology to forecast groundwater levels using machine learning techniques is not well understood. This paper aims to consider both the direct and indirect key drivers for forecasting seasonal groundwater levels. In this context, a modified approach based on a deep learning model has been formulated that considers land cover dynamics, lithological properties, and climatic variables such as temperature and precipitation. The model was calibrated and validated to forecast seasonal groundwater levels for four Shared Socioeconomic Pathways (SSPs) scenarios. The results show that the accuracy level, R2 is 0.86 which is acceptable. Overall, the results obtained broadly correspond to an acceptable degree of accuracy. The proposed methodology is applicable for seasonal groundwater level forecasting and can be useful to farmers and key stakeholders.

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Analysis of hydraulic conductivity of fractured groundwater flow media using artificial neural network back propagation

Cahyadi

Syihab

Widodo

et al. 2020

Neural Comput & Applic

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Application of Clustering System to Analyze Geological, Geotechnical and Hydrogeological Data Base according to HC-System Approach

Cited by 5 publications

References 4 publications

Boreholes Data Analysis Architecture Based on Clustering and Prediction Models for Enhancing Underground Safety Verification

Boreholes Data Analysis Architecture Based on Clustering and Prediction Models for Enhancing Underground Safety Verification

Forecasting of Groundwater Level Variation Under Changing Climate in Chhattisgarh State Using Deep Learning Technique

Analysis of hydraulic conductivity of fractured groundwater flow media using artificial neural network back propagation

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