[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123817-en":3,"doc-seo-123817-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123817,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Application of Data Mining and Machine Learning Techniques for Geohydrological Datasets in South Africa","A desktop study was conducted to research data-driven modelling approaches for classifying relationships between borehole parameters and geological settings. Because borehole surveying and drilling are costly, applying data mining and machine learning to national groundwater databases and other spatial datasets can improve insight for managing groundwater resources. Five machine learning algorithms were evaluated on a consolidated dataset and benchmarked across three Vegter-region case studies. Results showed high-accuracy modelling for static water levels, while yield modelling was less reliable, leaving uncertainty about water-strike yield drivers.","Application of data mining and machine learning techniques for geohydrological datasets in South  \nAfrica  \nC de Bruyn  \n [orcid.org/0000-0003-3011-8563](orcid.org/0000-0003-3011-8563)  \nDissertation accepted in fulfilment of the requirements for the degree Master of Science in Environmental Sciences with Hydrology and Geohydrology at the North-West University  \nSupervisor: Dr SR Dennis  \nGraduation October 2023 24963623  \nACKNOWLEDGEMENTS  \nFirstly, I would like to thank YHVH, my Creator, and his Son, Yeshua, for giving me the strength to push onwards through this endeavour.  \nI am thankful to Dr Rainier Dennis and the Centre of Water Sciences and Management who granted me this opportunity and who guided me through this process, equipping me with the proper tools and knowledge.  \nTo my parents, for all the love and support through this tough time. I would not have been able to pursue this degree, let alone finish it without them. Also, my brother for giving me advice on atopic which was completely new to me at the start of this study. And my fiancé, for encouraging me to finish what I started.  \nFinally, I am grateful for having a friend in Lohan Bredenhann, who supported me and gave technical advice regarding this academic pursuit.  \nABSTRACT  \nA desktop study was conducted to research data-driven modelling techniques to classify relationships between borehole parameters and the relevant geological setting. Borehole surveying and drilling is a costly endeavour and by applying data mining and machine learning techniques to national groundwater databases and other available national datasets such as spatial data, better insight and improvements on management of groundwater resources can result.  \nFive machine learning algorithms were tested on a consolidated dataset and their performances compared in order to establish which algorithm yielded the most accurate results. It was established that Random Forest Regression and Classification could be used to model yield, and Support Vector Regression and Random Forest Classification could model static water levels. The algorithm was tested on three case study areas, based on Vegter regions.  \nThe results indicated that static water levels could be modelled with high rates of accuracy, but yield modelling was not as successful, and a lot of uncertainty still remains as to the drivers behind water strike yield.  \nKeywords: data mining, machine learning, groundwater resource management, geohydrological datasets, data-driven modelling; water level modelling, yield modelling.  \nTABLE OF CONTENTS  \nACKNOWLEDGEMENTS .......................................................................................................... I  \nABSTRACT .............................................................................................................................. II  \nLIST OF TABLES .................................................................................................................... IX  \nLIST OF FIGURES.................................................................................................................... X  \nLIST OF EQUATIONS ...........................................................................................................XIV  \nLIST OF ABBREVIATIONS ....................................................................................................XV  \nCHAPTER 1: INTRODUCTION................................................................................................. 1  \n1.1 Background ...................................................................................................... 1  \n1.2 Problem statement ........................................................................................... 2  \n1.3 Aims and objectives ......................................................................................... 3  \n1.3.1 Aims ...........................................................................................................................","cbCaiuE7TL322PC7","https://ap.wps.com/l/cbCaiuE7TL322PC7","pdf",10040973,1,166,"English","en",105,"# Acknowledgements\n# Abstract\n# List of Tables\n# List of Figures\n# List of Equations\n# List of Abbreviations\n# Chapter 1: Introduction\n## Background\n## Problem statement\n## Aims and objectives\n## Basic hypothesis\n## Scope of research\n## Assumptions and limitations\n## Research contribution\n## Dissertation structure\n# Chapter 2: Literature Review\n## Introduction\n## Data mining\n## Datasets\n## Data-mining methods\n## Modelling and forecasting of geohydrological settings\n## Data-driven modelling techniques","[{\"question\":\"What is the main goal of the study on South African geohydrological datasets?\",\"answer\":\"To use data-driven modelling to classify relationships between borehole parameters and geological settings, supporting improved groundwater management.\"},{\"question\":\"Which machine learning algorithms performed best for static water levels and yield?\",\"answer\":\"Random Forest Regression and Classification were used to model yield, while Support Vector Regression and Random Forest Classification were used for static water levels.\"},{\"question\":\"How did the results vary between static water level modelling and yield modelling?\",\"answer\":\"Static water levels were modelled with high accuracy, but yield modelling was less successful, with significant uncertainty about drivers of water-strike yield.\"}]","Application of Data Mining and Machine Learning Techniques for Geohydrological Datasets in South Africa | 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is the main goal of the study on South African geohydrological datasets?","Question",{"text":75,"@type":76},"To use data-driven modelling to classify relationships between borehole parameters and geological settings, supporting improved groundwater management.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms performed best for static water levels and yield?",{"text":80,"@type":76},"Random Forest Regression and Classification were used to model yield, while Support Vector Regression and Random Forest Classification were used for static water levels.",{"name":82,"@type":73,"acceptedAnswer":83},"How did the results vary between static water level modelling and yield modelling?",{"text":84,"@type":76},"Static water levels were modelled with high accuracy, but yield modelling was less successful, with significant uncertainty about drivers of water-strike 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