Relevant bibliographies by topics / Groundwater level changing prediction

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Academic literature on the topic 'Groundwater level changing prediction'

Author: Grafiati
Published: 28 May 2022
Last updated: 31 July 2025

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Journal articles on the topic "Groundwater level changing prediction"

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Gonzalez, Rebeca Quintero, and Jamal Jokar Arsanjani. "Prediction of Groundwater Level Variations in a Changing Climate: A Danish Case Study." ISPRS International Journal of Geo-Information 10, no. 11 (2021): 792. http://dx.doi.org/10.3390/ijgi10110792.

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Shallow groundwater is a key resource for human activities and ecosystems, and is susceptible to alterations caused by climate change, causing negative socio-economic and environmental impacts, and increasing the need to predict the evolution of the water table. The main objective of this study is to gain insights about future water level changes based on different climate change scenarios using machine learning algorithms, while addressing the following research questions: (a) how will the water table be affected by climate change in the future based on different socio-economic pathways (SSPs
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Zheng, Yufeng, Dong Huang, Xiaoyi Fan, and Lili Shi. "Groundwater Level Prediction for Landslides Using an Improved TANK Model Based on Big Data." Water 16, no. 16 (2024): 2286. http://dx.doi.org/10.3390/w16162286.

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Geological conditions and rainfall intensity are two primary factors that can induce changes in groundwater level, which are one of the major triggering causes of geological disasters, such as collapse, landslides, and debris flow. In view of this, an improved TANK model is developed based on the influence of rainfall intensity, terrain, and geological conditions on the groundwater level in order to effectively predict the groundwater level evolution of rainfall landslides. A trapezoidal structure is used instead of the traditional rectangular structure to define the nonlinear change in a wate
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Wang, Hao, Quan Cai Wang, Xiao Ling Xu, and Qing Wu. "Analysis of Kinematic Behavior on Landslide Groundwater at K144 Section along Da-Yu Highway." Applied Mechanics and Materials 166-169 (May 2012): 1353–57. http://dx.doi.org/10.4028/www.scientific.net/amm.166-169.1353.

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Most of landslides occurred because of the action of water. Dynamics of groundwater is directly related to the stability of landslide. Change of groundwater level is one of the most important factors of landslide prediction. In this paper, We used waterclocks to monitor groundwater level of K144 landslide for two years and found that different slope regions of the groundwater level showed different variation. Groundwater level of unstable region changes between 4m ~ 6m in the sandy mudstone area where average precipitations is about 1000 mm, which were the same trend with the monthly total rai
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Zhong, Shuang, Hui Geng, Fengjun Zhang, Zhaoying Liu, Tianye Wang, and Boyu Song. "Risk Assessment and Prediction of Heavy Metal Pollution in Groundwater and River Sediment: A Case Study of a Typical Agricultural Irrigation Area in Northeast China." International Journal of Analytical Chemistry 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/921539.

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The areas with typical municipal sewage discharge river and irrigation water function were selected as study sites in northeast China. The samples from groundwater and river sediment in this area were collected for the concentrations and forms of heavy metals (Cr(VI), Cd, As, and Pb) analysis. The risk assessment of heavy metal pollution was conducted based on single-factor pollution index (I) and Nemerow pollution index (NI). The results showed that only one groundwater sampling site reached a polluted level of heavy metals. There was a high potential ecological risk of Cd on the N21-2 sampli
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Kajewska-Szkudlarek, Joanna, Justyna Kubicz, and Ireneusz Kajewski. "Correlation approach in predictor selection for groundwater level forecasting in areas threatened by water deficits." Journal of Hydroinformatics 24, no. 1 (2021): 143–59. http://dx.doi.org/10.2166/hydro.2021.059.

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Abstract Reliable long-term groundwater level (GWL) prediction is essential to assess the availability of resources and the risk to drinking water supply in changing climatic and socio-economic conditions, especially in areas with water deficits. The modern approach in this area involves the use of machine learning methods. However, the greatest challenge in these methods lies in the optimization of input selection. The presented research concerns the selection of the best combination of predictors using the Hellwig method. It served as a preprocessing technique before GWL prediction using sup
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Correia, Monica M., Thokozani Kanyerere, Nebo Jovanovic, Jacqueline Goldin, and Moyin John. "Climate and Groundwater Depth Relationships in Selected Breede Gouritz Water Management Area Subregions Between 2009 and 2020." Water 17, no. 13 (2025): 1969. https://doi.org/10.3390/w17131969.

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Groundwater resources are changing under the current climate change trajectory. Mitigation and adaptation measures include understanding the inter-working relationships among all climate variables and water resources, specifically groundwater, since it has less direct impacts than surface waters due to its nature. The Breede Gouritz Water Management Area provides an interesting platform to assess these interdependencies, since they have not been assessed before. To assess any underlying dependencies, a multivariate analysis of independent variables including monthly average temperature, summat
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Ghazavi, Reza, and Haidar Ebrahimi. "Predicting the impacts of climate change on groundwater recharge in an arid environment using modeling approach." International Journal of Climate Change Strategies and Management 11, no. 1 (2019): 88–99. http://dx.doi.org/10.1108/ijccsm-04-2017-0085.

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Purpose Groundwater is an important source of water supply in arid and semi-arid areas. The purpose of this study is to predict the impact of climate change on groundwater recharge in an arid environment in Ilam Province, west of Iran. Design/methodology/approach A three-dimensional transient groundwater flow model (modular finite difference groundwater FLOW model: MODFLOW) was used to simulate the impacts of three climate scenarios (i.e. an average of a long-term rainfall, predicted rainfall in 2015-2030 and three years moving average rainfall) on groundwater recharge and groundwater levels.
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Raphael, Oaikhena Oyanyan* Michael Akong Toko. "POTENTIAL IMPACT OF CLIMATE CHANGE ON GROUNDWATER RESOURCES IN PORT HARCOURT, NIGERIA." Global Journal of Engineering Science and Research Management 5, no. 2 (2018): 9–17. https://doi.org/10.5281/zenodo.1170640.

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Global warming is permanently changing the climate. Hydrological cycle is being intensified with increase in the rate of evaporation, condensation and precipitation resulting in frequent intense rainfall, flooding, sea level rise and drought in different parts of the world. Groundwater is connected to the hydrological cycle. The rate of aquifer’s recharge depends among others mainly on the amount of rainfall. Port Harcourt is part of the globe and not exempted from climate change; and groundwater is the main source of fresh water. Therefore, this study presents a critical evaluation of t
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Pulido-Velazquez, M., S. Peña-Haro, A. Garcia-Prats, et al. "Integrated assessment of the impact of climate and land use changes on groundwater quantity and quality in Mancha Oriental (Spain)." Hydrology and Earth System Sciences Discussions 11, no. 9 (2014): 10319–64. http://dx.doi.org/10.5194/hessd-11-10319-2014.

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Abstract. Climate and land use change (global change) impacts on groundwater systems cannot be studied in isolation, as various and complex interactions in the hydrological cycle take part. Land-use and land-cover (LULC) changes have a great impact on the water cycle and contaminant production and transport. Groundwater flow and storage are changing in response not only to climatic changes but also to human impacts on land uses and demands (global change). Changes in future climate and land uses will alter the hydrologic cycles and subsequently impact the quantity and quality of regional water
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Herath, Madhawa, Tharaka Jayathilaka, Hazi Mohammad Azamathulla, Vishwanadham Mandala, Namal Rathnayake, and Upaka Rathnayake. "Sensitivity Analysis of Parameters Affecting Wetland Water Levels: A Study of Flood Detention Basin, Colombo, Sri Lanka." Sensors 23, no. 7 (2023): 3680. http://dx.doi.org/10.3390/s23073680.

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Wetlands play a vital role in ecosystems. They help in flood accumulation, water purification, groundwater recharge, shoreline stabilization, provision of habitats for flora and fauna, and facilitation of recreation activities. Although wetlands are hot spots of biodiversity, they are one of the most endangered ecosystems on the Earth. This is not only due to anthropogenic activities but also due to changing climate. Many studies can be found in the literature to understand the water levels of wetlands with respect to the climate; however, there is a lack of identification of the major meteoro
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Dissertations / Theses on the topic "Groundwater level changing prediction"

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Сєрікова, О. М. "Прогнозування і управління рівнем ґрунтових вод для підвищення екологічної безпеки забудованих територій України". Thesis, Харківський національний університет міського господарства ім. О.М. Бекетова, 2019. http://essuir.sumdu.edu.ua/handle/123456789/72650.

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Дисертація присвячена підвищенню рівня екологічної безпеки урбанізованих територій, що зазнають підтоплення ґрунтовими водами, за рахунок наукового обґрунтування методів та засобів прогнозування та управління підтопленням. Підтоплення територій не тільки негативно впливає на життєдіяльність населення, а і може привести до виникнення екологічних катастроф та значних матеріальних збитків. Вперше розроблено крайові умови математичної моделі зміни рівня ґрунтових вод, що враховують вплив штучних покриттів поверхні ґрунту і евапотранспірацію для вдосконалення прогнозів та управління рівнем ґрунтов
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Strelnikova, E., and E. Serikova. "Critical changing groundwater level in Ukrainian industrial cities." Thesis, Sumy State University, 2015. http://essuir.sumdu.edu.ua/handle/123456789/40863.

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Paper treats the issue concerned with drastic increasing of groundwater level through the influence of technogenic factors of urban environment. The aim is to present possibilities of proposed mathematical model of prediction of groundwater level increasing for different areas of urban territory.
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Cheng, Chun-Wei, and 鄭鈞瑋. "Prediction of Groundwater Level Based on EMD and ANN." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/82875331341906604373.

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碩士<br>淡江大學<br>水資源及環境工程學系碩士班<br>100<br>Recently, the change of hydrological environment is being accelerated significantly by the impact of global warming due to climate change. It is important for the management of groundwater resources because of limited water resource. However, the use of groundwater resources efficiently relies on a more real and precise prediction model. In this study, it will seek for a reasonable and effective way to predict the changes of groundwater level by modifying the traditional method. HHT is a relatively new method to analyze time series data that possess in
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Yu, Meng-Kai, and 游孟楷. "Applying Nonparametric Methods and Statistical Learning to Hydro-Climatic Analysis and Groundwater Level Prediction." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/19288944071062211655.

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碩士<br>國立中興大學<br>土木工程學系所<br>105<br>Owing to the high applicability and robust performance of statistical methods, various hydrology-related applications have been regarded as a salient research track. This study aims to demonstrate the application of nonparametric methods and statistical learning for the analysis and forecasting of groundwater level in Pingtung alluvial plain in Taiwan. Data acquired in this region include groundwater level (151 monitoring wells), river flow (14 stations) and water level (5 stations), average daily temperature (2 stations) and rainfall (20 stations). This st
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Wang, Chun-Hao, and 王俊皓. "Coupling of TRIGRS and TOPMODEL in estimation of groundwater level for shallow landslide prediction." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/b3fngv.

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碩士<br>國立中央大學<br>應用地質研究所<br>105<br>Rainfall infiltration is the main reason most of the shallow slope failure because the rise of the groundwater level will make the weight of the slope to increase and the shear strength to deteriorate. In the past, the USGS proposed TRIGRS model combining rainfall infiltration and slope stability which is the one-dimensional vertical infiltration transient model to analyze the groundwater level. The model assumed that soil body of each grid as an independent individual and didn’t consider of hydrological influence, so it can not reflect the groundwater level r
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Book chapters on the topic "Groundwater level changing prediction"

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Shukla, Prabhakar, and Raj Mohan Singh. "Groundwater System Modelling and Sensitivity of Groundwater Level Prediction in Indo-Gangetic Alluvial Plains." In Groundwater. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-5789-2_5.

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Raturi, Mayank, Deepak Khare, and Nitesh Patidar. "Machine Learning Models for Groundwater Level Prediction." In The Springer Series in Applied Machine Learning. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64403-0_5.

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Kumar, Sumant, and Surjeet Singh. "Forecasting Groundwater Level Using Hybrid Modelling Technique." In Management of Natural Resources in a Changing Environment. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12559-6_6.

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Ng, Soon Min, Mohd Ashraf Mohamad Ismail, and Ismail Abustan. "Parameter Optimization of Conceptual Tank Model for Groundwater Level Prediction." In Proceedings of AICCE'19. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32816-0_60.

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Shah, Saurabh A., Dinesh G. Harkut, and Sayali M. Thakre. "DLSTM with Adam Waterwheel Optimization for Groundwater Level Prediction in India." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-6992-6_23.

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Mbouopda, Michael Franklin, Thomas Guyet, Nicolas Labroche, and Abel Henriot. "Experimental Study of Time Series Forecasting Methods for Groundwater Level Prediction." In Advanced Analytics and Learning on Temporal Data. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-24378-3_3.

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Bonkoungou, Abdoul Aziz, Souleymane Zio, Aminata Sabane, Rodrique Kafando, Abdoul Kader Kabore, and Tegawendé F. Bissyande. "A Comparison of AI Methods for Groundwater Level Prediction in Burkina Faso." In IFIP Advances in Information and Communication Technology. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63219-8_1.

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Ingle, Ritesh S., Sandeep Samantaray, and Abinash Sahoo. "Groundwater Level Prediction Based on Hybrid GRU with Grey Wolf Optimizer Approach." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-4892-1_42.

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Yang, Junyan, Jianhua Wu, Yan Tian, Xiaomei Kou, and Song He. "Characteristics of Groundwater Level Dynamics in Xi’an (2010–2020) and Groundwater Level Prediction for 2030 Using Regression Analysis and BP Neural Network: Implication to Groundwater Resources Management." In Springer Hydrogeology. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-82194-3_9.

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Broyd, T. W., R. C. Hall, and R. W. Paige. "The Prediction of Contamination Potential in Coastal Aquifers from Proposed Shallow Disposal Sites for Low-Level Radioactive Wastes." In Groundwater Contamination: Use of Models in Decision-Making. Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-009-2301-0_43.

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Conference papers on the topic "Groundwater level changing prediction"

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JaeSeong, Park. "Prediction of groundwater level using satellite image and multivariate GRU." In Remote Sensing for Agriculture, Ecosystems, and Hydrology XXVI, edited by Christopher M. Neale, Antonino Maltese, Caroline Nichol, and Charles R. Bostater. SPIE, 2024. http://dx.doi.org/10.1117/12.3031436.

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Saha, Aditi, Mohammad Rahman, and Fan Wu. "Groundwater Level Prediction: Analyzing the Performance of LSTM and QLSTM Model." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825269.

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Tiwari, Abhishek, and Himanshu. "A Hybrid Machine Learning and Deep Learning Model for Time-Series Groundwater Level Prediction." In 2025 Fourth International Conference on Smart Technologies, Communication and Robotics (STCR). IEEE, 2025. https://doi.org/10.1109/stcr62650.2025.11019322.

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R, Sriram, and Jasmeen. "Groundwater Level Prediction: A Novel Study on Machine Learning Based Approach with Regression Models for Sustainable Resource Management." In 2023 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM). IEEE, 2023. http://dx.doi.org/10.1109/ccem60455.2023.00028.

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Krishnamurthy, Siddhartha, Aric Aumann, Stephen Rizzi, and D. Douglas. "Auralization of Rotorcraft Periodic Flyover Noise from Design Predictions." In Vertical Flight Society 74th Annual Forum & Technology Display. The Vertical Flight Society, 2018. http://dx.doi.org/10.4050/f-0074-2018-12664.

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This paper combines the synthesis and propagation of rotorcraft sounds, or auralizations, with their noise predictions and design optimization to incorporate human response into the process of creating low-noise rotorcraft. The NASA Auralization Framework is described and used to auralize the sounds of an AS350 helicopter main rotor at a ground observer from predicted acoustic pressure time histories. This paper uses spherical phase interpolation as part of the sound synthesis. This interpolation is an improvement over the linear phase interpolation described in previous work, which can introd
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Kommineni, Madhuri, K. Veniha Reddy, K. Jagathi, B. Dushyanth Reddy, Roshini A, and V. Bhavani. "Groundwater Level Prediction Using Modified Linear Regression." In 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2020. http://dx.doi.org/10.1109/icaccs48705.2020.9074313.

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Beixiao Shi and Changjun Z. "Groundwater level prediction using ARMA-ANN model." In 2009 International Conference on Test and Measurement (ICTM). IEEE, 2009. http://dx.doi.org/10.1109/ictm.2009.5413048.

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Changjun Zhu, Sha Li, and Liping Wu. "Time series analysis mmethods of the groundwater level prediction." In 2009 2nd International Conference on Power Electronics and Intelligent Transportation System (PEITS). IEEE, 2009. http://dx.doi.org/10.1109/peits.2009.5406889.

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Tiwari, Vivek, and Manikant Verma. "Prediction Of Groundwater Level Using Advance Machine Learning Techniques." In 2023 3rd International Conference on Intelligent Technologies (CONIT). IEEE, 2023. http://dx.doi.org/10.1109/conit59222.2023.10205583.

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Ito, Yoshinori, Hideaki Marui, Kousei Yamabe, and Wataru Sagara. "Prediction of groundwater level fluctuation in landslide area using genetic algorithm." In 4th Regional Symposium on Landslides in the Adriatic - Balkan Region. Društvo za geotehniku u Bosni i Hercegovini, 2019. http://dx.doi.org/10.35123/resylab_2019_25.

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Reports on the topic "Groundwater level changing prediction"

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Abed El Rahman, Hassoun. Report on gaps in the European Ocean Observing and Forecasting System. EuroSea, 2023. http://dx.doi.org/10.3289/eurosea_d1.9.

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The European Ocean Observing and Forecasting System (EOOFS) plays a pivotal role in understanding, monitoring, forecasting, and managing the complex dynamics and resources of Europe's Seas. It serves as a critical interdisciplinary system for addressing a myriad of challenges, from climate change impacts to marine resources management. However, to ensure its continued effectiveness, it is essential to identify and address the gaps within this system and provide actionable recommendations for improvements at short- and long-term. Therefore, this document serves as a baseline that can guide the
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