On Big Data and Hydroinformatics

Chen, Yiheng; Han, Dawei

doi:10.1016/j.proeng.2016.07.443

Cited by 10 publications

(7 citation statements)

References 10 publications

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“…These types of applications work well in developed areas, with a large number of users and sufficient Internet access. In the less developed urban areas and rural settings of the SADC region, the spatial coverage of this type of data may be limited [56]. In addition, it is very difficult to visualize groundwater from the surface, as it is hidden below layers of soil and rock.…”

Section: Social Media and The Web Datamentioning

confidence: 99%

Big Data Analytics and Its Role to Support Groundwater Management in the Southern African Development Community

Gaffoor

Pietersen

Jovanovic

et al. 2020

Water

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Big data analytics (BDA) is a novel concept focusing on leveraging large volumes of heterogeneous data through advanced analytics to drive information discovery. This paper aims to highlight the potential role BDA can play to improve groundwater management in the Southern African Development Community (SADC) region in Africa. Through a review of the literature, this paper defines the concepts of big data, big data sources in groundwater, big data analytics, big data platforms and framework and how they can be used to support groundwater management in the SADC region. BDA may support groundwater management in SADC region by filling in data gaps and transforming these data into useful information. In recent times, machine learning and artificial intelligence have stood out as a novel tool for data-driven modeling. Managing big data from collection to information delivery requires critical application of selected tools, techniques and methods. Hence, in this paper we present a conceptual framework that can be used to manage the implementation of BDA in a groundwater management context. Then, we highlight challenges limiting the application of BDA which included technological constraints and institutional barriers. In conclusion, the paper shows that sufficient big data exist in groundwater domain and that BDA exists to be used in groundwater sciences thereby providing the basis to further explore data-driven sciences in groundwater management.

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Section: Social Media and The Web Datamentioning

confidence: 99%

Big Data Analytics and Its Role to Support Groundwater Management in the Southern African Development Community

Gaffoor

Pietersen

Jovanovic

et al. 2020

Water

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“…Employing what we have knowledge to take valid decisions may be an automated procedure in several situations. It can be useful to split BD into these three parts (Thompson and Kadiyala, 2014a;2014b;Shaw, 2017;Zhang et al, 2016;Ahmad et al, 2017;Chen and Han, 2016).…”

Section: Getting Mad To Be Smartmentioning

confidence: 99%

Applying big data in water treatment industry: A new era of advance

Ghernaout

2018

Int. j. adv. appl. sci.

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“…In this vein, recent and rapid technological advancements are providing new instrumentation, impressive computational power and huge data storage opportunities to deal with big volumes of hydrological data (Butler, 2014;Tauro et al, 2018). In turn, big data mandate advanced data analysis techniques (Chen and Han, 2016;Chen and Wang, 2018;Blöschl et al, 2019;Sun and Scanlon, 2019;Papacharalampous et al, 2021). Among emerging statistical and data mining methods, machine learning (ML) approaches have had an impressive diffusion in the environmental sciences and specifically in hydrology.…”

Section: Introductionmentioning

confidence: 99%

Feature importance measures to dissect the role of sub-basins in shaping the catchment hydrological response: a proof of concept

Cappelli

Tauro

Apollonio

et al. 2022

Preprint

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Understanding the response of a catchment is a crucial problem in hydrology, with a variety of practical and theoretical implications. Dissecting the role of sub-basins is helpful both for advancing current knowledge on physical processes and for improving the implementation of simulation or forecast models. In this context, recent advancements in sensitivity analysis tools could be worthwhile for bringing out hidden dynamics otherwise not easy to discriminate in complex data driven investigations. In the present work seven feature importance measures are described and tested in a specific and simplified proof of concept case study. In practice, simulated runoff time series are generated for a watershed and its inner 15 sub-basins. A machine learning tool is calibrated using the sub-basins time series for forecasting the watershed runoff. Importance measures are applied on such synthetic hydrological scenario with the aim to investigate the role of each sub-basin in shaping the overall catchment response. This proof of concept offers a simplified representation of the complex dynamics of catchment response. The interesting result is that the discharge at the catchment outlet depends mainly on 3 sub-basins that are consistently identified by alternative sensitivity measures. The proposed approach can be extended to real applications, providing useful insights on the role of each sub-basin also analyzing more complex scenarios.

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On Big Data and Hydroinformatics

Cited by 10 publications

References 10 publications

Big Data Analytics and Its Role to Support Groundwater Management in the Southern African Development Community

Big Data Analytics and Its Role to Support Groundwater Management in the Southern African Development Community

Applying big data in water treatment industry: A new era of advance

Feature importance measures to dissect the role of sub-basins in shaping the catchment hydrological response: a proof of concept

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