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Water Resources and Flood Management Using Artificial Intelligence and Big-Data Mining

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A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Environmental Science and Engineering".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 25399

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Special Issue Editors

Dr. Mawuli Dzakpasu

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Guest Editor

1. University College Dublin, Belfield, Dublin, Ireland;
2. School of Environmental and Municipal Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, Shaanxi Province, China
Interests: treatment wetlands; alternative water resources technologies; sustainable water management; urban water systems analysis; water resources and quality forecasting
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Guangtao Fu

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Assistant Guest Editor

Centre for Water Systems, University of Exeter, North Park Road, Exeter EX4 4QF, UK
Interests: artificial intelligence; water system analysis; flood management; system resilience analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The fact at modern cities face increasing risks due to the increase in frequency and extent of extreme events such as urban floods or extended dry periods cannot be overemphasized. Moreover, the increasing water demand in urban areas and freshwater withdrawal calls for improved water efficiency. Projected increases in extreme rainfall events may lead to more severe flooding, the consequences of which will be even more severe as urbanization forces new populations into floodplains. Increasing attention calls for the development of forecasting systems with high spatial resolution and adequate lead-time to cope with changes to water resources and increasing flood hazards. An accurate and timely warning, as necessary information, can ensure public preparedness for flooding and drought events.

Significant advances in flood forecasting have been achieved through a range of improvements in observing capabilities, modelling techniques, and decision support systems. Notable improvements are satellite and radio detection and ranging (radar) observations and associated computer modelling techniques for the use of these data to produce lead times varying from hours to days.

New techniques for merging multiple sources of information, such as satellite-based, radar, and gauged rainfall, as well as numerical weather prediction model outputs, can extend lead-times and enhance the quality of ensemble forecasts. Improved real-time rainfall estimates and forecasts give rise to the prospect of reliable water resources and flood forecasts. This prospect is further enhanced by advances in computing technologies, coupled with big data mining. These techniques have boosted data-driven applications, among which artificial intelligence (AI) technology, bearing flexibility and scalability in pattern extraction, has modernized not only scientific thinking but also predictive applications.

Water resource assurance and flood hazard mitigation efforts may involve forecasting of reservoir inflows, river flows, and flooding at different lead times and/or scales. Modern technologies such as, but not limited to, AI, big data mining, multiple data aggregation/ensembles, and model ensembles offer some potential avenues. Furthermore, analyses of impacts, risks, uncertainty, vulnerability, resilience, and scenarios coupled with policy-oriented suggestions will give insights. Moreover, the use of geological information systems for visual presentation is essential and helpful in decision making.

This Special Issue of the International Journal of Environmental Research and Public Health aims at exploring recent advances in AI for water resources and flood management. Contributions on interdisciplinary approaches to modelling the complexity of water resources and flood hazards-related issues are welcome. Additionally, contributions with integrated solutions at local, regional or global scales are encouraged.

Dr. Mawuli Dzakpasu
Dr. Guangtao Fu
Guest Editors

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Keywords

Artificial Intelligence
Artificial neural network
Machine learning
Big data
Hydrology
Water resource management
Flood inundation forecast
Flood early warning system
Geological information system
Remote sensing

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Published Papers (7 papers)

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Research

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17 pages, 1244 KiB

Open AccessArticle

Determining the Efficiency of the Sponge City Construction Pilots in China Based on the DEA-Malmquist Model

by Heng Zhang, Qian Chang, Sui Li and Jiandong Huang

Int. J. Environ. Res. Public Health 2022, 19(18), 11195; https://doi.org/10.3390/ijerph191811195 - 6 Sep 2022

Cited by 5 | Viewed by 2040

Abstract

Sponge city construction (SCC) has improved the quality of the urban water ecological environment, and the policy implementation effect of SCC pilots is particularly remarkable. Based on the data envelopment analysis (DEA) model, this study employed the related index factors such as economy, ecology, infrastructure, and the population of the pilot city as the input, and the macro factors of SCC as the output, to scientifically evaluate the relative efficiency between the SCC pilots in China. Eleven representative SCC pilots were selected for analysis from the perspectives of static and dynamic approaches, and comparisons based on the horizontal analysis of the efficiency of SCC pilots were conducted and some targeted policy suggestions are put forward, which provide a reliable theoretical model and data support for the efficiency evaluation of SCC. This paper can be used as a reference for construction by providing a DEA model for efficiency evaluation methods and thus helps public sector decision makers choose the appropriate construction scale for SCC pilots. Full article

(This article belongs to the Special Issue Water Resources and Flood Management Using Artificial Intelligence and Big-Data Mining)

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26 pages, 1520 KiB

Open AccessArticle

Comparison of Missing Data Infilling Mechanisms for Recovering a Real-World Single Station Streamflow Observation

by Thelma Dede Baddoo, Zhijia Li, Samuel Nii Odai, Kenneth Rodolphe Chabi Boni, Isaac Kwesi Nooni and Samuel Ato Andam-Akorful

Int. J. Environ. Res. Public Health 2021, 18(16), 8375; https://doi.org/10.3390/ijerph18168375 - 7 Aug 2021

Cited by 15 | Viewed by 3525

Abstract

Reconstructing missing streamflow data can be challenging when additional data are not available, and missing data imputation of real-world datasets to investigate how to ascertain the accuracy of imputation algorithms for these datasets are lacking. This study investigated the necessary complexity of missing data reconstruction schemes to obtain the relevant results for a real-world single station streamflow observation to facilitate its further use. This investigation was implemented by applying different missing data mechanisms spanning from univariate algorithms to multiple imputation methods accustomed to multivariate data taking time as an explicit variable. The performance accuracy of these schemes was assessed using the total error measurement (TEM) and a recommended localized error measurement (LEM) in this study. The results show that univariate missing value algorithms, which are specially developed to handle univariate time series, provide satisfactory results, but the ones which provide the best results are usually time and computationally intensive. Also, multiple imputation algorithms which consider the surrounding observed values and/or which can understand the characteristics of the data provide similar results to the univariate missing data algorithms and, in some cases, perform better without the added time and computational downsides when time is taken as an explicit variable. Furthermore, the LEM would be especially useful when the missing data are in specific portions of the dataset or where very large gaps of ‘missingness’ occur. Finally, proper handling of missing values of real-world hydroclimatic datasets depends on imputing and extensive study of the particular dataset to be imputed. Full article

(This article belongs to the Special Issue Water Resources and Flood Management Using Artificial Intelligence and Big-Data Mining)

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17 pages, 4732 KiB

Open AccessArticle

Future Changes in Simulated Evapotranspiration across Continental Africa Based on CMIP6 CNRM-CM6

by Isaac Kwesi Nooni, Daniel Fiifi T. Hagan, Guojie Wang, Waheed Ullah, Jiao Lu, Shijie Li, Mawuli Dzakpasu, Nana Agyemang Prempeh and Kenny T. C. Lim Kam Sian

Int. J. Environ. Res. Public Health 2021, 18(13), 6760; https://doi.org/10.3390/ijerph18136760 - 23 Jun 2021

Cited by 18 | Viewed by 3419

Abstract

The main goal of this study was to assess the interannual variations and spatial patterns of projected changes in simulated evapotranspiration (ET) in the 21st century over continental Africa based on the latest Shared Socioeconomic Pathways and the Representative Concentration Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) provided by the France Centre National de Recherches Météorologiques (CNRM-CM) model in the Sixth Phase of Coupled Model Intercomparison Project (CMIP6) framework. The projected spatial and temporal changes were computed for three time slices: 2020–2039 (near future), 2040–2069 (mid-century), and 2080–2099 (end-of-the-century), relative to the baseline period (1995–2014). The results show that the spatial pattern of the projected ET was not uniform and varied across the climate region and under the SSP-RCPs scenarios. Although the trends varied, they were statistically significant for all SSP-RCPs. The SSP5-8.5 and SSP3-7.0 projected higher ET seasonality than SSP1-2.6 and SSP2-4.5. In general, we suggest the need for modelers and forecasters to pay more attention to changes in the simulated ET and their impact on extreme events. The findings provide useful information for water resources managers to develop specific measures to mitigate extreme events in the regions most affected by possible changes in the region’s climate. However, readers are advised to treat the results with caution as they are based on a single GCM model. Further research on multi-model ensembles (as more models’ outputs become available) and possible key drivers may provide additional information on CMIP6 ET projections in the region. Full article

(This article belongs to the Special Issue Water Resources and Flood Management Using Artificial Intelligence and Big-Data Mining)

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14 pages, 2490 KiB

Open AccessArticle

Spatial Distribution and Environmental Significance of Phosphorus Fractions in River Sediments and Its Influencing Factor from Hongze and Tiaoxi Watersheds, Eastern China

by Ja Bawk Marip, Xuyin Yuan, Hai Zhu, Isaac Kwesi Nooni, Solomon O. Y. Amankwah, Nana Agyemang Prempeh, Eyram Norgbey, Taitiya Kenneth Yuguda and Zaw Myo Khaing

Int. J. Environ. Res. Public Health 2020, 17(16), 5787; https://doi.org/10.3390/ijerph17165787 - 10 Aug 2020

Cited by 14 | Viewed by 2847

Abstract

This study explored the spatial distribution of phosphorus fractions in river sediments and analyzed the relationship between different phosphorus fractions and their environmental influence on the sediments within different watersheds in Eastern China. River sediments from two inflow watersheds (Hongze and Tiaoxi) to Hongze and Taihu Lake in Eastern China were analyzed by the sequential extraction procedure. Five fractions of sedimentary phosphorus, including freely sorbed phosphorus (NH₄Cl-P), redox-sensitive phosphorus (BD-P), bound phosphorus metal oxide (NaOH-P), bound phosphorus calcium (HCl-P), and residual phosphorus (Res-P) were all analyzed. The orders of rankings for the P fractions of the rivers Anhe and Suihe were HCl-P > NaOH-P > BD-P > NH₄Cl-P and HCl-P > BD-P > NaOH-P > NH₄Cl-P, respectively. For the rank order of the Hongze watershed, HCl-P was higher while the NH₄Cl-P contents were significantly lower. The rank order for the Dongtiaoxi River was NaOH-P > HCl-P > BD-P > NH₄Cl-P, and that of Xitiaoxi River was NaOH-P > BD-P > HCl-P > NH₄Cl-P. Compared with the phosphorus forms of the Tiaoxi watershed, NaOH-P contents were significantly higher compared to HCl-P, which was significantly higher in the Hongze watershed. In comparison, NH₄Cl-P contents were significantly lower in both. Variations may be attributed to differential discharge of the P form in the watershed due to land-use changes and urban river ambient conditions. Full article

(This article belongs to the Special Issue Water Resources and Flood Management Using Artificial Intelligence and Big-Data Mining)

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14 pages, 5692 KiB

Open AccessArticle

A Sediment Diagenesis Model of Seasonal Nitrate and Ammonium Flux Spatial Variation Contributing to Eutrophication at Taihu, China

by Linda Sarpong, Yiping Li, Eyram Norgbey, Amechi S. Nwankwegu, Yue Cheng, Salifu Nasiru, Isaac Kwesi Nooni and Victor Edem Setordjie

Int. J. Environ. Res. Public Health 2020, 17(11), 4158; https://doi.org/10.3390/ijerph17114158 - 11 Jun 2020

Cited by 7 | Viewed by 3385

Abstract

Algal blooms have thrived on the third-largest shallow lake in China, Taihu over the past decade. Due to the recycling of nutrients such as nitrate and ammonium, this problem has been difficult to eradicate. Sediment flux, a product of diagenesis, explains the recycling of nutrients. The objective was to simulate the seasonal spatial variations of nitrate and ammonium flux. In this paper, sediment diagenesis modeling was applied to Taihu with Environmental Fluid Dynamics Code (EFDC). Latin hypercube sampling was used to create an input file from twelve (12) nitrogen related parameters of sediment diagenesis and incorporated into the EFDC. The results were analyzed under four seasons: summer, autumn, winter, and spring. The concentration of NH₄–N in the sediment–water column increased from 2.744903 to 22.38613 (g/m³). In summer, there was an accumulation of ammonium in the water column. In autumn and winter, the sediment was progressively oxidized. In spring, low-oxygen conditions intensify denitrification. This allows algal blooms to continue to thrive, creating a threat to water quality sustainability. The sediment diagenesis model, coupled with water quality measured data, showed an average relative error for Total Nitrogen (TN) of 38.137%, making the model suitable. Future studies should simulate phosphate flux and measure sediment fluxes on the lake. Full article

(This article belongs to the Special Issue Water Resources and Flood Management Using Artificial Intelligence and Big-Data Mining)

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26 pages, 3441 KiB

Open AccessArticle

Data-Driven Modeling and the Influence of Objective Function Selection on Model Performance in Limited Data Regions

by Thelma Dede Baddoo, Zhijia Li, Yiqing Guan, Kenneth Rodolphe Chabi Boni and Isaac Kwesi Nooni

Int. J. Environ. Res. Public Health 2020, 17(11), 4132; https://doi.org/10.3390/ijerph17114132 - 10 Jun 2020

Cited by 4 | Viewed by 2817

Abstract

The identification of unit hydrographs and component flows from rainfall, evapotranspiration and streamflow data (IHACRES) model has been proven to be an efficient yet basic model to simulate rainfall–runoff processes due to the difficulty in obtaining the comprehensive data required by physical models, especially in data-scarce, semi-arid regions. The success of a calibration process is tremendously dependent on the objective function chosen. However, objective functions have been applied largely in over daily and monthly scales and seldom over sub-daily scales. This study, therefore, implements the IHACRES model using ‘hydromad’ in R to simulate flood events with data limitations in Zhidan, a semi-arid catchment in China. We apply objective function constraints by time aggregating the commonly used Nash–Sutcliffe efficiency into daily and hourly scales to investigate the influence of objective function constraints on the model performance and the general capability of the IHACRES model to simulate flood events in the study watershed. The results of the study demonstrated the advantage of the finer time-scaled hourly objective function over its daily counterpart in simulating runoff for the selected flood events. The results also indicated that the IHACRES model performed extremely well in the Zhidan watershed, presenting the feasibility of the use of the IHACRES model to simulate flood events in data scarce, semi-arid regions. Full article

(This article belongs to the Special Issue Water Resources and Flood Management Using Artificial Intelligence and Big-Data Mining)

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Review

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19 pages, 2962 KiB

Open AccessReview

Review of Urban Flood Resilience: Insights from Scientometric and Systematic Analysis

by Meiyan Gao, Zongmin Wang and Haibo Yang

Int. J. Environ. Res. Public Health 2022, 19(14), 8837; https://doi.org/10.3390/ijerph19148837 - 21 Jul 2022

Cited by 19 | Viewed by 5916

Abstract

In recent decades, climate change is exacerbating meteorological disasters around the world, causing more serious urban flood disaster losses. Many solutions in related research have been proposed to enhance urban adaptation to climate change, including urban flooding simulations, risk reduction and urban flood-resistance capacity. In this paper we provide a thorough review of urban flood-resilience using scientometric and systematic analysis. Using Cite Space and VOS viewer, we conducted a scientometric analysis to quantitively analyze related papers from the Web of Science Core Collection from 1999 to 2021 with urban flood resilience as the keyword. We systematically summarize the relationship of urban flood resilience, including co-citation analysis of keywords, authors, research institutions, countries, and research trends. The scientometric results show that four stages can be distinguished to indicate the evolution of different keywords in urban flood management from 1999, and urban flood resilience has become a research hotspot with a significant increase globally since 2015. The research methods and progress of urban flood resilience in these four related fields are systematically analyzed, including climate change, urban planning, urban system adaptation and urban flood-simulation models. Climate change has been of high interest in urban flood-resilience research. Urban planning and the adaptation of urban systems differ in terms of human involvement and local policies, while more dynamic factors need to be jointly described. Models are mostly evaluated with indicators, and comprehensive resilience studies based on traditional models are needed for multi-level and higher performance models. Consequently, more studies about urban flood resilience based on local policies and dynamics within global urban areas combined with fine simulation are needed in the future, improving the concept of resilience as applied to urban flood-risk-management and assessment. Full article

(This article belongs to the Special Issue Water Resources and Flood Management Using Artificial Intelligence and Big-Data Mining)

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