A review of the current status of flood modelling for urban flood risk management in the developing countries

Nkwunonwo, Ugonna C.; Whitworth, Malcolm; Baily, Brian

doi:10.1016/j.sciaf.2020.e00269

Cited by 157 publications

(95 citation statements)

References 136 publications

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“…These conditions might exhibit some patterns that facilitate the realization of the risk potential. This can be conducted employing data analytics approaches such as machine learning techniques [16]. Such methods are sufficient to provide predictions for the cases where the data required for conventional hydrological analysis techniques are incomplete or unavailable.…”

Section: Discussionmentioning

confidence: 99%

A participatory approach for identification of micro flood zones in poorly developed urban areas

Mansour¹

2020

acperpro

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The lack of observation technologies and the information records in developing countries restrict the applicability of advanced approaches for identification of flood zones and setting-up flood risk mitigation plans. Greater challenge is encountered in urban and areas where the natural hydrological conditions are altered. The current paper proposes an approach that utilizes the globally available data for the essential hydrological analysis. The approach integrates the spatial-analysis and hydrological modeling approaches with a set of sequential iterative field/desk activities that confine and filter the flood zones and identify the associated risks. The proposed approach involves a systematic system for classification of the zones prone to flood according to the present status and the foreseen flood-risks. The proposed approach is sufficient to identify the micro-flood zones which might not be captured by the traditional analysis methods. The case study of Gaza Strip in Palestine is considered for validation of the approach and identification of the approach potential and limitations.

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Section: Discussionmentioning

confidence: 99%

A participatory approach for identification of micro flood zones in poorly developed urban areas

Mansour¹

2020

acperpro

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“…Within this perspective, flood modelling which predicts flood data (mainly flood water depth, duration and extent, as well as depth-averaged velocities) essentially needed for flood risk/hazard analyses, mapping and assessment plays significant roles [36,37]. Conceptually, flood modelling may be perceived as a scientific technique that numerically or analytically solves relevant governing mathematical equations and generates computer algorithms and codes for fast, continuous and routine simulation of flood data [9]. Quick, continuous and routine provisions of flood data appear to undermine ground survey methods and remote sensing technologies, thus most evidently highlighting the relevance of flood modelling.…”

Section: Flood Modelling and Assessment Of Vulnerability To Flooding mentioning

confidence: 99%

“…Worthy of note within this context is the damage potential of flooding which is debatably unprecedented when compared to other known environmental hazards occurring within natural human environment in recent times [6,7]. Whilst the failure and/or limitation of efforts to tackle flooding are issues in the DCs that clearly require urgent attention [8,9], the need for sustainable development which underpins adaptability and collective resilience of the general public to flooding cannot be disregarded [10,11].…”

Section: Introductionmentioning

confidence: 99%

Flood Risk Analysis for Critical Infrastructure Protection: Issues and Opportunities in Less Developed Societies

Nkwunonwo¹

2021

Issues on Risk Analysis for Critical Infrastructure Protection

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This chapter presents all-important discussions relating to flood risk analysis which arguably is a subject of overwhelming significance within the context of less developed societies, for example Nigeria. Whilst a possible means of eradicating flooding from human environment is inconceivable, debates for more effective flood risk reduction methodologies for critical infrastructure protection must continue. Increased population and urbanisation scenarios drive worsened flood risk which trigger increased efforts for corporate adaptability to flooding. To ensure that social systems can cope with floods, it is important to investigate why best practices in flood risk reduction are not fully applicable. This chapter explores these issues drawing from extant dialogues on flood risk management (FRM). Arguably, the current flood modelling techniques and assessment of vulnerability operations largely do not support a realistic analysis of flood risk. Funnelled through an interpretative research paradigm, the chapter conceives that these limitations fall under five cardinal issues – (1) data, (2) theories and concepts, (3) existing flood risk analyses methods, (4) legislation and policy, and (5) sustainable development. It argues that the realisation of a more effective flood risk reduction for the poorer and less developed societies will depend on effective tackling of these issues which creates opportunities for flood risk analyses through simplified approaches, and use of free and open geospatial data infrastructure.

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“…The prediction model of water accumulation processes based on data-driven requires tremendous rainfall and water accumulation process data of water accumulation points [9]. Before the emergence of urban water accumulation monitoring systems, it was difficult to obtain these data.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Prediction of the Water Accumulation Process of Urban Stormy Accumulation Points Based on Deep Learning

Zhou

Wang

2020

IEEE Access

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Influenced by climate change and urbanization, urban flood frequently occurs and represents a serious challenge for many cities. Therefore, it is necessary to generate refined predictions of urban floods, such as the prediction of water accumulation processes at water accumulation points, which is of great significance for supporting water-related managers to reduce flood losses. In this study, 16 combination schemes of rainfall sensitivity indicators were used to determine the optimal scheme for predicting the depth of accumulated water, and the gradient boosting decision tree (GBDT) algorithm in deep learning was used to build a prediction model of the accumulation process of urban stormy accumulation points. Among the 16 schemes, the relative error of scheme 1 is 15.39%, and the qualified rate is 92.86%. This scheme exhibits the highest accuracy for the prediction results of water accumulation depth. Given this finding, the GBDT algorithm was used to construct a regression prediction model of the water accumulation process based on the collected historical rainfall water accumulation data of 50 water accumulation points. The results demonstrated that the GBDT regression prediction model has a mean relative error of 19.77%, a qualified rate of 82.00%, and a peak average relative error of 5.48%, which verify the validity and applicability of the model for the real-time prediction of the process of water accumulation. INDEX TERMS Urban flood, Deep learning, Water accumulation, Real-time prediction.

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A review of the current status of flood modelling for urban flood risk management in the developing countries

Cited by 157 publications

References 136 publications

A participatory approach for identification of micro flood zones in poorly developed urban areas

A participatory approach for identification of micro flood zones in poorly developed urban areas

Flood Risk Analysis for Critical Infrastructure Protection: Issues and Opportunities in Less Developed Societies

Real-Time Prediction of the Water Accumulation Process of Urban Stormy Accumulation Points Based on Deep Learning

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