Enhancing community resilience in arid regions: A smart framework for flash flood risk assessment

Nakhaei, Mahdi; Nakhaei, Pouria; Gheibi, Mohammad; Chahkandi, Benyamin; Behzadian, Kourosh; Chen, Albert S.; Campos, Luiza C.

doi:10.1016/j.ecolind.2023.110457

Cited by 16 publications

(4 citation statements)

References 84 publications

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“…Seasonal precipitation variability and land use changes were found to be two important variables that affect Pakistani streamflow forecasting accuracy. The AI algorithms (SVM, ANN) employed in flood prediction and classification operate on established assumptions and are trained using accessible data, potentially lacking a comprehensive grasp of the intricate elements shaping flash floods [23].…”

Section: Resultsmentioning

confidence: 99%

Enhancing Flood Resilience: Streamflow Forecasting and Inundation Modeling in Pakistan

Shehzadi,

Ali,

Abideen

et al. 2023

The 4th International Electronic Conference on Applied Sciences

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Section: Resultsmentioning

confidence: 99%

Enhancing Flood Resilience: Streamflow Forecasting and Inundation Modeling in Pakistan

Shehzadi,

Ali,

Abideen

et al. 2023

The 4th International Electronic Conference on Applied Sciences

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“…Climate variability introduces new challenges in predicting future flood scenarios, as shifts in precipitation patterns, temperature, and sea level contribute to altering the hydrological cycle. The model's reliance on historical data may not adequately account for these evolving climatic conditions, leading to potential inaccuracies in long-term predictions [64]. Furthermore, uncertainties in data quality and availability can significantly affect the model's performance.…”

Section: Evaluation Of Lstm and Rnn Networkmentioning

confidence: 99%

Comparative Evaluation of Deep Learning Techniques in Streamflow Monthly Prediction of the Zarrine River Basin

Nakhaei,

Zanjanian,

Nakhaei

et al. 2024

Water

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Predicting monthly streamflow is essential for hydrological analysis and water resource management. Recent advancements in deep learning, particularly long short-term memory (LSTM) and recurrent neural networks (RNN), exhibit extraordinary efficacy in streamflow forecasting. This study employs RNN and LSTM to construct data-driven streamflow forecasting models. Sensitivity analysis, utilizing the analysis of variance (ANOVA) method, also is crucial for model refinement and identification of critical variables. This study covers monthly streamflow data from 1979 to 2014, employing five distinct model structures to ascertain the most optimal configuration. Application of the models to the Zarrine River basin in northwest Iran, a major sub-basin of Lake Urmia, demonstrates the superior accuracy of the RNN algorithm over LSTM. At the outlet of the basin, quantitative evaluations demonstrate that the RNN model outperforms the LSTM model across all model structures. The S3 model, characterized by its inclusion of all input variable values and a four-month delay, exhibits notably exceptional performance in this aspect. The accuracy measures applicable in this particular context were RMSE (22.8), R2 (0.84), and NSE (0.8). This study highlights the Zarrine River’s substantial impact on variations in Lake Urmia’s water level. Furthermore, the ANOVA method demonstrates exceptional performance in discerning the relevance of input factors. ANOVA underscores the key role of station streamflow, upstream station streamflow, and maximum temperature in influencing the model’s output. Notably, the RNN model, surpassing LSTM and traditional artificial neural network (ANN) models, excels in accurately mimicking rainfall–runoff processes. This emphasizes the potential of RNN networks to filter redundant information, distinguishing them as valuable tools in monthly streamflow forecasting.

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“…This can aid in the prevention and resolution of issues, lowering the risk of water contamination, flooding and other UWI-related issues [62]. Furthermore, developing smart frameworks based on artificial intelligence for fast evaluation of flood risk can accelerate the flood risk assessment to make informed decisions and hence enhance community resilience [63].…”

Section: Resilience-enhancing Strategiesmentioning

confidence: 99%

Resilience Assessment in Urban Water Infrastructure: A Critical Review of Approaches, Strategies and Applications

Asghari,

Piadeh,

Egyir

et al. 2023

Preprint

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Resilient urban water infrastructure (UWI) is essential to maintaining public health and safety in urban areas and preventing consistent disruptions. However, UWI is vulnerable to a wide range of shocks and stresses due to the complex nature and interdependency of its components. The primary objective of this study is to evaluate the advances in resilience assessment of UWI comprising water supply, stormwater, and wastewater systems. This assessment involves examining bibliometric analysis, developed frameworks to understand resilience concepts for infrastructure and society, strategies for improving resilience, and resilience indicators. The study findings indicate that resilience assessment has primarily been conducted in developed countries, highlighting the macroeconomic importance of UWI. Three major areas were identified for analysing resilience in UWI: system design, development of resilience concepts, and implementation of green infrastructure. It was also found that while resilience is commonly defined based on technical approaches, a more thorough understanding of resilience can be obtained through holistic approach. While strategies such as system upgrade, decentralisation, digitalisation, and nature-based solutions can enhance resilience in UWI, they may be insufficient to achieve all resilience indicators. To address the issue of proper comparison of different resilience options, comprehensive and qualified indicators and metrics should be extensively examined in future.

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Enhancing community resilience in arid regions: A smart framework for flash flood risk assessment

Cited by 16 publications

References 84 publications

Enhancing Flood Resilience: Streamflow Forecasting and Inundation Modeling in Pakistan

Enhancing Flood Resilience: Streamflow Forecasting and Inundation Modeling in Pakistan

Comparative Evaluation of Deep Learning Techniques in Streamflow Monthly Prediction of the Zarrine River Basin

Resilience Assessment in Urban Water Infrastructure: A Critical Review of Approaches, Strategies and Applications

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