In accordance with the rapid proliferation of machine learning (ML) and data management, ML applications have evolved to encompass all engineering disciplines. Owing to the importance of the world’s water supply throughout the rest of this century, much research has been concentrated on the application of ML strategies to integrated water resources management (WRM). Thus, a thorough and well-organized review of that research is required. To accommodate the underlying knowledge and interests of both artificial intelligence (AI) and the unresolved issues of ML in WRM, this overview divides the core fundamentals, major applications, and ongoing issues into two sections. First, the basic applications of ML are categorized into three main groups, prediction, clustering, and reinforcement learning. Moreover, the literature is organized in each field according to new perspectives, and research patterns are indicated so attention can be directed toward where the field is headed. In the second part, the less investigated field of WRM is addressed to provide grounds for future studies. The widespread applications of ML tools are projected to accelerate the formation of sustainable WRM plans over the next decade.

/pdf/application-of-machine-learning-in-water-resources-2ej00yo2.pdf

Application of Machine Learning in Water Resources Management: A Systematic Literature Review

The ability to predict urban floods is crucial for reducing potential losses. Previous studies suggest that a multimodel combination is an effective way to improve the prediction performance of urban flood models; however, few studies have systematically investigated the impact of candidate models on the performance of the integrated model. Therefore, this study proposes a multimodel integrated forecasting method for urban flooding from the perspective of the response relationship between the candidate models and integrated model. The results of this study suggest that the prediction error of the proposed was reduced by 46.9%–64.6% compared with that of the single model. The results of various candidate model combinations indicate that there is a threshold effect for the number of candidate models in the integrated model; the integrated model with six candidate models exhibited the highest prediction accuracy. However, the increase in the number of candidate models was accompanied by a significant decrease in computational efficiency of the integrated model. Based on the accuracy and timeliness requirements of urban flood prediction, a scheme combining gradient lifting decision tree, random forest, back propagation, and support vector machine models was found to be the best candidate model combination scheme. The real-time warning results of the aforementioned combination model provided superior warning performance. The results of this study provide a reference for the construction of more suitable urban flood models, real-time forecasting, and warnings. 

Adaptive selection and optimal combination scheme of candidate models for real-time integrated prediction of urban flood

A novel AI-based model for real-time flooding image recognition using super-resolution generative adversarial network

A Bayesian networks approach for the integrated assessment of climate change impacts on water quality

Extreme sea levels and coastal flooding are projected to be among the most uncertain and severe consequences of climate change. In response, a wide development of coastal vulnerability assessment methodologies has been observed in research to support societal resilience to future coastal flood risks. This work aims to explore the scope of application of index-based methodologies for coastal vulnerability assessment, in terms of their suitability to convey information on variations in climate variables potentially leading to sea-level changes and inundation. For this purpose, the InVEST Coastal Vulnerability model was coupled for the first time with the ERA5 reanalysis and used to develop a case study assessment of the biophysical exposure component of vulnerability to coastal flooding for Liguria, an Italian coastal region facing the Mediterranean Sea. Different scenarios of wind speed and wave power were created in order to test the sensitivity of this approach to climate data inputs. The results support the applicability of this approach to provide a preliminary grasp of local vulnerability to coastal inundation. Yet, this work also highlights how the method’s data aggregation and indicator computation processes result in its insensitivity to wind and wave variations, and therefore in its unsuitability to reproduce climate scenarios. The implications of these findings for research methodology and regarding the operationalisation of vulnerability assessment results are discussed.

/pdf/climate-scenarios-for-coastal-flood-vulnerability-1okvj3fr.pdf

Climate Scenarios for Coastal Flood Vulnerability Assessments: A Case Study for the Ligurian Coastal Region

Due to a combination of climate change and urbanization, the instances of pluvial flooding are expected to increase in the next decades posing raising threats to properties, people and productive assets. Predicting and mapping pluvial flood-prone areas is becoming a crucial step in flood mitigation and early warnings, as well as climate change adaptation strategies, to be incorporate in urban planning. Most commonly applied machine learning (ML) procedures for pluvial flood risk assessment, neglect to account for spatio-temporal constraints, leading to overoptimistic models that underestimate the prediction error. In this paper, we propose a novel ML-based methodology for pluvial flood risk prediction in the Metropolitan City of Venice which, introducing a features selection process and spatio-temporal cross-validation, permits to reduce overfitting of the resulting ML models. Spatio-temporal characteristics of floods are derived from a dataset of 60 historical events occurred in the area between 1995 and 2020. Logistic Regression (LR), Neural Networks (NN) and Random Forest (RF) models are applied to identify and prioritize sub-areas that are more likely to be affected by pluvial flood risk, considering the daily precipitation amount and 12 different triggering factors. The models were validated using Random Cross-Validation (R-CV) and Leave Location and Time Out cross-validation (LLTO-CV), that split data in training and validation set considering both time and space. In addition, a forward features selection procedure was applied to identify the features, among the triggering factors, that better face spatio-temporal overfitting in pluvial flood prediction based on the Area Under the Curve (AUC) score. Results suggest that Logistic Regression and LLTO-CV represent the most reliable model to predict pluvial flood events in new spatio-temporal conditions, while, among the triggering factors, distance to river and distance to road resulted the prominent ones. 

Spatio-temporal cross-validation to predict pluvial flood events in the Metropolitan City of Venice

Review of organisations, methods and tools for the provision of relevant climate services for different sectors of society

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Spatio-temporal cross-validation to predict pluvial flood events in the Metropolitan City of Venice

Review of organisations, methods and tools for the provision of relevant climate services for different sectors of society