Urban flood-risk mapping is an important tool for the mitigation of flooding in view of continuing urbanization and climate change. However, many developing countries lack sufficiently detailed data to produce reliable risk maps with existing methods. Thus, improved methods are needed that can help managers and decision makers to combine existing data with more soft semi-subjective data, such as citizen observations of flood-prone and vulnerable areas in view of existing settlements. Thus, we present an innovative approach using the semi-subjective Analytic Hierarchy Process (AHP), which integrates both subjective and objective assessments, to help organize the problem framework. This approach involves measuring the consistency of decision makers’ judgments, generating pairwise comparisons for choosing a solution, and considering criteria and sub-criteria to evaluate possible options. An urban flood-risk map was created according to the vulnerabilities and hazards of different urban areas using classification and regression-tree models, and the map can serve both as a first stage in advancing flood-risk mitigation approaches and in allocating warning and forecasting systems. The findings show that machine-learning methods are efficient in urban flood zoning. Using the city Rasht in Iran, it is shown that distance to rivers, urban drainage density, and distance to vulnerable areas are the most significant parameters that influence flood hazards. Similarly, for urban flood vulnerability, population density, land use, dwelling quality, household income, distance to cultural heritage, and distance to medical centers and hospitals are the most important factors. The integrated technique for both objective and semi-subjective data as outlined in the present study shows credible results that can be obtained without complicated modeling and costly field surveys. The proposed method is especially helpful in areas with little data to describe and display flood hazards to managers and decision makers.

Urban flood risk mapping is an important tool for the mitigation of flooding in view of human activities and climate change. Many developing countries, however, lack sufficiently detailed data to produce reliable risk maps with existing methods. Thus, improved methods are needed that can improve urban flood risk management in regions with scarce hydrological data. Given this, we estimated the flood risk map for Rasht City (Iran), applying a composition of decision-making and machine learning methods. Flood hazard maps were produced applying six state-of-the-art machine learning methods such as classification and regression trees (CART), random forest (RF), boosted regression trees (BRT), multivariate adaptive regression splines (MARS), multivariate discriminant analysis (MDA), and support vector machine (SVM). Flood conditioning parameters applied in modeling were elevation, slope angle, aspect, rainfall, distance to river (DTR), distance to streets (DTS), soil hydrological group (SHG), curve number (CN), distance to urban drainage (DTUD), urban drainage density (UDD), and land use. In total, 93 flood location points were collected from the regional water company of Gilan province combined with field surveys. We used the Analytic Hierarchy Process (AHP) decision-making tool for creating an urban flood vulnerability map, which is according to population density (PD), dwelling quality (DQ), household income (HI), distance to cultural heritage (DTCH), distance to medical centers and hospitals (DTMCH), and land use. Then, the urban flood risk map was derived according to flood vulnerability and flood hazard maps. Evaluation of models was performed using receiver-operator characteristic curve (ROC), accuracy, probability of detection (POD), false alarm ratio (FAR), and precision. The findings showed that the CART method is most accurate method (AUC = 0.947, accuracy = 0.892, POD = 0.867, FAR = 0.071, and precision = 0.929). The results also demonstrated that DTR, UDD, and DTUD played important roles in flood hazard modeling; whereas, the population density was the most significant parameter in vulnerability mapping. These findings indicated that machine learning methods can improve urban flood risk management significantly in regions with limited hydrological data.

Real-time data plays a crucial role in predicting short-duration rainfall. These predictions have a significant impact on our daily life, especially in emergencies that can imperil human safety and the environment. A tragic example of this occurred on November 26, 2022, in Casamicciola, island of Ischia, in the Gulf of Naples (Italy), when heavy rainfall triggered landslides, resulting in loss of lives and extensive damage to buildings and infrastructure. Such destructive consequences highlight the urgent need for accurate short-term rainfall forecasts.In fact, it is very important to have reliable short-term rainfall forecasting for early warning purposes. This can help save lives and property by preventing the effects of flooding, landslides, and other hazards caused by heavy rainfall . It is hard to understand and model how rainfall changes over time, and therefore, predicting rainfall in the short term is a complex challenge. Many of the current models use complicated statistical methods that are often too expensive and time consuming. In contrast, machine-learning (ML) models can find hidden patterns in rainfall data and predict the hourly or sub hourly amount of rain with limited computational burden.In this study, a novel ML model is developed for nowcasting rainfall, to explore how it can make effective and quick short-term forecasts of precipitation. Specifically, the random forest (RF) algorithm is used, as recent studies have found this ML approach to be suitable (Mdegela et al., 2023). The study area is located on the island of Ischia, where 4 rain gauges (Ischia, Monte Epomeo, Forio, Piano Liguori) with a temporal resolution of 10 minutes are installed. The proposed model uses both the precipitation time series of the rain gauges and surface rainfall intensity provided by radar managed by the Civil Protection agency with temporal resolution of 5 minutes and a spatial resolution of 1 km, to predict the future precipitation. Different grid sizes (20x20, 30x30, 40x40 and 50x50 km) centred on the island of Ischia are considered to select the best radar input data (features) for the RF algorithm. The datasets were randomly selected for RF model training (70% of the data) and validation (30% of the data). The Minimum Inter-arrival Time (MIT) criterion was adapted for the definition of rainfall events within the rain gauge precipitation records (Heneker et al., 2001). A rainfall event is defined as a rainfall period preceded and followed by dry periods longer than MIT.The results indicate that the RF model provides reliable short-term precipitation forecasts using only observed values as input, making it a fast, simple, and convenient method for nowcasting. The resulting precipitation forecast has the potential to be used in an early warning system to mitigate the impact of landslides. ReferencesHeneker, T.M., Lambert, M.F., Kuczera, G., 2001. A point rainfall model for risk-based design. J. Hydrol. 247, 54–71.Mdegela, L., Municio, E., De Bock, Y., Luhanga, E., Leo, J. and Mannens, E., 2023. Extreme Rainfall Event Classification Using Machine Learning for Kikuletwa River Floods. Water, 15(6), p.1021.

Rainfall nowcasting, the short-term prediction of precipitation, is a vital component of early warning systems aimed at mitigating the effects of extreme weather events. In this study, we develop a deep learning approach based on convolutional neural networks (CNNs) for rainfall nowcasting and train it exclusively on data from rain gauge stations. Specifically, we adapt the original U-Net architecture and tailor it for regression tasks to forecast short-term precipitation at the Forio rain gauge station on the Island of Ischia, Italy. Two model input configurations are examined to assess the potential value of incorporating data from multiple sources: (a) using data solely from the Forio station, and (b) integrating data from multiple rain gauge stations across the island. The CNN-based nowcasting performance is evaluated across various lead times, ranging from 10 min to 3 hr. The results show that both models achieve high predictive accuracy ( $$ across most lead times), with the single-station model outperforming the multi-station configuration, indicating that adding data from other stations does not necessarily improve forecasting performance. These findings contribute to the advancement of rainfall nowcasting to inform real-time early warning systems.