Remote Sensing Methods for Flood Prediction: A Review

Flood analysis comparison with probability density functions and a stochastic weather generator

Flood prediction has become essential to hydrology and natural disaster management due to the increasing frequency and severity of extreme hydrological events driven by climate change. This study compares two methodologies for predicting flood events in Morelia, Mexico: theoretical distribution functions and stochastic weather generators. The methodology integrates maximum runoff results for different return periods into a drainage network hydraulic model, using the Soil Conservation Service Curve Number (SCS-CN) method and a multivariate stochastic model (MASVC). Hydrodynamic modeling with HEC-RAS, incorporating two-dimensional shallow water equations, was used to simulate flood inundation areas. The study reveals that while both modeling approaches similarly replicate the system's behavior, they produce different water levels due to variations in maximum flow values. The stochastic model tends to generate higher maximum water levels. High-resolution digital elevation models (DEMs) with a pixel size of five m in urban areas and 0.5 m in drainage network zones, and land use data were crucial in improving the accuracy of the hydraulic simulations. Findings indicate that unregulated urban growth in flood-prone areas significantly exacerbates the impact of flooding. The generated hazard maps and flood simulations provide valuable tools for urban planning and decision-making, highlighting the need for strategic interventions to mitigate flood risks. This research underscores the importance of integrating advanced modeling techniques in flood risk management to enhance the precision and reliability of flood predictions.

European Union crop map 2022: Earth observation's 10-meter dive into Europe's crop tapestry

To provide the information needed for a detailed monitoring of crop types across the European Union (EU), we present an advanced 10-metre resolution map for the EU and Ukraine with 19 crop types for 2022, updating the 2018 version. Using Earth Observation (EO) and in-situ data from Eurostat's Land Use and Coverage Area Frame Survey (LUCAS) 2022, the methodology included 134,684 LUCAS Copernicus polygons, Sentinel-1 and Sentinel-2 satellite imagery, land surface temperature and a digital elevation model. Based on this data, two classification layers were developed using a Random Forest machine learning approach: a primary map and a gap-filling map to address cloud-covered gaps. The combined maps, covering 27 EU countries, show an overall accuracy of 79.3% for seven major land cover classes and 70.6% for all 19 crop types. The trained model was used to derive the 2022 map for Ukraine, demonstrating its robustness even in regions without labelled samples for model training.

Mapping flood susceptibility with PROMETHEE multi-criteria analysis method

On a global scale, flooding is the most devastating natural hazard with an increasingly negative impact on humans. It is necessary to accurately detect flood-prone areas. This research introduces and evaluates the Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) integrated with GIS in the field of flood susceptibility in comparison with two conventional multi-criteria decision analysis (MCDA) methods: analytical hierarchy process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The Spercheios river basin in Greece, which is a highly susceptible area, was selected as a case study. The application of these approaches and the completion of the study requires the creation of a geospatial database consisting of eight flood conditioning factors (elevation, slope, NDVI, TWI, geology, LULC, distance to river network, rainfall) and a flood inventory of flood (564 sites) and non-flood locations for validation. The weighting of the factors is based on the AHP method. The output values were imported into GIS and interpolated to map the flood susceptibility zones. The models were evaluated by area under the curve (AUC) and the statistical metrics of accuracy, root mean squared error (RMSE), and frequency ratio (FR). The PROMETHEE model is proven to be the most efficient with AUC = 97.21%. Statistical metrics confirm the superiority of PROMETHEE with 87.54% accuracy and 0.12 RMSE. The output maps revealed that the regions most prone to flooding are arable land in lowland areas with low gradients and quaternary formations. Very high susceptible zone covers approximately 15.00-19.50% of the total area and have the greatest FR values. The susceptibility maps need to be considered in the preparation of a flood risk management plan and utilized as a tool to mitigate the adverse impacts of floods.

Combining multisensor images and social network data to assess the area flooded by a hurricane event

In this study, multisensor remote sensing datasets were used to characterize the land use and land covers (LULC) flooded by Hurricane Willa which made landfall on October 24, 2018. The landscape characterization was done using an unsupervised K-means algorithm of a cloud-free Sentinel-2 MultiSpectral Instrument (MSI) image, acquired during the dry season before Hurricane Willa. A flood map was derived using the histogram thresholding technique over a Synthetic Aperture Radar (SAR) Sentinel-1 C-band and combined with a flood map derived from a Sentinel-2 MSI image. Both, the Sentinel-1 and Sentinel-2 images were obtained after Willa landfall. While the LULC map reached an accuracy of 92%, validated using data collected during field surveys, the flood map achieved 90% overall accuracy, validated using locations extracted from social network data, that were manually georeferenced. The agriculture class was the dominant land use (about 2,624 km2), followed by deciduous forest (1,591 km2) and sub-perennial forest (1,317 km2). About 1,608 km2 represents the permanent wetlands (mangrove, salt marsh, lagoon and estuaries, and littoral classes), but only 489 km2 of this area belongs to aquatic surfaces (lagoons and estuaries). The flooded area was 1,225 km2, with the agricultural class as the most impacted (735 km2). Our analysis detected the saltmarsh class occupied 541 km2in the LULC map, and around 328 km2 were flooded during Hurricane Willa. Since the water flow receded relatively quickly, obtaining representative imagery to assess the flood event was a challenge. Still, the high overall accuracies obtained in this study allow us to assume that the outputs are reliable and can be used in the implementation of effective strategies for the protection, restoration, and management of wetlands. In addition, they will improve the capacity of local governments and residents of Marismas Nacionales to make informed decisions for the protection of vulnerable areas to the different threats derived from climate change.

Inventing a robust road-vehicle flood level monitoring device for disaster mitigation

Flooding impedes road utility and the frequency has increased across countries of the world owing to global climate change phenomena. Global road flooding casualties have risen from 371,800 in 2015 to 842,000 in 2017 resulting to economic losses valued at approximately US$71 billion. Existing devices that offer warning signals on safe threshold during flooding are predictive in nature and based on complex technologies that are cumbersome and rather expensive thereby affecting the attractiveness to low-economy societies of developing countries. There is therefore a dare need for better inventions towards greater mitigation. This paper presents an adaptive, affordable, robust, efficient and effective road vehicle flood level monitoring device for detecting rising flood on roads above a user defined safe threshold to mitigate road flooding disasters. The device operates on the principle of level conductivity sensor. The developed device offers interoperability with Google map enabling the level of flood on road to be accessed by road users online when fully commercialized. By this, road users are aware of the dangerous levels of flood to enable them use alternative routes. The study therefore recommends for adoption of mandatory inclusion of this invention on roads towards averting the usual road flooding hazard in Africa.

Monitoring the effects of urbanization and flood hazards on sandy ecosystem services

Urbanization, storms, and floods have compromised the benefits derived from various types of sand dune landscapes, particularly in developing countries located in humid monsoon tropical regions. One pertinent question is which driving forces have had a dominant impact on the contributions of sand dune ecosystems to human well-being. Has the decline in sand dune ecosystem services (ES) been primarily due to urbanization or flooding hazards? This study aims to address these issues by developing a Bayesian Belief Network (BBN) to analyze six different sand dune landscapes worldwide. The study uses various data types, including multi-temporal and -sensor remote sensing (SAR and optical data), expert knowledge, statistics, and GIS to analyze the trends in sand dune ecosystems. A support tool based on probabilistic approaches was developed to assess changes in ES over time due to the effects of urbanization and flooding. The developed BBN has the potential to assess the ES values of sand dunes during both rainy and dry seasons. The study calculated and tested the ES values in detail over six years (from 2016 to 2021) in Quang Nam province, Vietnam. The results showed that urbanization has led to an increase in the total ES values since 2016, while floods only had a minimal impact on dune ES values during the rainy season. The fluctuations of ES values were found to be more significant due to urbanization than floods. The study's approach can be useful in future research on coastal ecosystems.

A Multi-Modal Wireless Sensor System for River Monitoring: A Case for Kikuletwa River Floods in Tanzania

Reliable and accurate flood prediction in poorly gauged basins is challenging due to data scarcity, especially in developing countries where many rivers remain insufficiently monitored. This hinders the design and development of advanced flood prediction models and early warning systems. This paper introduces a multi-modal, sensor-based, near-real-time river monitoring system that produces a multi-feature data set for the Kikuletwa River in Northern Tanzania, an area frequently affected by floods. The system improves upon existing literature by collecting six parameters relevant to weather and river flood detection: current hour rainfall (mm), previous hour rainfall (mm/h), previous day rainfall (mm/day), river level (cm), wind speed (km/h), and wind direction. These data complement the existing local weather station functionalities and can be used for river monitoring and extreme weather prediction. Tanzanian river basins currently lack reliable mechanisms for accurately establishing river thresholds for anomaly detection, which is essential for flood prediction models. The proposed monitoring system addresses this issue by gathering information about river depth levels and weather conditions at multiple locations. This broadens the ground truth of river characteristics, ultimately improving the accuracy of flood predictions. We provide details on the monitoring system used to gather the data, as well as report on the methodology and the nature of the data. The discussion then focuses on the relevance of the data set in the context of flood prediction, the most suitable AI/ML-based forecasting approaches, and highlights potential applications beyond flood warning systems.

Impact of a DANA Event on the Thermal Response of Nectarine Trees

This field experiment focuses on the effects of a heavy rainfall event (DANA, depresión aislada en niveles altos) that occurred on 12-14 September 2019 (DOY, Day of the year, 255-257), in southern Spain on plant water status and the thermal response of nectarine trees. Two irrigation treatments were applied during the summer-autumn postharvest period (DOY 158-329): full-irrigated (CTL) and non-irrigated (DRY). Volumetric soil water content (θ_v), air temperature (Ta) and canopy temperature (Tc) were monitored in real-time and the crop water stress index (CWSI) was calculated. The difference in Tc between the DRY and CTL treatments (Tc' - Tc) is proposed as a new thermal indicator. Stem water potential (Ψ_stem) and leaf gas exchange measurements were recorded on representative days. During the DANA event, only the Tc measured by the infrared radiometer sensors could be monitored. Therefore, the effects of the DANA forced the soil water content sensors to be switched off, which prevented Ψ_stem and leaf gas exchange determinations from DOY 255 to 275. Before the DANA event, withholding irrigation caused a gradual decrease in the soil and plant water status in the DRY treatment. Significant differences appeared between treatments in the studied thermal indexes. Moreover, Tc' - Tc was more sensitive than Tc - Ta in assessing nectarine water stress. The effects of the DANA reduced these differences, suggesting different baselines for the calculation of CWSI. In this respect, the relationship Tc - Ta vs. VPD improved the coefficient of determination after the DANA event in full-irrigated trees. Similar values of Ψ_stem and leaf gas exchange were found in both treatments after the DANA event, even though thermal indexes showed some significant differences. In addition, the strong relationship found between Tc - Ta and CWSI vs. Ψ_stem worsened after DANA occurred, revealing a lower sensitivity of Ψ_stem compared to canopy temperature to accurately assess nectarine water status in these saturated soil conditions. This research underlined the robustness of infrared thermography to continuously monitor plant water status under these extreme weather conditions.

Hourly Water Level Forecasting in an Hydroelectric Basin Using Spatial Interpolation and Artificial Intelligence

In this work, a new hydroelectric basin modelling approach is described and applied to the Pontecosi basin, Italy. Several types of data sources were used to learn the model: a number of weather stations, satellite observations, the reanalysis dataset, and basin data. With the goal of predicting the water level of the basin, the model was composed by three cascade modules. Firstly, different spatial interpolation methods, such as Kriging, Radial Basis Function, and Natural Neighbours, were compared and applied to interpolate the weather stations data nearby the basin area to infer the main environmental variables (air temperature, air humidity, precipitation, and wind speed) in the basin area. Then, using these variables as inputs, a neural network was trained to predict the mean soil moisture concentration over the area, also to improve the low availability due to satellite orbits. Finally, a non-linear auto regressive exogenous input (NARX) model was trained to simulate the basin level with different prediction horizons, using the data from the previous modules and past basin data (water level, discharge flow rate, and turbine flow rate). Accurate predictions of the basin water level were achieved within 1 to 6 h ahead, with mean absolute errors (MAE) between 2 cm and 10 cm, respectively.

Crack Detection in Concrete Structures Using Deep Learning

Infrastructure, such as buildings, bridges, pavement, etc., needs to be examined periodically to maintain its reliability and structural health. Visual signs of cracks and depressions indicate stress and wear and tear over time, leading to failure/collapse if these cracks are located at critical locations, such as in load-bearing joints. Manual inspection is carried out by experienced inspectors who require long inspection times and rely on their empirical and subjective knowledge. This lengthy process results in delays that further compromise the infrastructure’s structural integrity. To address this limitation, this study proposes a deep learning (DL)-based autonomous crack detection method using the convolutional neural network (CNN) technique. To improve the CNN classification performance for enhanced pixel segmentation, 40,000 RGB images were processed before training a pretrained VGG16 architecture to create different CNN models. The chosen methods (grayscale, thresholding, and edge detection) have been used in image processing (IP) for crack detection, but not in DL. The study found that the grayscale models (F1 score for 10 epochs: 99.331%, 20 epochs: 99.549%) had a similar performance to the RGB models (F1 score for 10 epochs: 99.432%, 20 epochs: 99.533%), with the performance increasing at a greater rate with more training (grayscale: +2 TP, +11 TN images; RGB: +2 TP, +4 TN images). The thresholding and edge-detection models had reduced performance compared to the RGB models (20-epoch F1 score to RGB: thresholding −0.723%, edge detection −0.402%). This suggests that DL crack detection does not rely on colour. Hence, the model has implications for the automated crack detection of concrete infrastructures and the enhanced reliability of the gathered information.

HERO: Hybrid Effortless Resilient Operation Stations for Flash Flood Early Warning Systems

Floods are the most frequent type of natural disaster. Flash floods are one of the most common types of floods, caused by rapid and excessive rainfall. Normally, when a flash flood occurs, the water of the upstream river increases rapidly and flows to the downstream watersheds. The overflow of water increasingly submerges villages in the drainage basins. Flash flood early warning systems are required to mitigate losses. Water level monitoring stations can be installed at upstream river areas. However, telemetry stations face several challenges because the upstream river areas are far away and lack of public utilities (e.g., electric power and telephone lines). This research proposes hybrid effortless resilient operation stations, named HERO stations, in the flash flood early warning system. The HERO station was designed and developed with a modular design concept to be effortlessly customized and maintained. The HERO station adapts its working operation against the environmental changes to maintain a long working period with high data sensing accuracy. Moreover, the HERO station can switch its communication mode between the centralized and decentralized communication modes to increase availability. The network of the HERO stations has already been deployed in the northern part of Thailand. It results in improvements of the telemetry station’s availability. The HERO stations can adapt to environmental changes. The flash flood early warning messages can be disseminated to the villagers to increase the flood preparation time and to reduce flash flood damage.

Water Quality Carbon Nanotube-Based Sensors Technological Barriers and Late Research Trends: A Bibliometric Analysis

Water is the key element that defines and individualizes our planet. Relative to body weight, water represents 70% or more for the majority of all species on Earth. Taking care of water as a whole is equivalent with taking care of the entire biodiversity or the whole of humanity itself. Water quality is becoming an increasingly important component of terrestrial life, hence intensive work is being conducted to develop sensors for detecting contaminants and assessing water quality and characteristics. Our bibliometric analysis is focused on water quality sensors based on carbon nanotubes and highlights the most important objectives and achievements of researchers in recent years. Due to important measurement characteristics such as sensitivity and selectivity, or low detection limit and linearity, up to the ability to measure water properties, including detection of heavy metal content or the presence of persistent organic compounds, carbon nanotube (CNT) sensors, taking advantage of available nanotechnologies, are becoming increasingly attractive. The conducted bibliometric analysis creates a visual, more efficient keystones mapping. CNT sensors can be integrated into an inexpensive real-time monitoring data acquisition system as an alternative for classical expensive and time-consuming offline water quality monitoring. The conducted bibliometric analysis reveals all connections and maps all the results in this water quality CNT sensors research field and gives a perspective on the approached methods on this specific type of sensor. Finally, challenges related to integration of other trends that have been used and proven to be valuable in the field of other sensor types and capable to contribute to the development (and outlook) for future new configurations that will undoubtedly emerge are presented.