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(a) Location of the Pampanga River in the Philippines; (b) Locations of the Poyang and Dongting Lakes in China.
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Satellite remote sensing provides significant information for the monitoring of natural disasters. Recently, on a global scale, floods have been increasing both in frequency and in magnitude. In order to map the inundation area, flooding events are investigated using unique RGB composite imagery based on the MODIS surface reflectance (MOD09GA) data...
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Citations
... First, one can rely on on-site flood observations [11]. However, on-site observations may not always be feasible, prompting the need for an alternative approach: assessing floods using remote satellite sensing data [12,13]. Nevertheless, this method is limited to momentary actions during a flood event. ...
Floods are considered the most serious disaster among all-natural catastrophes because they occur more frequently than any other natural hazard and strongly impact more individuals than all other natural disasters. Therefore, it is necessary to study flood risk. Hillah River is critical in securing industrial, agricultural, and civil water in three Iraqi governorates: Babil, Diwaniyah, and Muthanna. Its primary source is the Euphrates River, which extends approximately 100 kilometers within Babil Governorate – the study area. This research aims to evaluate and study the river's capacity and predict future floods by developing scenarios for anticipated events. Also, a hydraulic model was developed to assess the manning coefficient for Shatt Al-Hillah. The one-dimensional HEC-RAS 6.0.1 program has been used to simulate water flow in the river, incorporating over 350 cross-sections spaced at 250-meter intervals surveyed in 2018 by the Department of Water Resources in Babil Governorate. The model was calibrated using observed discharge data from 2004 to 2022 in Shatt al-Hillah. Subsequently, it was compared with a range of water levels by varying manning factors. The calibration results indicated that a roughness coefficient of 0.023 was suitable for unstable flow conditions, and the least mean square root error between the measured and simulated water levels was 0.053. The simulation results showed that the current capacity of Al-Hilla River was 205 m3/s, such that it cannot pass the design discharge of 303 m3/s. After conducting scenarios greater than 205 m3/s, the results showed that increasing the discharge increased the areas exposed to flooding so that when the discharge was 450 m3/s, flooding of the submerged areas increased. With a percentage of 92.2%, the northern side of Babil Governorate will be more vulnerable to flooding than the southern side because the southern part levels are lower than the northern part.
... The recent increase in the number of natural disasters has become a global issue, because of the damages to the hydrological and ecological environment and human-made infrastructure, and the threats to human lives. Satellite remote sensing techniques provide valuable support for monitoring these disasters and for post-event crisis management [7]. Floods are the most frequent and second-most costliest natural disasters worldwide, making up a share of 43% of the recorded disaster events in 1998-2017 and affecting over 2 billion people, according to the survey of the United Nations Office for Disaster Risk Reduction (UNISDR) ([8,9]). ...
The increasing rate of adoption of innovative technological achievements along with the penetration of the Next Generation Internet (NGI) technologies and Artificial Intelligence (AI) in the water sector, are leading to a shift to a Water-Smart Society. New challenges have emerged in terms of data interoperability, sharing, and trustworthiness due to the rapidly increasing volume of heterogeneous data generated by multiple technologies. Hence, there is a need for efficient harmonisation and smart modeling of the data to foster advanced AI analytical processes which will lead to efficient water data management. The main objective of this work is to propose two Smart Data Models focusing on the modeling of the Satellite Imaginary data and the Flood Risk Assessment processes. The utilisation of those models reinforces the fusion and homogenisation of diverse information and data facilitating the adoption of AI technologies for flood mapping and monitoring. Furthermore, a holistic framework has been developed and evaluated via qualitative and quantitative performance indicators revealing the efficacy of the proposed models concerning the usage of the models in real cases. The framework is based on the well-known and compatible technologies on NGSI-LD standards which are customised and applicable easily to support the water data management processes effectively.
... General damage estimations of tropical storms typically employ moderate resolution (Al-Amin Hoque et al. 2015Hoque et al. , 2016Hoque et al. , 2017Phiri et al. 2020) or high-resolution imagery (Barnes et al. 2007;Doshi et al. 2018;Mas et al. 2015). Flood mapping and impact assessments on a large scale mainly make use of publicly available low resolution optical sensors, most commonly the Moderate Resolution Imaging Spectroradiometer (MODIS) (e.g., Arvind et al. 2016;Ban et al. 2017;Coltin et al. 2016;Lin et al. 2017Lin et al. , 2019Memon et al. 2015). The benefits of MODIS are high spatial coverage and temporal resolution along with free availability. ...
Ground-based disaster damage assessments typically take the form of a team of experts being sent to the affected areas to conduct a survey. This approach is time-consuming, difficult, and costly. An alternative to this is an assessment based on satellite data, which can provide faster, cheaper, and possibly accurate insights into disaster’s specific impacts. An even timelier option of disaster ‘nowcasting’ is supposed to inform on impacts during or very shortly after the event. Typically, this has been done using risk models, but these usually do not account for compounding and cascading effects. We propose a novel nowcasting approach for tropical cyclones employing pre-existing socio-economic and demographic data and calibrated with satellite data. The method could be used to assess cyclone impacts based only on its known trajectory, and even before post-event satellite imagery is available. We investigate the feasibility of this approach focusing on Fiji and its agricultural sector. We link remote sensing data with available household surveys and the agricultural census data to identify potential correlates of vegetation damage from cyclones. If robust enough, these correlates could later be used for nowcasting cyclone impacts. We show that remote sensing data, when combined with pre-event socio-economic and demographic data, can be used for both nowcasting and post-disaster damage assessments.
... There are a variety of approaches that may be used to get a flood inventory map. The primary techniques for obtaining a decent and accurate map are inundated with historical documents (Jha and Bairagya, 2013;Rudra, 2018;Agugliaro and Witkowski, 2022) and the interpretation of aerial imagery (Ban et al., 2017;Tanim et al., 2022). It was identified that there were various historical flood experiences (Jha and Bairagya, 2013;Rudra, 2018;Corella et al., 2021) in the research region (Fig. 2), whose information was derived from historical documents, field surveys, and Google Earth Pro, among other sources. ...
The unique characteristics of drainage conditions in the Pagla river basin cause flooding and harm the socioeconomic environment. The main purpose of this study is to investigate the comparative utility of six machine learning algorithms to improve flood susceptibility and ensemble techniques' capability to elucidate the underlying patterns of floods and make a more accurate prediction of flood susceptibilities in the Pagla river basin. In the present scenario, the frequency of flood conditions in this study area becomes high with heavy and sudden rainfall, so it is essential to study flood mitigation and measure. At first, a spatial flood database was built with 200 flood locations and sixteen flood influencing factors, and its process with the help of the Geographic Information System (GIS) environment and build up different models applying the machine learning techniques. It has found different flood susceptibility zone using machine learning-based Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), Reduced Error Pruning Tree (REPTree), Logistic Regression (LR), and Bagging helping GIS environment and the model validation using the Receiver Operating Characteristic Curve (ROC). Afterward, ensemble all the models to gate comparative accuracy of the flood zone. The calculated areas are under the very high flood susceptibility zone 8.69%, 14.92%, 14.17%, 12.98%, 14.65%, 13.24% and 13.41% for ANN, SVM, RF, REPTree, LR and Bagging, respectively. Finally, ROC curve, the Standard Error (SE), and the Confidence Interval (CI) at 95 per cent were used to assess and compare the performance of the models. The obtained results indicate that all models are highly accepted Area Under Curve (AUC) of ROC between 0.889 (LR) to 0.926 (Ensemble). From the estimation of the accuracy of the applied methods using ROC, it is found that the Ensemble model has the higher capability compared to the other applied models in projecting flood susceptibility in the study area. It has the highest area under the ROC curve the AUC values are 0.918 and 0.926, the SE (0.023, 034), and the narrowest CI (95 per cent) (0.873–0.962, 0.859–0.993) whereas highest area under Bagging (the ROC) curve (AUC) value (0.914, 0.919), for both the training and validation datasets. After ensembling, the result shows that the result is a highly flood susceptible area located at the lower part of the study area. In this area, the very high flood susceptibility zone values lie between 4.46 and 6.00 in the ensemble result. The areas comprise the low height and belong to Murarai I, Murarai II, Suti I and Suti II C.D. block of West Bengal. The current study will help the policymakers and the researcher determine the flood conditioning problems for prospects.
... For example, the RGB composite technique can be used for this purpose. Indeed, in the RGB colour space, a colour can be expressed by a combination of the primary components red, green and blue and visualized in an n-bit display system [58]. In this way, assigning the red, green and blue channels respectively to acquisitions referring to different times or states, a single composite image is produced, where the final colour of each pixel is representative of changes occurred. ...
Moisture is one of the main problems that affect new and historic masonry buildings, which are the most common ones in Europe and worldwide. The application of surface treatments based on water-repellents is a very common solution to protect masonry from rain and hence from moisture-related problems. However, there are very few studies on the monitoring of water-repellents in real buildings and a deeper knowledge would be necessary on the long-term effectiveness and compatibility of water repellents, especially considering that defects and flaking issues are often reported in-the-field. In this paper, infrared thermography was proposed as a totally non-invasive technique to monitor the behaviour of brick masonry subjected to wetting, both from outside (rain) and inside (internal moisture). An active thermal approach was used to simulate internal and external wetting. The behaviour of the masonry during wetting and drying was investigated both in laboratory walls and in brick samples, to elucidate their water transport properties. All the materials were tested both in untreated conditions and after the application of two different hydrophobic coatings. The results show that the drying behaviour of treated masonry materials is a critical issue, as the coatings may strongly slow down the drying of internal moisture, even if the coefficient of resistance to water vapour diffusion of the products is very low. The results also suggest that the methodology used to process thermal images using multi-temporal analysis is a promising way to interpret the water transport in treated walls and to monitor real buildings where water-repellents were applied.
... Airborne and spaceborne Earth observation platforms have revolutionized the monitoring of inundation dynamics immediately following flood events (Dewan et al., 2006;Nateghi et al., 2016;Pradhan et al., 2016). Rapid progress has been made in flood detection algorithms that use optical images to assess flood events (Ban et al., 2017). Many early studies were based on relatively low spatial resolution imagery (≥250 m), such as images from the Advanced Very High-Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectrometer (MODIS; Ali et al., 1989;Brakenridge & Anderson, 2006;Sanyal & Lu, 2004). ...
... It is common today to use optical imagery with a spatial resolution of 30 m or finer (e.g., Landsat and Sentinel 2 missions) for flood monitoring (Caballero et al., 2019). However, long revisit time of fine spatial resolution satellite observations and persistent cloud cover during extreme weather events continue to limit the use of optical imagery for rapid response flood mapping (Ban et al., 2017;Bioresita et al., 2018). ...
Extreme precipitation events are intensifying due to a warming climate, which, in some cases, is leading to increases in flooding. Detection of flood extent is essential for flood disaster response, management, and prevention. However, it is challenging to delineate inundated areas through most publicly available optical and short‐wavelength radar data, as neither can “see” through dense forest canopies. In 2018, Hurricane Florence produced heavy rainfall and subsequent record‐setting riverine flooding in North Carolina, USA. NASA/JPL collected daily high‐resolution full‐polarized L‐band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data between September 18th and 23rd. Here, we use UAVSAR data to construct a flood inundation detection framework through a combination of polarimetric decomposition methods and a Random Forest classifier. Validation of the established models with compiled ground references shows that the incorporation of linear polarizations with polarimetric decomposition and terrain variables significantly enhances the accuracy of inundation classification, and the Kappa statistic increases to 91.4% from 64.3% with linear polarizations alone. We show that floods receded faster near the upper reaches of the Neuse, Cape Fear, and Lumbee Rivers. Meanwhile, along the flat terrain close to the lower reaches of the Cape Fear River, the flood wave traveled downstream during the observation period, resulting in the flood extent expanding 16.1% during the observation period. In addition to revealing flood inundation changes spatially, flood maps such as those produced here have great potential for assessing flood damages, supporting disaster relief, and assisting hydrodynamic modeling to achieve flood‐resilience goals.
... Reliable monitoring of the flood situation, mapping of inundated areas, estimating the residential, land, and crops damages are essential to ameliorate destructive consequences of floods (Amarnath et al., 2016;Chowdhury et al., 2017;Zoka et al., 2018). However, Remote Sensing (RS) has been frequently used in monitoring and mapping the flood (Amarnath et al., 2016;Ban et al., 2017;Dewan et al., 2007;Li et al., 2012;Nandi et al., 2017;Proud et al., 2011;Rahman et al., 2017Rahman et al., , 2019Shen et al., 2019;Sun et al., 2012;Sanyal et al., 2004). On the other hand, satellite images, as one of the most important sources of spatial information in comparison with other common sources (such as maps and areal images), have significant advantages such as vast coverage, less in situ measurements, lower computational costs, and less time-consuming processes. ...
... In general, RS technologies, applied in hydrology studies, are divided into two groups: (i) active (Radar) and (ii) passive (Optical) sensors. Optical RS sensors utilize medium-to-high spatial resolution to provide reliable data with a high temporal resolution, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, Landsat series, and Sentinel-2 satellite (Amarnath et al., 2016;Ban et al., 2017;Feyisa et al., 2014;Nandi et al., 2017;Rahman et al., 2017Rahman et al., , 2019Li et al., 2021). Optical RS was considered an important source of reliable information employed in the analysis of natural resources, particularly surface water (Feyisa et al., 2014). ...
Traditional methodologies of flood monitoring are generally time-consuming and demanding tasks. In most cases, there is no possibility of flood monitoring in large areas. Due to the major drawbacks of conventional methods in flood detection of large districts, Remote Sensing (RS) has been efficiently employed as the best solution owing to its being synoptic view and cost-effective methodologies. One of the most challenging issues in RS technologies is choosing the optimal spectral bands to detect changes in the natural environment. In this research, Elimination and Choice Expressing Reality (ELECTRE), as one of the most widely used Multi-Criteria Decision Making (MCDM) techniques, was applied to select the optimal bands of Sentinel-2 satellite images for detection of flood-affected areas. For this purpose, the decision-making method was implemented during ten options and six criteria. The properties of the Sentinel-2 satellite images consisted of ten bands (with 10 and 20m spatial resolutions) and the criteria are the signal to noise ratio (SNR) related to sensor, standard deviation, variance, the SNR related to the bands, spatial resolution, and wavelength. Afterward, the ELECTRE technique was used to select six optimal bands among ten bands. The ELECTRE algorithm was programmed in MATLAB programming language that could make decisions with multiple options and multiple criteria. Furthermore, the Support Vector Machine (SVM) classification method, as one of the most powerful Machine Learning (ML) models, has been applied to classify the water bodies related to before and after the flood. According to the results of optimal bands classification, Overall Accuracy (OA) and Kappa Coefficient (KC) for the pre-flood classification were 93.65 percent and 0.923, respectively, and for the post-flood classification, the OA and KC values were 94.52 percent and 0.935 respectively. In the case of before and after flooding, the results of classification model for optimal bands had more accuracy levels in comparison with those obtained by original bands. Generally, it was found that the ELECTRE technique for selecting the best bands of Sentinel-2 satellite images and detection of flood-affected areas, in a short period of time with high accuracy, offers remarkable and consistent results.
... Freely available remote sensing products such as optical, radar, and hyperspectral datasets are more popular in studies quantifying different aspects of natural hazards (Lin and Yan, 2016;Yao et al., 2019). These remote sensing datasets are used to monitoring of current flood events (Ban et al., 2017) and to compute different set of variables that are entered as input in flood prediction models (Arora et al., 2021a). ...
This study has developed a new ensemble model and tested another ensemble model for flood susceptibility mapping in the Middle Ganga Plain (MGP). The results of these two models have been quantitatively compared for performance analysis in zoning flood susceptible areas of low altitudinal range, humid subtropical fluvial floodplain environment of the Middle Ganga Plain (MGP). This part of the MGP, which is in the central Ganga River Basin (GRB), is experiencing worse floods in the changing climatic scenario causing an increased level of loss of life and property. The MGP experiencing monsoonal subtropical humid climate, active tectonics induced ground subsidence, increasing population, and shifting landuse/landcover trends and pattern, is the best natural laboratory to test all the susceptibility prediction genre of models to achieve the choice of best performing model with the constant number of input parameters for this type of topoclimatic environmental setting. This will help in achieving the goal of model universality, i.e., finding out the best performing susceptibility prediction model for this type of topoclimatic setting with the similar number and type of input variables. Based on the highly accurate flood inventory and using 12 flood predictors (FPs) (selected using field experience of the study area and literature survey), two machine learning (ML) ensemble models developed by bagging frequency ratio (FR) and evidential belief function (EBF) with classification and regression tree (CART), CART-FR and CART-EBF, were applied for flood susceptibility zonation mapping. Flood and non-flood points randomly generated using flood inventory have been apportioned in 70:30 ratio for training and validation of the ensembles. Based on the evaluation performance using threshold-independent evaluation statistic, area under receiver operating characteristic (AUROC) curve, 14 threshold-dependent evaluation metrices, and seed cell area index (SCAI) meant for assessing different aspects of ensembles, the study suggests that CART-EBF (AUCSR = 0.843; AUCPR = 0.819) was a better performant than CART-FR (AUCSR = 0.828; AUCPR = 0.802). The variability in performances of these novel-advanced ensembles and their comparison with results of other published models espouse the need of testing these as well as other genres of susceptibility models in other topoclimatic environments also. Results of this study are important for natural hazard managers and can be used to compute the damages through risk analysis.
... Areas devoid of water bodies appear brighter owing to high reflectance (Haibo et al. 2011;Mondejar and Tongco 2019;Oliveira, Kampel, and Amaral 2008). NIR imaging has been widely used for various applications in the marine environment and is useful for water body location and delineation (Mondejar and Tongco 2019), geomorphological observations (Oliveira, Kampel, and Amaral 2008), marine sediment determination (Chang et al. 2005), shoreline (van der Werff 2019) and coastal wetland biomass mapping (Doughty and Cavanaugh 2019), and information provision for flooded areas (Ban et al. 2017). ...
Marine debris significantly influences the environment and economics of marine ecosystems. Measures to ban illegally discarded materials using monitoring techniques are expected to mitigate marine debris mismanagement. For example, derelict oyster farming rafts along the southwestern coast of Taiwan have been a source of marine debris pollution since the 1980s. An efficient inspection system is an urgent requirement for monitoring oyster farming areas and, is expected to implement control and surveillance measures for litter discarded from coastal fisheries. To address this issue, this study examined combinations of radio frequency identification (RFID), drone archiving, and onshore receiving systems to ascertain the positions of oyster rafts and their owners with digitally tagged labels. The results showed that the two proposed monitoring systems are feasible for use in marine environments. The RFID signals archived by the drone reached 100% of the 200 tags with a spatial bias ranging from 2.38 to 59.99 m. RFID-GPS hybrid tag signals received by the onshore station covered 100% of the 20 tags with spatial bias ranging from 1.27 to 10.47 m. The RFID-GPS hybrid system was confirmed as a feasible approach for monitoring oyster rafts within 3 km of the coast. The real-time (1 h intervals) position and attributes of each raft detected by the system indicated that our designed techniques enhance responsible fishery surveillance and management of coastal aquaculture worldwide.
... Freely available remote sensing products such as optical, radar, and hyperspectral datasets are more popular in studies quantifying different aspects of natural hazards (Lin and Yan, 2016;Yao et al., 2019). These remote sensing datasets are used to monitoring of current flood events (Ban et al., 2017) and to compute different set of variables that are entered as input in flood prediction models (Arora et al., 2021a). ...
This study has developed a new ensemble model and tested another ensemble model for flood susceptibility mapping in the Middle Ganga Plain (MGP). The results of these two models have been quantitatively compared for performance analysis in zoning flood susceptible areas of low altitudinal range, humid subtropical fluvial floodplain environment of the Middle Ganga Plain (MGP). This part of the MGP, which is in the central Ganga River Basin (GRB), is experiencing worse floods in the changing climatic scenario causing an increased level of loss of life and property. The MGP experiencing monsoonal subtropical humid climate, active tectonics induced ground subsidence, increasing population, and shifting landuse/landcover trends and pattern, is the best natural laboratory to test all the susceptibility prediction genre of models to achieve the choice of best performing model with the constant number of input parameters for this type of topoclimatic environmental setting. This will help in achieving the goal of model universality, i.e., finding out the best performing susceptibility prediction model for this type of topoclimatic setting with the similar number and type of input variables. Based on the highly accurate flood inventory and using 12 flood predictors (FPs) (selected using field experience of the study area and literature survey), two machine learning (ML) ensemble models developed by bagging frequency ratio (FR) and evidential belief function (EBF) with classification and regression tree (CART), CART-FR and CART-EBF, were applied for flood susceptibility zonation mapping. Flood and non-flood points randomly generated using flood inventory have been apportioned in 70:30 ratio for training and validation of the ensembles. Based on the evaluation performance using threshold-independent evaluation statistic, area under receiver operating characteristic (AUROC) curve, 14 threshold-dependent evaluation metrices, and seed cell area index (SCAI) meant for assessing different aspects of ensembles, the study suggests that CART-EBF (AUCSR = 0.843; AUCPR = 0.819) was a better performant than CART-FR (AUCSR = 0.828; AUCPR = 0.802). The variability in performances of these novel-advanced ensembles and their comparison with results of other published models espouse the need of testing these as well as other genres of susceptibility models in other topoclimatic environments also. Results of this study are important for natural hazard managers and can be used to compute the damages through risk analysis.
