This paper presents a new data-driven modelling framework for forecasting probabilistic flood inundation maps for real-time applications. The proposed end-to-end (rainfall–inundation) method combines a suite of machine learning (ML) algorithms to forecast discharge and deliver probabilistic flood inundation maps with a 3 h lead time. To classify wet/dry cells, the method applies rainfall–discharge models based on random forest technique on top of classifiers based on multi-layer perceptron. The hybrid modelling framework was tested using two subsets of data created from an observed fluvial flood event in a small flood-prone town in the UK. The results showed that the model can effectively emulate the outcomes of a hydrodynamic model (Flood Modeller (FM)) with considerably high accuracy measured in terms of flood arrival time error and classification accuracy. The mean arrival time difference between the proposed model and the hydrodynamic model was 1 h 53 min. The classification accuracy was measured against a synthetic aperture radar image, producing accuracies of 88.22% and 86.58% for the proposed data-driven model and FM, respectively. The key features of the proposed modelling framework are that it is simple to implement, detects flooded cells effectively and substantially reduces computational time.

This paper presents a systematic investigation into modelling capacities of three conventional data-driven modelling techniques, namely, wavelet-based artificial neural network (WANN), support vector regression (SVR) and deep belief network (DBN) for multi-step ahead stream flow forecasting. To evaluate the effectiveness of these modelling techniques, hydro-meteorological hourly datasets from three case-study rivers located in the UK have been used. A heuristic performance analysis of the modelling schemes has been conducted by systematically analysing the key statistics that measure magnitude, scatter and density of model errors. Finally, for each of the modelling techniques, the performance deterioration rate in time was estimated. The results show that the SVR model can forecast quite accurately up to one to two hours ahead but its performance deteriorates gradually from three hours onwards. Further it has been found that the WANN model performs better when the overall non-linearity of the system increases, whereas the DBN model appeared to show consistently poor predictive capabilities when compared to the other models presented herein. The authors conclude by stating that, for any selected model, it is possible to use an identical model structure for up to two steps ahead forecasting. Models need to be re-configured beyond that limit.

The scarcity of high-resolution urban digital elevation model (DEM) datasets, particularly in certain developing countries, has posed a challenge for many water-related applications such as flood risk management. A solution to address this is to develop effective approaches to reconstruct high-resolution DEMs from their low-resolution equivalents that are more widely available. However, the current high-resolution DEM reconstruction approaches mainly focus on natural topography. Few attempts have been made for urban topography, which is typically an integration of complex artificial and natural features. This study proposed a novel multi-scale mapping approach based on convolutional neural network (CNN) to deal with the complex features of urban topography and to reconstruct high-resolution urban DEMs. The proposed multi-scale CNN model was firstly trained using urban DEMs that contained topographic features at different resolutions, and then used to reconstruct the urban DEM at a specified (high) resolution from a low-resolution equivalent. A two-level accuracy assessment approach was also designed to evaluate the performance of the proposed urban DEM reconstruction method, in terms of numerical accuracy and morphological accuracy. The proposed DEM reconstruction approach was applied to a 121 km² urbanized area in London, United Kingdom. Compared with other commonly used methods, the current CNN-based approach produced superior results, providing a cost-effective innovative method to acquire high-resolution DEMs in other data-scarce regions.

In Bangladesh, each emergency physician faces amitriptyline overdose nearly a day. An acute cardiovascular complication, one of the worst complications is mainly responsible for the mortality in tricyclic overdose. Recently, we managed ventricular tachycardia in a young female presented with an impaired consciousness 10 h after intentionally ingesting 2500 mg amitriptyline. Here, we report it, discuss how the electrocardiography is vital to acknowledge and predict it and its’ complications and also the recent update of the management of it. A young married Bangladeshi-Bengali girl, 25-year-old, having a history of disharmony with her husband, came with an impaired consciousness after intentionally ingesting 2500 mg amitriptyline about 10 h before arrival. There was blood pressure 140/80 mmHg, heart rate 140 beats-per-min, temperature 103 °F, Glasgow coma scale 10/15, wide complex tachycardia with QRS duration of 178 ms in electrocardiography, blood pH 7.36. Initially, treated with 100 ml 8.4% sodium bicarbonate. After that, QRS duration came to 100 ms in electrocardiography within 10 min of infusion. To maintain the pH 7.50–7.55 over the next 24 h, the infusion of 8.4% sodium bicarbonate consisting of 125 ml dissolved in 375 ml normal saline was started and titrated according to the arterial blood gas analysis. Hence, a total dose of 600 mmol sodium bicarbonate was given over next 24 h. In addition to this, gave a 500 ml intravenous lipid emulsion over 2 h after 24 h of admission as she did not regain her consciousness completely. Afterward, she became conscious, though, in electrocardiography, ST/T wave abnormality persisted. So that, we tapered sodium bicarbonate infusion slowly and stopped it later. At the time of discharge, she was by heart rate 124/min, QRS duration 90 ms in electrocardiogram along with other normal vital signs. Diagnosis of amitriptyline-induced ventricular tachycardia is difficult when there is no history of an overdose obtained. Nevertheless, it should be performed in the clinical background and classic electrocardiographic changes and wise utilization of sodium bicarbonate, intravenous lipid emulsion, and anti-arrhythmic drugs may save a life.

Flood modelling is essential for addressing a range of scientific and engineering challenges. In recent years, the high computational demands of solving shallow water equations numerically have led researchers to explore machine-learning-based emulators for predicting floods and flood risk. Specifically, the proliferation of convolutional neural networks in solving different scientific problems has encouraged researchers to investigate their applicability in flood modelling. Most of these studies, however, have focused on specific locations or hydrological conditions, meaning that their findings may not be directly applicable to other situations without additional data and further training. We present here a U-Net model, a popular deep learning algorithm, which has the capacity to approximate maximum flood depths across multiple return periods while maintaining catchment generalizability. The model was trained using the outputs from a 2D hydraulic model (JFlow) to predict maximum water depths for a set of rainfall return periods (20, 100 and 1000 years). The pre-trained model was then applied to estimate depths in three unseen catchment areas. Our results demonstrate that U-Net can be used to approximate water depths in previously unseen catchments with significantly less computational time compared to the 2D model.