Flood forecasting in large rivers with data-driven models

Nguyen, Phuoc Khac-Tien; Chua, Leok Poh; Son, Lam Hung

doi:10.1007/s11069-013-0920-7

Cited by 23 publications

(5 citation statements)

References 19 publications

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“…In 2013, Gourbesville et al [26] proposed that ubiquitous computing in relation to flood warning systems resulted in better supervision of warning systems. In 2013, Nguyen et al [27] used adaptive neuro-fuzzy inference system to forecast water levels at three stations. The flow conditions at different locations along the mainstream can differ in terms of the contributions from upstream stations and contributions from rainfall and lateral flows from subbasins.…”

Section: Hpc Cloud Computing In Digital Earthmentioning

confidence: 99%

IoT, big data and HPC based smart flood management framework

Sood

Sandhu

Singla

et al. 2018

Sustainable Computing: Informatics and Systems

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The disastrous effect of flood has shown its influence in the past, and as a result, millions of dollars infrastructure have been shattered. Even after so much research, still there is no global ubiquitous system that can collect, store and analyze big data and generate the flood prediction results. In this paper, a social collaborative Internet of Things (IoT) based smart flood monitoring and forecasting architecture is proposed with the convergence between big data and HPC. It classifies geographical areas into a web of hexagonal for effective installation of energy efficient IoT devices. All relevant flood causing and flood preventing attributes are sensed using these IoT devices and computed by big data and HPC rocessing. Singular Value Decomposition (SVD) is used for attributes reduction. The K-mean clustering algorithm is used to predict the current state of flood and flood rating in any location, whereas Holt-Winter's forecasting method is used to forecast the flood. Experimental evaluation is being done on meteorological data collected by the Indian government and results indicated the effectiveness of the proposed architecture.

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Section: Hpc Cloud Computing In Digital Earthmentioning

confidence: 99%

IoT, big data and HPC based smart flood management framework

Sood

Sandhu

Singla

et al. 2018

Sustainable Computing: Informatics and Systems

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“…These approaches were implemented for studies over a great number of catchments worldwide, within operational framework in some cases (Yuan et al, 2020;Nevo et al, 2022). Most strategies are based on Neural Network (NN) models such as Multilayer Perceptron (MLP) (Riad et al, 2004;Mosavi et al, 2018;Noymanee and Theeramunkong, 2019), which is a simple version of feed-forward neural networks (Dawson and Wilby, 1998;Toukourou et al, 2011); or ANFIS models (Khac-Tien Nguyen and Hock-Chye Chua, 2012;Nguyen et al, 2014). These studies showed that ML models can outperform standard solvers like rainfall-runoff models, especially for highly correlated networks and short lead times.…”

Section: Introductionmentioning

confidence: 99%

Real-time flood forecasting with Machine Learning using scarce rainfall-runoff data

Defontaine,

Ricci,

Lapeyre

et al. 2024

Preprint

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Abstract. Flooding is the most devastating natural hazard that our society must adapt to worldwide, especially as the severity and the occurrence of flood events intensify with climate change. Several initiatives have joined efforts in monitoring and modelling river hydrodynamics, in order to provide Decision Support System services with accurate flood prediction at extended forecast lead times. This work presents how fully data-driven machine learning models predict discharge with better performance and extended lead-time, with respect to the current empirical Lag and Route model used operationally at the local flood forecasting services for the Garonne River in Toulouse. The database is composed of discharge and rainfall data, upstream of Toulouse, for 36 flood events over the past 15 years (40 k data points). This scarce data set is used to train a Linear Regression, a Gradient Boosting Regressor and a MultiLayer Perceptron in order to forecast the discharge in Toulouse at 6-hour and 8-hour lead times. We showed that the machine learning approach outperforms the empirical Lag and Route for 6-hour lead-time. It also provides a reliable solution for extended lead times and saves the implementation of a new empirical Lag and Route model. It was demonstrated that the scarcity and the heterogeneity of the data heavily weigh on the learning strategy and that the layout of the learning and validation sets should be adapted to the presence of outliers. It was also shown that the addition of rainfall data increases the predictive performance of machine learning models, especially for longer lead times. Different strategies for rainfall data preprocessing were investigated. This study concludes that, with the present test case, time-averaged rain information should be favored over instantaneous or time varying data.

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“…Broadly, rainfall-runoff models can be classified into the following: (i) data-driven-based methods (Nguyen & Chua 2012;Nguyen et al 2014;Yaghoubi et al 2019;Hadid et al 2020;Xiang et al 2020), (ii) conceptual model-based approaches (Nash & Sutcliffe 1970;Brath & Rosso 1993;Kan et al 2017;Unduche et al 2018), and (iii) physical model-based methods (Vieux et al 2003;Chen et al 2016;Setti et al 2020). Of these methods, datadriven-based models were found to be of a great value due to their accuracy and simplicity (Teegavarapu & Chandramouli 2005;Wu et al 2009;Wu & Chau 2010;Orouji et al 2013;Nguyen et al 2014;Steyn et al 2017;Ahani et al 2018;Mazrooei & Sankarasubramanian 2019). Precipitation is a significant input variable of the different input parameters required for rainfall-runoff models (Mazrooei & Sankarasubramanian 2019;Guntu et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Investigation of satellite precipitation product driven rainfall-runoff model using deep learning approaches in two different catchments of India

Yeditha

Rathinasamy

Neelamsetty

et al. 2021

Journal of Hydroinformatics

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Rainfall–runoff models are valuable tools for flood forecasting, management of water resources, and drought warning. With the advancement in space technology, a plethora of satellite precipitation products (SPPs) are available publicly. However, the application of the satellite data for the data-driven rainfall–runoff model is emerging and requires careful investigation. In this work, two satellite rainfall data sets, namely Global Precipitation Measurement-Integrated Multi-Satellite Retrieval Product V6 (GPM-IMERG) and Climate Hazards Group Infrared Precipitation with Station (CHIRPS), are evaluated for the development of rainfall–runoff models and the prediction of 1-day ahead streamflow. The accuracy of the data from the SPPs is compared to the India Meteorological Department (IMD)-gridded precipitation data set. Detection metrics showed that for light rainfall (1–10 mm), the probability of detection (POD) value ranges between 0.67 and 0.75 and with an increasing rainfall range, i.e., medium and heavy rainfall (10–50 mm and >50 mm), the POD values ranged from 0.24 to 0.45. These results indicate that the satellite precipitation performs satisfactorily with reference to the IMD-gridded data set. Using the daily precipitation data of nearly two decades (2000–2018) over two river basins in India's Eastern part, artificial neural network, extreme learning machine (ELM), and long short-time memory (LSTM) models are developed for rainfall–runoff modelling. One-day ahead runoff prediction using the developed rainfall–runoff modelling confirmed that both the SPPs are sufficient to drive the rainfall–runoff models with a reasonable accuracy estimated using the Nash–Sutcliffe Efficiency coefficient, correlation coefficient, and the root-mean-squared error. In particular, the 1-day streamflow forecasts for the Vamsadhara river basin (VRB) using LSTM with GPM-IMERG inputs resulted in NSC values of 0.68 and 0.67, while ELM models for Mahanadhi river basin (MRB) with the same input resulted in NSC values of 0.86 and 0.87, respectively, during training and validation stages. At the same time, the LSTM model with CHIRPS inputs for the VRB resulted in NSC values of 0.68 and 0.65, and the ELM model with CHIRPS inputs for the MRB resulted in NSC values of 0.89 and 0.88, respectively, in training and validation stages. These results indicated that both the SPPs could reliably be used with LSTM and ELM models for rainfall–runoff modelling and streamflow prediction. This paper highlights that deep learning models, such as ELM and LSTM, with the GPM-IMERG products can lead to a new horizon to provide flood forecasting in flood-prone catchments.

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Flood forecasting in large rivers with data-driven models

Cited by 23 publications

References 19 publications

IoT, big data and HPC based smart flood management framework

IoT, big data and HPC based smart flood management framework

Real-time flood forecasting with Machine Learning using scarce rainfall-runoff data

Investigation of satellite precipitation product driven rainfall-runoff model using deep learning approaches in two different catchments of India

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