A Review of Hydrodynamic and Machine Learning Approaches for Flood Inundation Modeling

Karim, Fazlul; Armin, Mohammed Ali; Ahmedt-Aristizabal, David; Tychsen-Smith, Lachlan

doi:10.3390/w15030566

Cited by 38 publications

(21 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It has been observed that data-driven models usually make faster predictions with minimal input using ML [47]. Multiple ML algorithms including decision tree (DT) [48], Random Forest (RF) [49] and Gradient Boost (GB) [50] have been applied to improve fault-tolerant accuracy in flood prediction handling more intricate information by applying their complex algorithms in a short time span [51]. Innovative deep learning-based model has been suggested in [52] that provides accurate and timely flood prediction as compared to traditional models.…”

Section: Related Workmentioning

confidence: 99%

FFM: Flood Forecasting Model Using Federated Learning

et al. 2023

View full text Add to dashboard Cite

Floods are one of the most common natural disasters that occur frequently causing massive damage to property, agriculture, economy and life. Flood prediction offers a huge challenge for researchers struggling to predict floods since long time. In this article, flood forecasting model using federated learning technique has been proposed. Federated Learning is the most advanced technique of machine learning (ML) that guarantees data privacy, ensures data availability, promises data security, and handles network latency trials inherent in prediction of floods by prohibiting data to be transferred over the network for model training. Federated Learning technique urges for onsite training of local data models, and focuses on transmission of these local models on the network instead of sending huge data set towards central server for local model aggregation and training of global data model at the central server. In this article, the proposed model integrates locally trained models of eighteen clients, investigates at which station flooding is about to happen and generates flood alert towards a specific client with five days lead time. A local feed forward neural network (FFNN) model is trained at the client station where the flood has been expected. Flood forecasting module of local FFNN model predicts the expected water level by taking multiple regional parameters as input. The dataset of five different rivers and barrages has been collected from 2015 to 2021 considering four aspects including snow melting, rainfall-runoff, flow routing and hydrodynamics. The proposed flood forecasting model has successfully predicted previous floods happened in the selected zone during 2010 to 2015 with 84 % accuracy. INDEX TERMS hydraulic, meteorological, flood forecasting system, federated learning, feed-forward neural network.FIGURE 1. Natural hazards percentage as per occurrence in Pakistan during 2021 [6]

show abstract

Section: Related Workmentioning

confidence: 99%

FFM: Flood Forecasting Model Using Federated Learning

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Among data-driven methods, traditional machine learning (ML) approaches are widely used for inundation predictions and recently deep learning (DL) approaches have gained more attention across the research community. We reviewed recently published literature on ML and DL application for flood modelling for various hydrologic and catchment characteristics (Karim et al, 2023). Our literature review showed that DL models produce better accuracy compared to traditional approaches.…”

mentioning

confidence: 99%

Machine learning methods for flood prediction: A review of methods, their strengths and limitations

2023

MODSIM2023, 25th International Congress on Modelling and Simulation.

View full text Add to dashboard Cite

Floods are among the most damaging natural disasters in the world. They are also hard to predict numerically and require models with high complexity. Hydrodynamic models have been in use in the last several decades for flood prediction and inundation modelling. While hydrodynamic models are highly effective tools for estimating detailed and accurate flood information (e.g. flooded area, water height, flow velocity, timing of flood peak), they typically require extensive configuration and calibration before simulating future floods. Moreover, hydrodynamic modelling is computationally expensive, limiting the capability of these tools for near real-time flood predictions. In recent years with technological advancement, machine learning (also called data-driven) methods contributed highly to the advancement of prediction systems providing better performance and cost-effective solutions.

show abstract

“…

…”

mentioning

confidence: 99%

“…This category, often referred to as surrogate models, includes algorithms that predict flood inundation at specific future time points, as well as those that generate series of predictions.Compared with PIML, a key concern regarding ANN-based surrogate models is their generalizability. That is, if the simulation data used to train the surrogate model are insufficiently diverse (in terms of the covered ranges of scenarios and conditions) or inadequately representative, whether the model is able to effectively learn the underlying physics in the absence of physics-based constraints.Recently, we proposed an ANN-based framework for rapid inundation modelling in large regions (Tychsen-Smith, et al, 2023). This framework takes into account the local elevation, surface roughness, river inflows and the current water heights, and predicts the water heights at the subsequent time point.…”

mentioning

confidence: 99%

“…Scenarios of flood inundation are traditionally simulated by numerically solving the partial differential equations (PDEs) that govern fluid dynamics, with the initial and boundary conditions derived from observational data. While proven highly valuable for improving flood management and risk mitigation (Karim et al, 2023), such hydrodynamic simulations are limited in its applicability in near-real-time or emergency management due to its high demand on time and hardware resources.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Generalizability of deep-learning-based emulation of hydrodynamic flood models

2023

MODSIM2023, 25th International Congress on Modelling and Simulation.

View full text Add to dashboard Cite

Scenarios of flood inundation are traditionally simulated by numerically solving the partial differential equations (PDEs) that govern fluid dynamics, with the initial and boundary conditions derived from observational data. While proven highly valuable for improving flood management and risk mitigation (Karim et al., 2023), such hydrodynamic simulations are limited in its applicability in near-real-time or emergency management due to its high demand on time and hardware resources.Drastically improved efficiency can result from leveraging the power of deep artificial neural networks (ANNs). In a recently proposed methodology known as physics-informed machine learning (PIML, Karniadakis et al., 2021), ANNs are trained to obtain numerical solutions based on the PDEs and limited observational data. A more mature and realistic category of methods, on the other hand, employs ANNs to emulate traditional hydrodynamic simulators. That is, an ANN is trained to predict the output of a physicsbased simulator given its inputs. This category, often referred to as surrogate models, includes algorithms that predict flood inundation at specific future time points, as well as those that generate series of predictions.Compared with PIML, a key concern regarding ANN-based surrogate models is their generalizability. That is, if the simulation data used to train the surrogate model are insufficiently diverse (in terms of the covered ranges of scenarios and conditions) or inadequately representative, whether the model is able to effectively learn the underlying physics in the absence of physics-based constraints.Recently, we proposed an ANN-based framework for rapid inundation modelling in large regions (Tychsen-Smith, et al., 2023). This framework takes into account the local elevation, surface roughness, river inflows and the current water heights, and predicts the water heights at the subsequent time point. The prediction is performed at grid points in a rolling forward fashion. In this paper we focus on case studies on the generalizability of this framework. Specifically, we aim to answer the following questions: How well does a model based on our framework perform on unseen floods in the same location? How well does a model that was trained in one location perform in a different location? How much can the performance on unseen floods in unseen locations improve with an additional simulation in an additional location? Finally, we propose measures aimed at improving the generalizability of an ANN-based model.

show abstract

A Review of Hydrodynamic and Machine Learning Approaches for Flood Inundation Modeling

Cited by 38 publications

References 55 publications

FFM: Flood Forecasting Model Using Federated Learning

FFM: Flood Forecasting Model Using Federated Learning

Machine learning methods for flood prediction: A review of methods, their strengths and limitations

Generalizability of deep-learning-based emulation of hydrodynamic flood models

Contact Info

Product

Resources

About