Short-term flood probability density forecasting using a conceptual hydrological model with machine learning techniques

Zhou, Yanlai; Cui, Zhen; Lin, Kangling; Sheng, Sheng; Chen, Hua; Guo, Shenglian; Xu, Chong‐Yu

doi:10.1016/j.jhydrol.2021.127255

Cited by 53 publications

(20 citation statements)

References 50 publications

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“…Advanced flood forecasting systems for long-term and short-term prediction of floods are also significant for the generation of early flood warnings. The Hydraulic models of flow have been practiced forecasting rainfall, storms and tsunamis [36]. These models are also used to predict impact of climatic change [37], ocean waves [38] and floods [39].…”

Section: Related Workmentioning

confidence: 99%

FFM: Flood Forecasting Model Using Federated Learning

et al. 2023

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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]

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Section: Related Workmentioning

confidence: 99%

FFM: Flood Forecasting Model Using Federated Learning

et al. 2023

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“…One of the ways of hybrid modeling is to use the output of physicsbased models as input for ML models. Zhou et al (2022) has proposed an integrated model which combines the Xinanjiang conceptual model with the Monotone Composite Quantile Regression Neural Network (MCQRNN) for forecasting flood probability density where they fed the output of Xinanjiang model for forecasted steps, observed streamflow and rainfall at past steps to the MCQRNN model. Merely considering the streamflow in the forecasted inputs makes the model sensitive to the performance of the physics-based model.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Contextual Cues from a Conceptual Model with Predictive Skills of Machine Learning for Improved Predictability and Interpretability in the Hydrological Processes

Bhasme

Bhatia

2023

Preprint

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In recent years, Machine Learning (ML) techniques have gained the attention of the hydrological community for their better predictive skills. Specifically, ML models are widely applied for streamflow predictions. However, limited interpretability in the ML models indicates space for improvement. Leveraging domain knowledge from conceptual models can aid in overcoming interpretability issues in ML models. Here, we have developed the Physics Informed Machine Learning (PIML) model at daily timestep, which accounts for memory in the hydrological processes and provides an interpretable model structure. We demonstrated three model cases, including lumped model and semi-distributed model structures with and without reservoir. We evaluate the first two model structures on three catchments in India, and the applicability of the third model structure is shown on the two United States catchments. Also, we compared the result of the PIML model with the conceptual model (SIMHYD), which is used as the parent model to derive contextual cues. Our results show that the PIML model outperforms simple ML model in target variable (streamflow) prediction and SIMHYD model in predicting target variable and intermediate variables (for example, evapotranspiration, reservoir storage) while being mindful of physical constraints. The water balance and runoff coefficient analysis reveals that the PIML model provides physically consistent outputs. The PIML modeling approach can make a conceptual model more modular such that it can be applied irrespective of the region for which it is developed. The successful application of PIML in different climatic as well as geographical regions shows its generalizability.

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“…ML provides adequate computation power [29,30] and is used in a wide variety of research and applications in hydrology. Some examples of ML applications in the hydrology domain are rainfall-runoff prediction [31][32][33], flood forecasting [34][35][36], sedimentation studies [37][38][39], water quality prediction [40][41][42][43], groundwater prediction [44,45], river temperature prediction [46][47][48][49], and rainfall estimation [50,51]. In recent years, ML algorithms have significantly improved and are also widely used for rainfall-runoff simulation [52,53] thanks to the rapid advancement of computer technology.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning and Process-Based Models for Rainfall-Runoff Simulation in DuPage River Basin, Illinois

et al. 2022

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Rainfall-runoff simulation is vital for planning and controlling flood control events. Hydrology modeling using Hydrological Engineering Center—Hydrologic Modeling System (HEC-HMS) is accepted globally for event-based or continuous simulation of the rainfall-runoff operation. Similarly, machine learning is a fast-growing discipline that offers numerous alternatives suitable for hydrology research’s high demands and limitations. Conventional and process-based models such as HEC-HMS are typically created at specific spatiotemporal scales and do not easily fit the diversified and complex input parameters. Therefore, in this research, the effectiveness of Random Forest, a machine learning model, was compared with HEC-HMS for the rainfall-runoff process. Furthermore, we also performed a hydraulic simulation in Hydrological Engineering Center—Geospatial River Analysis System (HEC-RAS) using the input discharge obtained from the Random Forest model. The reliability of the Random Forest model and the HEC-HMS model was evaluated using different statistical indexes. The coefficient of determination (R2), standard deviation ratio (RSR), and normalized root mean square error (NRMSE) were 0.94, 0.23, and 0.17 for the training data and 0.72, 0.56, and 0.26 for the testing data, respectively, for the Random Forest model. Similarly, the R2, RSR, and NRMSE were 0.99, 0.16, and 0.06 for the calibration period and 0.96, 0.35, and 0.10 for the validation period, respectively, for the HEC-HMS model. The Random Forest model slightly underestimated peak discharge values, whereas the HEC-HMS model slightly overestimated the peak discharge value. Statistical index values illustrated the good performance of the Random Forest and HEC-HMS models, which revealed the suitability of both models for hydrology analysis. In addition, the flood depth generated by HEC-RAS using the Random Forest predicted discharge underestimated the flood depth during the peak flooding event. This result proves that HEC-HMS could compensate Random Forest for the peak discharge and flood depth during extreme events. In conclusion, the integrated machine learning and physical-based model can provide more confidence in rainfall-runoff and flood depth prediction.

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Short-term flood probability density forecasting using a conceptual hydrological model with machine learning techniques

Cited by 53 publications

References 50 publications

FFM: Flood Forecasting Model Using Federated Learning

FFM: Flood Forecasting Model Using Federated Learning

Leveraging Contextual Cues from a Conceptual Model with Predictive Skills of Machine Learning for Improved Predictability and Interpretability in the Hydrological Processes

Application of Machine Learning and Process-Based Models for Rainfall-Runoff Simulation in DuPage River Basin, Illinois

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