scholarly journals Geomorphology-based genetic programming approach for rainfall–runoff modeling

2012 ◽  
Vol 15 (2) ◽  
pp. 427-445 ◽  
Author(s):  
Vahid Nourani ◽  
Mehdi Komasi ◽  
Mohamad Taghi Alami
Keyword(s):  
Time Series ◽  
Genetic Programming ◽  
Spatial Data ◽  
Integrated Model ◽  
Programming Approach ◽  
Rainfall Time Series ◽  
Spatiotemporal Modeling ◽  
Rainfall Runoff ◽  
Runoff Modeling ◽  
Geomorphological Features

Nowadays, artificial intelligence approaches such as artificial neural network (ANN) as a self-learn non-linear simulator and genetic programming (GP) as a tool for function approximations are widely used for rainfall–runoff modeling. Both approaches are usually created based on temporal characteristics of the process. Hence, the motivation to present a comprehensive model which also employs the watershed geomorphological features as spatial data. In this paper, two different scenarios, separated and integrated geomorphological GP (GGP) modeling based on observed time series and spatially varying geomorphological parameters, were presented for rainfall–runoff modeling of the Eel River watershed. In the first scenario, the model could present a good insight into the watershed hydrologic operation via GGP formulation. In the second scenario, an integrated model was proposed to predict runoff in stations with lack of data or any point within the watershed due to employing the spatially variable geomorphic parameters and rainfall time series of the sub-basins as the inputs. This ability of the integrated model for the spatiotemporal modeling of the process was examined through the cross-validation technique. The results of this research demonstrate the efficiency of the proposed approaches due to taking advantage of geomorphological features of the watershed.

scholarly journals Multi-time-scale input approaches for hourly-scale rainfall–runoff modeling based on recurrent neural networks

2021 ◽  
Author(s):  
Kei Ishida ◽  
Masato Kiyama ◽  
Ali Ercan ◽  
Motoki Amagasaki ◽  
Tongbi Tu
Keyword(s):  
Time Series ◽  
Time Scale ◽  
Time Series Data ◽  
Series Data ◽  
Computational Time ◽  
Rainfall Runoff ◽  
Runoff Modeling ◽  
Input Time ◽  
Target Data ◽  
Resolution Data

Abstract This study proposes two effective approaches to reduce the required computational time of the training process for time-series modeling through a recurrent neural network (RNN) using multi-time-scale time-series data as input. One approach provides coarse and fine temporal resolutions of the input time-series data to RNN in parallel. The other concatenates the coarse and fine temporal resolutions of the input time-series data over time before considering them as the input to RNN. In both approaches, first, the finer temporal resolution data are utilized to learn the fine temporal scale behavior of the target data. Then, coarser temporal resolution data are expected to capture long-duration dependencies between the input and target variables. The proposed approaches were implemented for hourly rainfall–runoff modeling at a snow-dominated watershed by employing a long short-term memory network, which is a type of RNN. Subsequently, the daily and hourly meteorological data were utilized as the input, and hourly flow discharge was considered as the target data. The results confirm that both of the proposed approaches can reduce the required computational time for the training of RNN significantly. Lastly, one of the proposed approaches improves the estimation accuracy considerably in addition to computational efficiency.

Comparing runoff statistics simulated with a hydrological model utilizing stochastically generated lumped catchment rainfall and multiple point rainfall

2020 ◽  
Author(s):  
Luisa-Bianca Thiele ◽  
Ross Pidoto ◽  
Uwe Haberlandt
Keyword(s):  
Time Series ◽  
Hydrological Modelling ◽  
Flood Frequency ◽  
Multiple Point ◽  
Rainfall Time Series ◽  
Rainfall Runoff ◽  
Design Flood ◽  
Catchment Scale ◽  
Time Step ◽  
Rainfall Runoff Model

<p>For derived flood frequency analyses, stochastic rainfall models can be linked with rainfall-runoff models to improve the accuracy of design flood estimations when the length of observed rainfall and runoff data is not sufficient. In the past, when using stochastic rainfall time series for hydrological modelling purposes, catchment rainfall for use in hydrological modelling was calculated from the multiple point rainfall time series. As an alternative to this approach, it will be tested whether catchment rainfall can be modelled directly, negating the drawbacks (and need) encountered in generating spatially consistent time series. An Alternating Renewal rainfall model (ARM) will be used to generate multiple point and lumped catchment rainfall time series in hourly resolution. The generated rainfall time series will be used to drive the rainfall-runoff model HBV-IWW with an hourly time step for mesoscale catchments in Germany. Validation will be performed by comparing modelled runoff regarding runoff and flood statistics using stochastically generated lumped catchment rainfall versus multiple point rainfall. It would be advantageous if the results based on catchment rainfall are comparable to those using multiple point rainfall, so catchment rainfall could be generated directly with the stochastic rainfall models. Extremes at the catchment scale may also be better represented if catchment rainfall is generated directly.</p>

scholarly journals Identification of Hydrologic Models, Optimized Parameters, and Rainfall Inputs Consistent with In Situ Streamflow and Rainfall and Remotely Sensed Soil Moisture

2018 ◽  
Vol 19 (8) ◽  
pp. 1305-1320 ◽  
Author(s):  
Ashley J. Wright ◽  
Jeffrey P. Walker ◽  
Valentijn R. N. Pauwels
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Input Data ◽  
Remotely Sensed ◽  
Model Parameters ◽  
Rainfall Time Series ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Model Input ◽  
Rainfall Estimates

Abstract An increased understanding of the uncertainties present in rainfall time series can lead to improved confidence in both short- and long-term streamflow forecasts. This study presents an analysis that considers errors arising from model input data, model structure, model parameters, and model states with the objective of finding a self-consistent set that includes hydrological models, model parameters, streamflow, remotely sensed (RS) soil moisture (SM), and rainfall. This methodology can be used by hydrologists to aid model and satellite selection. Taking advantage of model input data reduction and model inversion techniques, this study uses a previously developed methodology to estimate areal rainfall time series for the study catchment of Warwick, Australia, for multiple rainfall–runoff models. RS SM observations from the Soil Moisture Ocean Salinity (SMOS) and Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) satellites were assimilated into three different rainfall–runoff models using an ensemble Kalman filter (EnKF). Innovations resulting from the observed and predicted SM were analyzed for Gaussianity. The findings demonstrate that consistency between hydrological models, model parameters, streamflow, RS SM, and rainfall can be found. Joint estimation of rainfall time series and model parameters consistently improved streamflow simulations. For all models rainfall estimates are less than the observed rainfall, and rainfall estimates obtained using the Sacramento Soil Moisture Accounting (SAC-SMA) model are the most consistent with gauge-based observations. The SAC-SMA model simulates streamflow that is most consistent with observations. EnKF innovations obtained when SMOS RS SM observations were assimilated into the SAC-SMA model demonstrate consistency between SM products.

A Genetic Programming Approach to System Identification of Rainfall-Runoff Models

2017 ◽  
Vol 31 (12) ◽  
pp. 3975-3992 ◽  
Author(s):  
Jayashree Chadalawada ◽  
Vojtech Havlicek ◽  
Vladan Babovic
Keyword(s):  
System Identification ◽  
Genetic Programming ◽  
Programming Approach ◽  
Rainfall Runoff

scholarly journals Selecting the Best Forecasting-Implied Volatility Model Using Genetic Programming

2009 ◽  
Vol 2009 ◽  
pp. 1-19 ◽  
Author(s):  
Wafa Abdelmalek ◽  
Sana Ben Hamida ◽  
Fathi Abid
Keyword(s):  
Time Series ◽  
Genetic Programming ◽  
Implied Volatility ◽  
Real Data ◽  
Programming Approach ◽  
Sample Mean ◽  
Mean Squared Errors ◽  
Hedging Strategies ◽  
Volatility Model ◽  
Out Of Sample

The volatility is a crucial variable in option pricing and hedging strategies. The aim of this paper is to provide some initial evidence of the empirical relevance of genetic programming to volatility's forecasting. By using real data from S&P500 index options, the genetic programming's ability to forecast Black and Scholes-implied volatility is compared between time series samples and moneyness-time to maturity classes. Total and out-of-sample mean squared errors are used as forecasting's performance measures. Comparisons reveal that the time series model seems to be more accurate in forecasting-implied volatility than moneyness time to maturity models. Overall, results are strongly encouraging and suggest that the genetic programming approach works well in solving financial problems.

scholarly journals A stochastic design rainfall generator based on copulas and mass curves

2010 ◽  
Vol 7 (3) ◽  
pp. 3613-3648 ◽  
Author(s):  
S. Vandenberghe ◽  
N. E. C. Verhoest ◽  
E. Buyse ◽  
B. De Baets
Keyword(s):  
Time Series ◽  
Return Period ◽  
Goodness Of Fit ◽  
Rainfall Time Series ◽  
Return Periods ◽  
Rainfall Runoff ◽  
Design Studies ◽  
Design Storm ◽  
Practical Usefulness ◽  
Storm Duration

Abstract. The use of design storms can be very useful in many hydrological and hydraulic practices. In this study, the concept of a copula-based secondary return period in combination with the concept of mass curves is used to generate design storms. The analysis is based on storms selected from the 105 year rainfall time series with a 10 min resolution, measured at Uccle, Belgium. In first instance, bivariate copulas and secondary return periods are explained, together with a focus on which couple of storm variables is of highest interest for the analysis and a discussion of how the results might be affected by the goodness-of-fit of the copula. Subsequently, the fitted copula is used to sample storms with a predefined secondary return period for which characteristic variables such as storm duration and total storm depth can be derived. In order to construct design storms with a realistic storm structure, mass curves of 1st, 2nd, 3rd and 4th quartile storms are developed. An analysis shows that the assumption of independence between the secondary return period and the internal storm structure could be made. Based on the mass curves, a technique is developed to randomly generate an intrastorm structure. The coupling of both techniques eventually results in a methodology for stochastic design storm generation. Finally, its practical usefulness for design studies is illustrated based on the generation of design storm ensembles and rainfall-runoff modelling.

scholarly journals Fully distributed rainfall-runoff modeling using spatial-temporal graph neural network

2022 ◽  
Author(s):  
Zhongrun Xiang ◽  
Ibrahim Demir
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Spatial Information ◽  
Model Performance ◽  
Grid Cell ◽  
Great Promise ◽  
Rainfall Runoff ◽  
Streamflow Forecasting ◽  
Runoff Modeling ◽  
Runoff Model

Recent studies using latest deep learning algorithms such as LSTM (Long Short-Term Memory) have shown great promise in time-series modeling. There are many studies focusing on the watershed-scale rainfall-runoff modeling or streamflow forecasting, often considering a single watershed with limited generalization capabilities. To improve the model performance, several studies explored an integrated approach by decomposing a large watershed into multiple sub-watersheds with semi-distributed structure. In this study, we propose an innovative physics-informed fully-distributed rainfall-runoff model, NRM-Graph (Neural Runoff Model-Graph), using Graph Neural Networks (GNN) to make full use of spatial information including the flow direction and geographic data. Specifically, we applied a time-series model on each grid cell for its runoff production. The output of each grid cell is then aggregated by a GNN as the final runoff at the watershed outlet. The case study shows that our GNN based model successfully represents the spatial information in predictions. NRM-Graph network has shown less over-fitting and a significant improvement on the model performance compared to the baselines with spatial information. Our research further confirms the importance of spatially distributed hydrological information in rainfall-runoff modeling using deep learning, and we encourage researchers to incorporate more domain knowledge in modeling.

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