Including spatial distribution in a data-driven rainfall-runoff model to improve reservoir inflow forecasting in Taiwan

Multi-step ahead inflow forecasting has a critical role to play in reservoir operation and management in Taiwan during typhoons as statutory legislation requires a minimum of 3-hours warning to be issued before any reservoir releases are made. However, the complex spatial and temporal heterogeneity of typhoon rainfall, coupled with a remote and mountainous physiographic context makes the development of real-time rainfall-runoff models that can accurately predict reservoir inflow several hours ahead of time challenging. Consequently, there is an urgent, operational requirement for models that can enhance reservoir inflow prediction at forecast horizons of more than 3-hours. In this paper we develop a novel semi-distributed, data-driven, rainfall-runoff model for the Shihmen catchment, north Taiwan. A suite of Adaptive Network-based Fuzzy Inference System solutions is created using various combinations of auto-regressive, spatially-lumped radar and point-based rain gauge predictors. Different levels of spatially-aggregated radar-derived rainfall data are used to generate 4, 8 and 12 sub-catchment input drivers. In general, the semi-distributed radar rainfall models outperform their less complex counterparts in predictions of reservoir inflow at lead-times greater than 3-hours. Performance is found to be optimal when spatial aggregation is restricted to 4 sub-catchments, with up to 30% improvements in the performance over lumped and point-based models being evident at 5-hour lead times. The potential benefits of applying semi-distributed, data-driven models in reservoir inflow modelling specifically, and hydrological modelling more generally, is thus demonstrated

Merging multiple precipitation sources for flash flood forecasting

We investigated the effectiveness of combining gauge observations and satellite-derived precipitation on flood forecasting. Two data merging processes were proposed: the first one assumes that the individual precipitation measurement is non-bias, while the second process assumes that each precipitation source is biased and both weighting factor and bias parameters are to be calculated. Best weighting factors as well as the bias parameters were calculated by minimizing the error of hourly runoff prediction over Wu-Tu watershed in Taiwan. To simulate the hydrologic response from various sources of rainfall sequences, in our experiment, a recurrent neural network (RNN) model was used. The results demonstrate that the merged method used in this study can efficiently combine the information from both rainfall sources to improve the accuracy of flood forecasting during typhoon periods. The contribution of satellite-based rainfall, being represented by the weighting factor, to the merging product, however, is highly related to the effectiveness of ground-based rainfall observation provided gauged. As the number of gauge observations in the basin is increased, the effectiveness of satellite-based observation to the merged rainfall is reduced. This is because the gauge measurements provide sufficient information for flood forecasting; as a result the improvements added on satellite-based rainfall are limited. This study provides a potential advantage for extending satellite-derived precipitation to those watersheds where gauge observations are limited. © 2007 Elsevier B.V. All rights reserved

Complex Hydrological System Inflow Prediction using Artificial Neural Network

Artificial neural networks have been successfully used to model and predict water flows for a few decades. Different network types have proven to work better in different cases and additional tools and algorithms have been implemented to improve those neural models. However, some problems still occur in certain cases. This paper deals with the limitation of complex hydrological system inflow prediction using artificial neural network and inflow time series. This limitation is called the prediction lag and it disables the model from giving accurate predictions. To eliminate the prediction lag and to extend prediction horizon an alternative input variable named forecasted precipitation frequency is proposed in addition to antecedent inflow time-series. Simulation results prove the efficiency of the proposed solution that enables time series neural network model for 7th-day inflow prediction. This represents important information in operational planning of the hydrological system, used for short-term optimization of the system, e.g. optimization of the hydroelectric power plant operation

Short and long-term forecasting using artificial neural networks for stock prices in Palestine: a comparative study

To compare the forecast accuracy, Artificial Neural Networks, Autoregressive Integrated Moving Average and regression models were fit with training data sets and then used to forecast prices in a test set. Three different measures of accuracy were computed: Root Mean Square Error, Mean Absolute Error and Mean Absolute Percentage Error. To determine how the accuracy depends on sample size, models were compared between daily, monthly and quarterly time series of stock closing prices from Palestine

Short and long-term forecasting using artificial neural networks for stock prices in Palestine: a comparative study

To compare the forecast accuracy, Artificial Neural Networks, Autoregressive Integrated Moving Average and regression models were fit with training data sets and then used to forecast prices in a test set. Three different measures of accuracy were computed: Root Mean Square Error, Mean Absolute Error and Mean Absolute Percentage Error. To determine how the accuracy depends on sample size, models were compared between daily, monthly and quarterly time series of stock closing prices from Palestine

A time delay artificial neural network approach for flow routing in a river system

International audienceRiver flow routing provides basic information on a wide range of problems related to the design and operation of river systems. In this paper, three layer cascade correlation Time Delay Artificial Neural Network (TDANN) models have been developed to forecast the one day ahead daily flow at Ilarionas station on the Aliakmon river, in Northern Greece. The networks are time lagged feed-formatted with delayed memory processing elements at the input layer. The network topology is using multiple inputs, which include the time lagged daily flow values further up at Siatista station on the Aliakmon river and at Grevena station on the Venetikos river, which is a tributary to the Aliakmon river and a single output, which are the daily flow values at Ilarionas station. The choice of the input variables introduced to the input layer was based on the cross-correlation. The use of cross-correlation between the ith input series and the output provides a short cut to the problem of the delayed memory determination. Kalman's learning rule was used to modify the artificial neural network weights. The networks are designed by putting weights between neurons, by using the hyperbolic-tangent function for training. The number of nodes in the hidden layer was determined based on the maximum value of the correlation coefficient. The results show a good performance of the TDANN approach for forecasting the daily flow values, at Ilarionas station and demonstrate its adequacy and potential for river flow routing. The TDANN approach introduced in this study is sufficiently general and has great potential to be applicable to many hydrological and environmental applications

A study of statistical and machine learning methods for power price prediction based on filling levels of hydropower reservoirs

The European power markets have become highly integrated over the past decade. The electrical grids of individual countries are increasingly well connected between each other, which allows for trading the electricity on the common markets and thus enhances the development of diverse electricity sources across the continent. With that comes an increasing volatility of the power prices. It is in the interest of all market players involved in generating, supplying, trading and consuming the electricity to find a way to forecast the power price as accurately as possible. This study investigates the potential of using filling level data from hydropower reservoirs and historical power price data - particularly, the Nordic system price - to forecast the future system price. For this purpose, three forecasting models for time series analysis were developed and evaluated - a statistical approach, as well as two artificial neural network architectures with different levels of complexity. The statistical approach is based on the autoregressive integrated moving-average model with exogenous inputs (ARIMAX), while the investigated neural networks include (a) a standard recurrent neural network (RNN), and (b) a combination of one-dimensional convolutional layers (1D CNNs) and a long short-term memory cell (LSTM). The experimental part of this work is based on data collected from 63 Norwegian hydropower reservoirs between 2015-2021. An extensive hyperparameter tuning was conducted on the machine learning models, including input data transformations, prediction time frames, network architecture parameters and the shape of the RNN/LSTM 3D input data tensor. The ARIMAX model outperformed the machine learning models for both most thoroughly tested prediction time frames of 14 and 28 days, achieving the R2 score of 0.8 and the MAE of 5.40 EUR. After a qualitative assessment of the obtained results it has been concluded that the models show some promising potential, however, a number of aspects would have to be further investigated to develop a mature solution, ready for practical use in, e.g. power trading.M-D

Flood Forecasting Using Machine Learning Methods

This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate

Optimisation of hedging-integrated rule curves for reservoir operation

Reservoir managers use operational rule curves as guides for managing and operating reservoir systems. However, this approach saves no water for impending droughts, resulting in large shortages during such droughts. This problem can be tempered by integrating hedging with the rule curves to curtail the water releases during normal periods of operation and use the saved water to limit the amount and impact of water shortages during droughts. However, determining the timing and amount of hedging is a challenge. This thesis presents the application of genetic algorithms (GA) for the optimisation of hedging-integrated reservoir rule curves. However, due to the challenge of establishing the boundary of feasible region in standard GA (SGA), a new development of the GA i.e. the dynamic GA (DGA), is proposed. Both the new development and its hedging policies were tested through extensive simulations of the Ubonratana reservoir (Thailand). The first observation was that the new DGA was faster and more efficient than the SGA in arriving at an optimal solution. Additionally, the derived hedging policies produced significant changes in reservoir performance when compared to no-hedging policies. The performance indices analysed were reliability (time and volume), resilience, vulnerability and sustainability; the results showed that the vulnerability (i.e. average single periods shortage) in particular was significantly reduced with the optimised hedging rules as compared to using the no-hedging rule curves. This study also developed a monthly inflow forecasting model using artificial neural networks (ANN) to aid reservoir operational decision-making. Extensive testing of the model showed that it was able to provide inflow forecasts with reasonable accuracy. The simulated effect on reservoir performance of forecasted inflows vis-à-vis other assumed reservoir inflow knowledge situations showed that the ANN forecasts were superior, further reinforcing the importance of good inflow information for reservoir operation. The ability of hedging to harness the inherent buffering capacity of existing water resources systems for tempering water shortage (or vulnerability) without the need for expensive new-builds is a major outcome of this study. Although applied to Ubonratana, the study has utility for other regions of the world, where e.g. climate and other environmental changes are stressing the water availability situation

MetZoom: A CNN/LSTM hybrid based model for water reservoir inflow prediction

Hydropower reservoir volumes fluctuate as water levels increase or decrease according to precipitation, valve output and inflow through water retained in the surrounding area. Predicting these fluctuations with machine learning is possible through the use of an Artificial Neural Network (ANN) architecture proposed in this thesis. The neural network model aims to fore- cast the changes in relative water level for a reservoir managed by Saudefaldene, a hydropower company in Rogaland, Norway. The predictions are made through the use of radar images reflecting the precipitation rate, and a dataset provided by Saudefaldene. The provided dataset contains the precipitation history, valve-opening records and relative water levels across 2014- 2021. Such a forecast can have various impacts on hydropower reservoir management, which lay the foundation for the thesis. The architecture proposed in this thesis, namely MetZoom, contains a Convolutional Neural Network (CNN) architecture which predicts future precipitation rates in the form of radar image replications and precipitation i up to 12 hours ahead. The use of radar images is motivated by the intent to forecast precipitation as a tool for predicting changes in the relative water level. The predictions made by the CNN are forwarded to a Recurrent Neural Network (RNN) in the form of a Long Short-Term Memory (LSTM) network to learn the fluctuations of reservoir water levels. The architecture of Met- Zoom is a result of several tested CNN and RNN models and a combination of these.Masteroppgave i informatikkINF399MAMN-PROGMAMN-IN