Statistical and Hybrid Methods Implemented in a Web Application for Predicting Reservoir Inflows during Flood Events

Abstract

Reservoir management is a critical component of flood management, and information on reservoir inflows is particularly essential for reservoir managers to make real-time decisions given that flood conditions change rapidly. This study's objective is to build real-time data-driven services that enable managers to rapidly estimate reservoir inflows from available data and models. We have tested the services using a case study of the Texas flooding events in the Lower Colorado River Basin in November 2014 and May 2015, which involved a sudden switch from drought to flooding. We have constructed two prediction models: a statistical model for flow prediction and a hybrid statistical and physics-based model that estimates errors in the flow predictions from a physics-based model. The study demonstrates that the statistical flow prediction model can be automated and provides acceptably accurate short-term forecasts. However, for longer term prediction (2 h or more), the hybrid model fits the observations more closely than the purely statistical or physics-based prediction models alone. Both the flow and hybrid prediction models have been published as Web services through Microsoft's Azure Machine Learning (AzureML) service and are accessible through a browser-based Web application, enabling ease of use by both technical and nontechnical personnel.(KEY TERMS: flooding; data-driven model services; AzureML; reservoir inflow.)

Featured Collection Introduction: National Flood Interoperability Experiment II

“…Their work was tested in watersheds in Washington and Idaho and showed a very high degree of accuracy. Zhao et al (2017) presented an application that predicts reservoir inflows from statistical and physicsbased models using a machine learning model on Azure. This is a key element in water resources management and their approach could be compared with predictions made by the national water model.…”

Flood Disaster and Early Warning: Application of ANFIS for River Water Level Forecasting

“…To date, a number of flood forecasting models are mainly data-specific and involve simplified various input assumptions [3]. Thus to mimic the complex mathematical expression of physical processes and river behaviors, such models benefit from specific techniques e.g., empirical black-box models, stochastic and hybrids [4]. These physically and statistically based models boost the usage of advanced data-driven methods, e.g., machine learning techniques.…”

Intelligent flood disaster warning on the fly: developing IoT-based management platform and using 2-class neural network to predict flood status

“…A flow rate sensor and a water level sensor were utilized in the process of data acquisition while prediction model was done to make a system that can predict flood occurrence. Researchers have used statistical means to analyse flood data in [5,7] while the use of ANN and other hybrid system integrating fuzzy system and neural network have been heavily explored in [8][9][10][11][12][13][14]. The use of Boosted regression tree for analysis was carried out in [8] to analyze reservoir inflow.…”

“…Researchers have used statistical means to analyse flood data in [5,7] while the use of ANN and other hybrid system integrating fuzzy system and neural network have been heavily explored in [8][9][10][11][12][13][14]. The use of Boosted regression tree for analysis was carried out in [8] to analyze reservoir inflow. Wavelet Analysis was used to clean the data from unwanted errors that might exist in the data from the reservoir inflow.…”

“…Boosted regression tree is a neural network model which reduces error in the previous tree network by limiting the mean difference of the data under test [7]. [8] used a neuro-fuzzy inference (ANFIS) hybrid system to modelled rainfall data and the system integrated both fuzzy and neural network. Classification of inputs into member functions is carried out by fuzzy systems and the output can be used to make decision and control purposes.…”