Neural networks and non-parametric methods for improving real-time flood forecasting through conceptual hydrological models

International audienceTime-series analysis techniques for improving the real-time flood forecasts issued by a deterministic lumped rainfall-runoff model are presented. Such techniques are applied for forecasting the short-term future rainfall to be used as real-time input in a rainfall-runoff model and for updating the discharge predictions provided by the model. Along with traditional linear stochastic models, both stationary (ARMA) and non-stationary (ARIMA), the application of non-linear time-series models is proposed such as Artificial Neural Networks (ANNs) and the ?nearest-neighbours' method, which is a non-parametric regression methodology. For both rainfall forecasting and discharge updating, the implementation of each time-series technique is investigated and the forecasting schemes which perform best are identified. The performances of the models are then compared and the improvement in the efficiency of the discharge forecasts achievable is demonstrated when i) short-term rainfall forecasting is performed, ii) the discharge is updated and iii) both rainfall forecasting and discharge updating are performed in cascade. The proposed techniques, especially those based on ANNs, allow a remarkable improvement in the discharge forecast, compared with the use of heuristic rainfall prediction approaches or the not-updated discharge forecasts given by the deterministic rainfall-runoff model alone

Short-term rainfall nowcasting: using rainfall radar imaging

As one of the most useful sources of quantitative precipitation measurement, rainfall radar analysis can be a very useful focus for research into developing methods for rainfall prediction. Because radar can estimate rainfall distribution over a wide range, it is thus very attractive for weather prediction over a large area. Short lead time rainfall prediction is often needed in meteorological and hydrological applications where accurate prediction of rainfall can help with flood relief, with agriculture and with event planning. A system of short-term rainfall prediction over Ireland using rainfall radar image processing is presented in this paper. As the only input, consecutive rainfall radar images are processed to predict the development of rainfall by means of morphological methods and movement extrapolation. The results of a series of experimental evaluations demonstrate the ability and efficiency of using our rainfall radar imaging in a nowcasting system

A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series

Author name used in this publication: Chun-Tian Cheng2009-2010 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Multi-step Ahead Inflow Forecasting for a Norwegian Hydro-Power Use-Case, Based on Spatial-Temporal Attention Mechanism

Hydrological forecasting has been an ongoing area of research due to its importance to improve decision making on water resource management, flood management, and climate change mitigation. With the increasing availability of hydrological data, Machine Learning (ML) techniques have started to play an important role, enabling us to better understand and predict complex hydrological events. However, some challenges remain. Hydrological processes have spatial and temporal dependencies that are not always easy to capture with traditional ML models, and a thorough understanding of these dependencies is essential when developing accurate predictive models. This thesis explores the use of ML techniques in hydrological forecasting and consists of an introduction, two papers, and an application developed alongside the case study. The motivation for this research is to enhance our understanding of the spatial and temporal dependencies in hydrological processes and to explore how ML techniques, particularly those incorporating attention mechanisms, can aid in hydrological forecasting. The first paper is a chronological literature review that explores the development of data-driven forecasting in hydrology, and highlighting the potential application of attention mechanisms in hydrological forecasting. These attention mechanisms have proven to be successful in various domains, allowing models to focus on the most relevant parts of the input for making predictions, which is particularly useful when dealing with spatial and temporal data. The second paper is a case study of a specific ML model incorporating these attention mechanisms. The focus is to illustrate the influence of spatial and temporal dependencies in a real-world hydrological forecasting scenario, thereby showcasing the practical application of these techniques. In parallel with the case study, an application has been developed, employing the principles and techniques discovered throughout the course of this research. The application aims to provide a practical demonstration of the concepts explored in the thesis, contributing to the field of hydrological forecasting by introducing a tool for hydropower suppliers.Masteroppgave i Programvareutvikling samarbeid med HVLPROG399MAMN-PRO

Data-driven models for monthly streamflow time series prediction

Author name used in this publication: K.W. Chau2010-2011 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Predicting monthly streamflow using data-driven models coupled with data-preprocessing techniques

Author name used in this publication: K. W. Chau2009-2010 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

A Systematic Review of Real-time Urban Flood Forecasting Model in Malaysia and Indonesia -Current Modelling and Challenge

Several metropolitan areas in tropical Southeast Asia, mainly in Malaysia and Indonesia have lately been witnessing unprecedentedly severe flash floods owing to unexpected climate change. The fast water flooding has caused extraordinarily serious harm to urban populations and social facilities. In addition, urban Southeast Asia generally has insufficient capacity in drainage systems, complex land use patterns, and a largely susceptible population in confined urban regions. To lower the urban flood risk and strengthen the resilience of vulnerable urban populations, it has been of fundamental relevance to create real-time urban flood forecasting systems for flood disaster prevention agencies and the urban public. This review examined the state-of-the-art models of real-time forecasting systems for urban flash floods in Malaysia and Indonesia. The real-time system primarily comprises the following subsystems, i.e., rainfall forecasting, drainage system modeling, and inundation area mapping. This review described the current urban flood forecasting modeling for rainfall forecasting, physical-process-based hydraulic models for flood inundation prediction, and data-driven artificial intelligence (AI) models for the real-time forecasting system. The analysis found that urban flood forecasting modeling based on data-driven AI models is the most applied in many metropolitan locations in Malaysia and Indonesia. The analysis also evaluated the existing potential of data-driven AI models for real-time forecasting systems as well as the challenges towards i

A Promising Wavelet Decomposition –NNARX Model to Predict Flood: Application to Kelantan River Flood

Flood is a major disaster that happens around the world. It has caused many casualties and massive destruction of property. Estimating the chance of a flood occurring depends on several factors, such as rainfall, the structure and the flow rate of the river. This research used the neural network autoregressive exogenous input (NNARX) model to predict floods. One of the research challenges was to develop accurate models and improve the forecasting model. This research aimed to improve the performance of the neural network model for flood prediction. A new technique was proposed for modelling nonlinear data of flood forecasting using the wavelet decomposition-NNARX approach. This paper discusses the process of identifying the parameters involved to make a forecast as the rainfall value requires the flow rate of the river and its water level. The original data were processed by wavelet decomposition and filtered to generate a new set of data for the NNARX prediction model where the process can be compared. This research compared the performance of the wavelet and the non-wavelet NNARX model. Experimental results showed that the proposed approach had better performance testing results in relation to its counterpart in terms of hourly forecast, with the mean square error (MSE) of 2.0491e-4 m2 compared to 6.1642e-4 m2, respectively. The proposed approach was also studied for long-term forecast up to 5 years, where the obtained MSE was higher, i.e., 0.0016 m2

River flow forecasting using an integrated approach of wavelet multi-resolution analysis and computational intelligence techniques

In this research an attempt is made to develop highly accurate river flow forecasting models. Wavelet multi-resolution analysis is applied in conjunction with artificial neural networks and adaptive neuro-fuzzy inference system. Various types and structure of computational intelligence models are developed and applied on four different rivers in Australia. Research outcomes indicate that forecasting reliability is significantly improved by applying proposed hybrid models, especially for longer lead time and peak values