Neuro-fuzzy river ice breakup forecasting system

doi:10.1016/j.coldregions.2006.08.009

Cold Regions Science and Technology

Volume 46, Issue 2, November 2006, Pages 100-112

https://doi.org/10.1016/j.coldregions.2006.08.009 Get rights and content

Abstract

Despite the serious threat posed to communities by ice during spring river ice breakup, there are no reliable means to predict the severity of breakup with a significant lead time. Building on previous data collection and regression analyses for the Athabasca River at Fort McMurray, this paper evaluates the application of soft computing through fuzzy logic and artificial neural networks for modeling the maximum water level during river ice breakup for both flood and non-flood event years. A prototype fuzzy logic model is presented, based on four input variables, each available with a lead time of several weeks prior to river breakup. The performance of the model was evaluated for several designs including a neuro-fuzzy model created to reduce the subjectivity of expert knowledge for rule base definition. It was found that a simple fuzzy expert system, based exclusively on expert experience, could qualitatively distinguish years when flooding occurred but produced poor quantitative results. A neuro-fuzzy model was able to simulate water levels with an R² of 0.88, performing equally in comparison to a multiple linear regression model based on twice as many input variables, some with much less lead time. The performance of this neuro-fuzzy model with relatively few input variables holds promise for modeling sites where the volume of available data is limited.

Introduction

The spring breakup period is often a time of severe flood threat for many northern communities. The clearing of the winter ice cover can vary between two extremes: one innocuous, where the ice cover deteriorates due to meteorological influences and simply melts in place, and one quite threatening, where a large snowmelt runoff wave lifts and breaks the ice cover resulting in ice runs and ice jams. Ice jam formation and release events are among the most dangerous types of flood risk situations, primarily because the sudden congestion of a river channel with ice can result in dramatic and rapid water level increases. Water can rise several meters in a matter of minutes, inundating flood prone areas with little or no warning, and providing very little time to perform even the most basic mitigation measures. As a result, flood damages are usually high; the U.S. Army Corps of Engineers (2004) estimates that ice jam damages in the United States alone amount to more than $100 million annually.

A number of researchers have developed predictive methods for breakup ice jam forecasting but, due the complex interactions between hydrometeorological influences and ice mechanical properties, only limited progress has been achieved to date using purely deterministic approaches for modeling dynamic river breakup. Until now, the majority of river breakup forecasting tools have been statistically based, including threshold models (e.g. Shulyakovskii, 1963, Wuebben et al., 1995), multiple regression models (e.g. Beltaos, 1984, Mahabir et al., in press) and discriminant analysis models (Zachrisson, 1990; White and Daly, 2002). Massie et al. (2001) developed an artificial neural network (ANN) to produce a daily forecast of jam/no jam. Some of the common criticisms of these earlier models are that they are site specific, prone to false positive results, provide only qualitative assessments (jam/no jam) and require data available only days before river breakup, limiting the practical lead time for potential forecasting applications.

Belonging to the same family of soft computing methods as ANNs, but not based exclusively on recorded data, fuzzy logic is another non-linear method that has potential for application in river ice breakup forecasting. Zongfu (1992) first proposed the idea of using fuzzy logic for predicting ice jam occurrence and Mahabir et al. (2002) first applied it to develop a promising preliminary model for the Athabasca River, Canada, which produced a qualitative prediction of breakup water levels, showing few false positives for moderate events and no false positives for major events. Shouyu and Honglan (2005) used fuzzy logic to optimize an ANN in an attempt to predict the timing of breakup on the Yellow River, China forecasting the breakup date within 7 days of actual for the 5 validation years (but also reporting that over 90% of the time river breakup is within 7 days of the median breakup date).

Fuzzy logic is a form of artificial intelligence that is ideal for incorporating generalized knowledge. With fuzzy logic, linguistic descriptions are used to represent inputs, to evaluate input sets based on defined rules and to provide a linguistic assessment of the resulting set. Pioneered by Zadeh (1965), fuzzy logic has been effectively used in combination with other soft computing methods for predictive water resource related sciences. One of the primary advantages of fuzzy logic over traditional mathematics is that it is enables the modeler to incorporate a conceptual understanding of cause and effect relationships describing the process to be modeled. This is ideally suited to the river ice breakup flood forecasting application, while it is not yet possible to model the complex hydrometeorological interactions leading to the occurrence of ice jams in a fully deterministic manner, many heuristic “rules of thumb” do exist. For example, a high spring runoff would be expected to increase the likeliness of an ice jam occurrence.

Fuzzy logic has also been combined successfully with other forms of modeling to produce hybrid models that incorporate the advantages of both parent models. For example, Nayak et al. (2005) found that a neuro-fuzzy model had superior performance to both fuzzy models and ANNs for long lead forecasts in rainfall runoff process models. For river ice breakup modeling, combining fuzzy logic with the learning ability of ANNs in a neuro-fuzzy model provides the potential to combine available heuristic knowledge with limited recorded data in model development. A hybrid neuro-fuzzy model combines the modeling advantages gained with fuzzy logic with the ability to learn from the limited historical data that is available.

This paper details the development of fuzzy logic and neuro-fuzzy models for river ice jam flood forecasting. It provides insight into the application of fuzzy logic to predicting the severity of river ice breakup and the ability to use ANNs to make optimal use of the limited data so typical in this application. The potential for both linguistic assessments and quantitative predictions are explored. A prototype model is developed and alternate selections in model design are compared.

Section snippets

Site description and previous models

The Athabasca River is the largest unregulated river in Alberta, Canada. It has its headwaters in the Rocky Mountains and flows in a northeasterly direction across the province to the Peace Athabasca Delta as shown in Fig. 1. The drainage basin, as measured at the Water Survey of Canada gauging site just below Fort McMurray, is 133,000 km² with the majority of the basin area located south of this gauge site. Because the southern reaches tend to produce snowmelt prior to significant ice

Basic components of a fuzzy logic model

The basic components of fuzzy expert systems involve fuzzification of the input variables, application of a fuzzy operator, implication from an antecedent to the consequent, aggregation of the consequents across the rules and, potentially, defuzzification (interpretation of resultant fuzzy set to a crisp or unique number). A basic description of the components is presented here with more details provided in relation to aspects that were found to be important to river ice modeling. Several books

Variable selection

While the multiple linear regression model (Mahabir et al., in press) had its limitations, the variables it identified as key to modeling river breakup should provide a reasonable basis from which to consider variables for a nonlinear model, as highly influential variables should play a role in both models. The variables in this fuzzy logic prototype model were therefore selected from that multiple linear regression model. It logically follows that the variables should be considered

Prototype neuro-fuzzy model

To reduce the subjectivity of the prototype model, ANNs were explored for training the rule base with fuzzyTECH® software. Through backward propagation with random unvised training, the degree of support for each rule and the distribution of the membership functions could be determined. (The degree of support is equivalent to rule weightings in an ANN for this application.) A complete rule base containing all possible outcomes was generated with random weightings for each rule. All training was

Type of membership function

For both the expert knowledge fuzzy logic model and the neuro-fuzzy trained model, performance was not improved by altering the definition of membership functions from the standard membership functions to statistical membership functions. Likely this is somewhat due to the fact that none of the variables used in the model were highly skewed; therefore, the statistical membership functions did not vary significantly from the standard membership functions. While the standard membership functions

Conclusions

Fuzzy logic modeling appears to provide a promising new tool for forecasting flood levels associated with breakup ice jams. Using data from the Athabasca River Basin, both fuzzy and neuro-fuzzy expert systems were successful in producing qualitative models for predicting the severity of water levels associated with the spring breakup. From this research, it appears that the development of reliable qualitative risk assessment models may be feasible at locations that do not have an extensive

Acknowledgements

The authors would like to gratefully acknowledge funding support from Alberta Environment and the NSERC MAGS Research Network. In addition, the knowledge and experience shared by Larry Garner, Alberta Environment, has made a significant contribution to the modeling presented in this paper.

References (22)

L.A. Zadeh
Fuzzy sets
Information and Control
(1965)
A. Bardossy et al.
Fuzzy sets, possibilities and probabilities
A. Bardossy et al.
Fuzzy regression in hydrology
Water Resources Research
(1990)
S. Beltaos
Study of river ice breakup using hydrometric station records
J.M. De La Garza et al.
Knowledge-elicitation study in construction scheduling domain
Journal of Computing in Civil Engineering
(1990)
A. Robinson Fayek et al.
A fuzzy expert system for design performance prediction and evaluation
Canadian Journal of Civil Engineering
(2001)
FuzzyTECH 5.5. 2001. User's Manual. INFORM GmbH Inform Software...
C. Mahabir et al.
Forecasting ice jam risk at Fort McMurray, AB using fuzzy logic
Mahabir, C., Hicks, F.E., Robichaud, C., Robinson Fayek, A. 2006 forecasting breakup water levels at Fort McMurray, AB,...
D.D. Massie et al.
Predicting ice jams with neural networks

P.C. Nayak et al.

Short-term flood forecasting with a neuro-fuzzy model

Water Resources Research

(2005)

Cited by (53)

The prediction of mid-winter and spring breakups of ice cover on Canadian rivers using a hybrid ontology-based and machine learning model
2023, Environmental Modelling and Software
Citation Excerpt :
Machine learning has also been extended to the prediction of spring breakup, using techniques such as artificial neural networks (ANN) (Zhao et al., 2012; Guo et al., 2018), support vector machines (SVM) (Wang et al., 2010; Barzegar et al., 2019), k-nearest neighbors (KNN) (Sun et al., 2020), adaptive neuro-fuzzy inference systems (ANFIS) (Sun and Trevor, 2015, 2018a), and ensemble techniques such as stacking ensembles (Sun, 2018). Similar studies have applied these techniques in the prediction of breakup ice jams, using methods such as ANNs (Massie et al., 2002), ANFIS (Mahabir et al., 2006), and stacking ensembles (De Coste et al., 2021). The challenges faced by these studies often revolve around data availability, with studies focussing on single rivers having smaller amounts of data to train models with and studies focussing on larger regions encountering issues related to the management of complex data.
Mid-winter breakups (MWBs) are an increasingly common event on Canadian rivers resulting in the early breakup of river ice cover, with complex and numerous drivers. This study focuses on the development of an MWB Ontology which allows the key data, events, and relationships in an ice season to be defined and analyzed. The MWB Ontology is applied to a national case study of 54 rivers in Canada. Through assessment of the MWB Ontology with network analysis techniques, a hybrid modelling framework coupling the MWB Ontology with machine learning is developed. The hybrid models produce greatly reduced errors in their forecasts of the timing breakup events, with the best performance being a mean absolute error of 12.47 days for MWBs and 10.68 days for spring breakup. The results demonstrate the utility of the MWB Ontology as a tool for collating data and a means for analysis and forecasting of these events.
Machine-learning approach for predicting the occurrence and timing of mid-winter ice breakups on canadian rivers
2022, Environmental Modelling and Software
The increasingly common occurrence of Mid-Winter Breakups (MWBs) in Canadian rivers, consisting of the early breakup of ice cover outside of the typical spring season, is a cause for concern. This study applied various data-driven modelling techniques to predict MWBs occurrence and timing with sufficient lead times on a national scale using a new Canadian River Ice Database (CRID) coupled with National Resources Canada gridded climate data. A two-level machine learning model structure was developed, with the first level model predicting MWB occurrence within a given period and the second level model predicting the timing of an MWB occurrence within that period. Machine learning techniques that can handle class imbalance were employed to address many of the issues inherent in rare event forecasting, including the implementation of data preprocessing, the selection of algorithms and performance metrics suited to rare events. Multiple configurations of both model levels, including variations on time series arrangement and input variables, were tested to select the optimal model structure. The best performing configuration, focussing on a biweekly time period, attained overall accuracies of 80.1% and 77.6% for the first and second level models respectively on the 452 MWBs in the CRID. In addition, probabilistic prediction results were analyzed to evaluate model uncertainty and robustness. This new modelling framework provides the first tool capable of predicting MWBs on a national scale, with easily extendable methodology to locations that have not yet experienced MWBs and can form the basis of vital decision-making support to affected communities.
Assessing and predicting the severity of mid-winter breakups based on Canada-wide river ice data
2022, Journal of Hydrology
Mid-winter breakups (MWBs), consisting of the early breakup of the winter river ice cover before the typical spring breakup season, are becoming increasingly common events in cold region rivers. These events can lead to potentially severe flooding, while also altering the expected spring flow regime, yet data on these events is limited. In this study, a newly released Canadian River Ice Database (CRID), containing river ice data from 196 rivers across Canada obtained from time series analysis, was used to analyse these MWBs on a previously impossible national scale. The CRID data was combined with the Natural Resources Canada (NRCan) gridded daily climate dataset to identify a list of potential hydrologic and climatic drivers for MWB events. Techniques such as correlation analysis, Least Absolute Selection Shrinkage Operator (LASSO) regression, and input omission were combined to select 20 key drivers of the severity of MWB events. A random forest model that was trained with these drivers using data-driven modelling techniques successfully classified the MWBs as either low, medium, or high severity, achieving an overall accuracy of 80%. A new threshold for the prediction of MWB initiation based on climatic conditions was subsequently proposed through the use of optimization via an exhaustive grid search and its accuracy in identifying MWBs exceeded those proposed by previous studies. The new threshold used in conjunction with the random forest model provide valuable tools for both the prediction of MWBs and the assessment of their potential severity.
A hybrid ensemble modelling framework for the prediction of breakup ice jams on Northern Canadian Rivers
2021, Cold Regions Science and Technology
The forecasting of river ice jams faces challenges relating to both the availability of data and the complexity of river ice dynamics, resulting in difficulties in model formulation. In this study, a hybrid ensemble modelling framework is developed to address the data scarcity issue and leverage advanced machine learning techniques for the prediction of ice jams with a one-day lead time. The proposed methodology utilises data easily monitored in advance of any ice jam events and maintains a realistic balance between ice jam and non-ice jam events. A combination of both single model algorithms, including classification trees, logistic regression, k-nearest neighbors, support vector machines, and artificial neural networks, and ensemble model algorithms, including random forest, adaptive boosting, gradient boosting, variance penalizing adaptive boosting, logistic boosting, class switching, and adaptive resampling and combining, are considered for both the member models of the first layer of the hybrid ensemble and for the ensemble combiner of the second layer. The final selection of both variables and member models for the hybrid ensemble is detailed, with a focus on the reduction of false negatives, the prediction of no ice jam on a day when one occurs. The proposed method is applied to the St. John River in New Brunswick, Canada, in a location particularly prone to ice jam flooding. Using the proposed methodology, a final model combining 6 different member models using a support vector machine as the ensemble combiner was produced, with a balanced prediction accuracy of 86%. This hybrid ensemble model outperformed the other tested ensemble models, as well as a series of generalized models produced using all available input variables and member models. The model also performed well against other ensemble techniques and against the individual member models. These results demonstrate the viability of the proposed methodology in constructing a hybrid ensemble model for the forecasting of ice jams on Northern Canadian Rivers. The techniques utilised can be adapted to other locations to facilitate ice jam forecasting, requiring data that is easily available and monitored in advance of any potential flooding events.
Ice jam formation, breakup and prediction methods based on hydroclimatic data using artificial intelligence: A review
2020, Cold Regions Science and Technology
Citation Excerpt :
Fig. 6 illustrates an example of a crossover. The review of literature shows that ANNs have been successfully applied to predict river ice jam flooding (e.g., Mahabir et al., 2007), river ice breakup (e.g., Mahabir et al., 2006; McDonald et al., 2002; Zhao et al., 2012), timing of ice formation and breakup (e.g., Hu et al., 2008; Chen and Ji, 2005; Sun and Trevor, 2018a; Wang and Li, 2009; Wang et al., 2008), river ice thickness (Chokmani et al., 2007), water level (Sun and Trevor, 2018b), and ice jam thickness (Wang et al., 2010). They have also successfully modelled ice growth on lakes (e.g., Seidou et al., 2006) and simulated ice conditions and parameters (Morse et al., 2003; Wang et al., 2008).
In cold regions, the high occurrence of ice jams results in severe flooding and significant damage caused by a rapid rise in water levels upstream of ice jams. These floods can be critical hydrological and hydraulic events and be a major concern for citizens, authorities, insurance companies and government agencies. In the past twenty years, several studies have been conducted in ice jam modelling and forecasting, and it has been found that predicting ice jam formation and breakup is challenging, due to the complexity of the interactions between the hydroclimatic variables leading to these processes. At this time, several mathematical models have been developed to predict breakup processes. The current methods of breakup prediction are highly empirical and site-specific. The information on the progress of the methods and the variables used to predict the occurrence, severity, and timing of the breakup ice jams still remains limited. This study summarizes the different processes contributing to ice jam formation and breakup, the various existing ice jam prediction models, and their potential and limitations regarding the improvement in ice jam predictions. An overview of the application of artificial neural networks and fuzzy logic systems in ice-related problems is presented. Genetic programming is also explained as a possible mean for ice-related problems. Although genetic programming shows promising results in hydrological modelling, it has not yet been used in ice-related problems. The review of literature highlights that data-driven and machine learning techniques provide promising means in predicting ice jams with better confidence, but more scientific research is needed.
A physically-based modelling framework for operational forecasting of river ice breakup
2020, Advances in Water Resources
Forecasting river ice breakup is critical for supporting emergency responses to river ice-related flooding along rivers in the northern hemisphere. However, due to complex river ice processes, forecasting river ice breakup is more challenging than predicting open-water flood conditions. Although considerable progress has been made in understanding the mechanisms and characteristics of breakup processes and in forecasting breakup timing using empirical methods at the local scale, fewer advances have been made in understanding and forecasting breakup using physically-based models, particularly at the catchment scale. In this study, we present a physically-based coupled hydrological and water temperature modelling framework for breakup prediction in cold region catchments in real time. The modelling framework was applied for operational forecasting of the 2019 breakup event along the Athabasca River at Fort McMurray in Alberta. Further model validation was performed by hindcasting the 2016, 2017 and 2018 breakup events. The model shows promising results for predicting the ice cover breakup with an average error of about 5 days, demonstrating its usefulness in real-time operational forecasting. Importantly, the model generates breakup progression at the catchment scale, providing an advantage over existing site specific breakup prediction methods.

View all citing articles on Scopus

¹: Tel.: +1 780 427 0784; fax: +780 422 0262.

²: Tel.: +1 780 492 1205; fax: +1 780 492 0249.

View full text

Neuro-fuzzy river ice breakup forecasting system

Abstract

Introduction

Section snippets

Site description and previous models

Basic components of a fuzzy logic model

Variable selection

Prototype neuro-fuzzy model

Type of membership function

Conclusions

Acknowledgements

Information and Control

Fuzzy sets, possibilities and probabilities

Fuzzy regression in hydrology

Water Resources Research

Study of river ice breakup using hydrometric station records

Knowledge-elicitation study in construction scheduling domain

Journal of Computing in Civil Engineering

A fuzzy expert system for design performance prediction and evaluation

Canadian Journal of Civil Engineering

Forecasting ice jam risk at Fort McMurray, AB using fuzzy logic

Predicting ice jams with neural networks

Short-term flood forecasting with a neuro-fuzzy model

Water Resources Research