IMPLEMENTATION OF AN ICE JAM PREDICTOR WITH USER INTERFACE

Transcription

1 Ice in the Environment: Proceedings of the 16th IAHR International Symposium on Ice Dunedin, New Zealand, 2nd 6th December 2002 International Association of Hydraulic Engineering and Research IMPLEMENTATION OF AN ICE JAM PREDICTOR WITH USER INTERFACE Regan P. McDonald 1, Kathleen D. White 2, Steven F. Daly 3 and Darrell D. Massie 4 ABSTRACT Break-up jams occur suddenly and often cause rapid increases in upstream water levels. Break-up ice jam prediction methods are desirable to provide early warning and allow effective mitigation. The lack of an analytical description of the complex physical processes involved in break-up ice jam formation has limited development of effective prediction models. Current methods include empirical, threshold-type models and statistical methods such as logistic regression and discriminant function analysis. A neural network method developed to predict break-up ice jams at Oil City, Pennsylvania proved more accurate than other methods previously attempted at this site. This paper will discuss the neural network input vector determination, including a watershed model of Oil Creek, and the methods used to appropriately account for the relatively low occurrence of jams. Discussion of how those vectors are estimated and how users of the predictor interface the software with a web-based package are presented. INTRODUCTION Break-up ice jams occur when a sudden rise in river stage resulting from precipitation, snowmelt, or both, causes an existing ice cover to break apart, be transported downstream and accumulate in the channel. The resultant flooding can occur rapidly and often causes significant damage. Break-up ice jams are difficult to predict because the complex interaction of the hydrologic, hydraulic and meteorological processes that cause them are not yet described fully by an analytical model. Current break-up ice jam prediction models range from empirical models to statistical methods, and no one methods has proven easily transferable to different locations. Oil City, Pennsylvania is the site of frequent damaging break-up ice jams. The existence 1 U.S. Army Corps of Engineers, Detroit District, 477 Michigan Ave., P.O. Box 1027, Detroit, MI 48231, USA 2 U.S. Army Engineer Research and Development Center, Cold Regions Research and Engineering Laboratory, 72 Lyme Rd., Hanover, NH 03755, USA 3 U.S. Army Engineer Research and Development Center, Cold Regions Research and Engineering Laboratory, 72 Lyme Rd., Hanover, NH 03755, USA 4 Department of Civil and Mechanical Engineering, U.S. Military Academy, West Point, NY 10996, USA

2 of ice records in addition to meteorological and hydrological records makes this an ideal location for examining ice jam prediction methods. Oil City lies at the confluence of Oil Creek and the Allegheny River in northwestern Pennsylvania (Figure 1), and is prone to flooding due to break-up ice jams for a number of reasons. First, the Oil Creek watershed, approximately 300 square miles (77,700 hectares), is relatively steep and responds rapidly to precipitation and/or snowmelt events. Second, Oil Creek flows into the Allegheny River just upstream from a deepened, slow-moving reach of river that usually forms a freezeup jam during early winter. The Allegheny River is slower to respond to precipitation and snowmelt than Oil Creek, thus the ice cover remains competent longer than the ice on Oil Creek. This competent ice cover, and particularly the freezeup accumulation downstream from the confluence, combine to block the movement of ice from Oil Creek into the Allegheny River, forming an ice jam (Deck and Gooch, 1981). When this combination of events occurs, Oil City experiences significant flooding and damage. Two ice control structures (ICS) exist in the vicinity of Oil City. The U.S. Army Corps of Engineers Pittsburgh District, in conjunction with the Engineer Research and Development Center's Cold Regions Research and Engineering Laboratory (CRREL), installed a floating ice boom on the Allegheny River in 1982 just upstream from the confluence of Oil Creek. The purpose of this structure is to reduce the occurrence of freeze-up jams on the Allegheny River near the mouth of Oil Creek. The second structure is a weir located on Oil Creek, 5.3 miles upstream from Oil City. This structure, completed in 1989, controls ice movement on Oil Creek. In the 67-year period of record, 17 ice jams have been recorded. Oil City, PA Figure 1: Map of Oil City, PA and vicinity.

3 Although a damaging break-up jam has not occurred since the construction of ice control structures, sufficient ice often exists downstream of the weir on Oil Creek to cause an ice jam. Because flooding and severe damage remain potential risks, White and Daly (2002) developed an Internet based prediction tool that has been used to predict ice jams at Oil City since This model, developed using discriminant function analysis, yields mean false negative and false positive errors of 15.6 and 8.3 percent, respectively. Because the false negative error is higher than preferred (i.e., an ice jam occurs when no jam is predicted), a prediction method with better accuracy is desired. Recently, an artificial intelligence method, the neural network, has been applied at Oil City, PA. This model demonstrated more accurate results in hindcasting break-up ice jam occurrence than existing empirical threshold and discriminant function analysis prediction methods for the same site (Massie et al., 2001, 2002; White and Daly, 2002). NEURAL NETWORKS Neural network analysis is an application of a type of artificial intelligence that is used to determine an outcome vector based a vector of input data. Provided with historical input and output data, a neural network can determine the relationship between variables that produce the given results. The goal for a neural network developed for classification problems such as differentiating between jam and no jam conditions is to assign new output variables to a number of discrete classes or categories. This is accomplished by mapping the relationship between an input vector and an output vector, where each output is given a class label. Neural networks are particularly well suited for these problems since they are easily configured to map several input variables to multiple output variables. Neural networks offer an advantage over empirical and statistical methods in that they may have non-linear or non-contiguous solution boundaries. Once a model has been trained using historical data, a test set of variables is input and the model results are compared to the actual historical values to determine model accuracy (Massie, 2001). Neural Network Prediction Model Development Development of the neural network prediction model for Oil City required variable selection, data pre-processing, selection of the training and testing data sets, and the actual training and testing of the neural network. Variable selection is of critical importance. This study utilized the same data set used by White and Daly (2002); 67 years of December through March average daily temperature at Franklin, PA and average daily discharge for Oil Creek and the Allegheny River for the years 1933 to After reviewing the data set for obvious errors and omitted data, an independent statistical analysis (Arnett, et al., 2001) confirmed that the most significant factors causing ice jams, as identified by White and Daly (2002), were an increase in accumulated freezing degree day (AFDD), and an increase in discharge in both Oil Creek and the Allegheny River. Variables selected for model testing and training are listed in Table 1. Output variables were jam or no-jam. Data preprocessing must be carefully carried out to assure that no single set of values will dominate the neural net solution in the training phase, thus decreasing the uncertainty in the testing phase. Several data transformations were performed, including normalization of variables to a mean of zero with unit standard deviation, and application of power functions and logarithms. Because pre-processing can significantly impact the final result, pre-processing, training, and testing were performed using an

4 iterative process. The most accurate transformation was selected for use in the final model. Table 1: Neural network input variables. Variable name (units) Average daily air temperature ( o F) Accumulated freezing degree days ( o F) 1 AFDD, 1 AFDD,, 15 AFDD ( o F) Allegheny River Discharge (cfs) Log 10 Allegheny River Discharge (cfs) Oil Creek Discharge (cfs) Log 10 Oil Creek Discharge (cfs) 1 day Oil Creek Discharge (cfs) Log 10 1 day Oil Creek Discharge (cfs) Because of the relatively few jam events, only 17 in 7,700 daily records, selection of the training set presented a challenge. With so few incidents of a jam, predicting none of them would still yield a very low, yet unacceptable, false negative error. Recognizing from the historical data that break-up jams occurred only when the average daily air temperature and flow in Oil Creek increased, 5,000 no-jam events that did not fit this general criteria were eliminated from the data set. Attempts to eliminate more data points via several clustering techniques proved unsuccessful. Fifty no-jam days were then selected at random and divided equally to create training and testing data sets. One third (6 of 17) of the jam days were included in the training set and the remaining eleven were incorporated into the testing set. Five of six jam events and 24 of 25 no-jam events were classified during the training phase. In the testing phase, the network correctly classified all eleven jam events and 23 of 25 no-jam events. The entire data set was then input into the trained neural network resulting in the correct classification of 94 % of jam events and 93 % of no-jam events. With false negative and false positive classifications of 6 % and 7 %, respectively, the results were significantly better than those employing empirical and statistical methods, as shown in Table 2. Table 2: Ice jam prediction results at Oil City. Error Type % False Positive Errors % False Negative Errors Empirical Method Discriminant Function Analysis Neural Network 40.0 % 8.3 % 5.9 % 11.8 % 15.6 % 7.4 % DETERMINING INPUT DATA FOR FORECASTING Application of the developed neural network model as a forward-looking ice jam prediction tool requires future knowledge of the input variables. Since meteorological forecasts are generally unreliable or unavailable beyond five days, a five-day period was

5 determined to be the reasonable limit to forecast future ice jams. While air temperature and precipitation forecasts are readily available, future discharges from Oil Creek are not. To forecast flows, a watershed model of the Oil Creek watershed was developed using the Watershed Modeling System (WMS, 1999). The model was used to quantify the response of Oil Creek to given precipitation and snowmelt events. Because WMS cannot be incorporated to run within the web-based interface at this time, the hydrographs generated by the watershed model were generalized for use in programming. Developing a watershed model to predict the flow in the Allegheny River was not necessary because the river is well gauged and dams operated by the U.S. Army Corps of Engineers Pittsburgh District largely control the flow. Reliable discharge forecasts for the Allegheny River are therefore readily available to the users of the ice jam prediction tool. Hydrologic Modeling of Oil Creek The watershed model of Oil Creek was based on a 1:250,000 scale Digital Elevation Model (DEM) and land use overlay obtained from the U.S. Geologic Survey (USGS), and soil type data from the U.S. Natural Resource Conservation Service. The Oil Creek watershed was delineated with the outlet at the confluence with the Allegheny River. The watershed was then subdivided into 14 sub-basins, with a node located at the USGS stream gage at Rouseville, PA, to facilitate model calibration. The Soil Conservation Service (SCS) Curve Number Method, operating in the Hydrologic Engineering Center's HEC-1 (HEC, 1990), was chosen as the hydrologic model. Channel routing was accomplished using the Muskingum method. Calibration of the model required significantly more detailed data to accurately depict the hydrologic response of Oil Creek to meteorological events than was readily available. Historical daily precipitation amounts are insufficient to calibrate the model because storm duration has a significant bearing on runoff amounts, and most storms were less than 24-hours in duration. Storm duration data was analyzed to determine the dominant weather pattern. A cumulative frequency distribution of precipitation durations revealed that over 80 % of storms lasted 6 hours or less. Events of one hour or less accounted for 25 % of all storms, but produced very little response in stream flow. For this reason, storms of 4 to 6 hours in duration, well isolated by antecedent dry periods, were used in model calibration. Further, snowmelt events are not directly captured in the historical record. Additional historical weather data was obtained from the Franklin, PA weather station containing hourly precipitation amounts for the same period of record as the stream flow and average daily temperature data. To account for the contribution of snowmelt to runoff, the historical daily snow pack was estimated by analysis of the average daily temperature and precipitation amount. The degree-day formula (HEC, 1990) was used to melt snow when the average daily temperature exceeded the freezing point. When snow is present and the average daily temperature is above freezing, the formula yields an estimate of the amount that melts on a given day by multiplying a melt coefficient by the difference in the average daily temperature and freezing. Depth of water runoff is then determined by estimating the water content of the snow pack. A rainfall hyetograph was constructed to provide a typical temporal distribution of rainfall over the basin. Several storms were selected from the historical record and

6 modeled in WMS. Sub-basin curve numbers were adjusted slightly so that model results converged to the historical records at the USGS gaging station at Rouseville. Several additional storms were then modeled as a test and the results were within 15 % of recorded values. The resulting hydrographs were summarized by selecting ordinates along the curve for use in programming. WEB BASED PREDICTION TOOL The neural network solution and the results of the hydrologic model were then melded into a web interface. This prediction tool is intended to be used by experts at the U.S. Army Corps of Engineers Pittsburgh District and the Cold Regions Research and Engineering Laboratory, and thus the web site is password-protected. The web interface (Figure 2) requires the user to enter average daily temperature, precipitation, and the Allegheny River discharge for the present day and five days into the future. Historical and season-to-date values of average daily temperature, precipitation, and Oil Creek and Allegheny River, along with accumulated freezing degree-days, which require a 15 day temperature history, are maintained in a database by the web-site administrator at CRREL. The database is automatically queried by the interface when needed. Current snow-pack over the watershed and current flow in Oil Creek is also required. Figure 2: Web interface input page. The input data are then preprocessed to generate all of the neural network input variables identified in Table 1. Temperature and precipitation data are analyzed to accumulate or melt snow pack. Free water from snowmelt is treated as precipitation for runoff determination. Abstractions are deducted from the total available water and the generalized hydrograph ordinates are applied to these quantities, similar to a unit

7 hydrograph, to estimate the flow in Oil Creek for a five-day period. Recognizing that the generalization of the watershed model is a potential shortcoming, the interface was modified to allow the user to directly input the five-day discharge forecasts for Oil Creek. The completed hydrologic model was provided to the users, greatly increasing the flexible use of the web-based predictor. When desired, the hydrologic model can be operated independently, and is therefore not constrained to only the most common weather patterns. This option also allows the user to refine snowmelt forecasts, or use data from future hydrologic models that may be developed. The input vectors are then fed into the neural network and a jam or no-jam prediction is made for each of the five days (Figure 3). Uncertainty in predictions increases as the time from the forecast date increases. Updating input data daily or hourly, particularly when weather conditions are changing rapidly, can help to refine the resulting predictions and minimize uncertainty. The user can also rapidly investigate the effects of different meteorological or hydrologic conditions on the jam outcome, an important capability for emergency managers. Figure 3: Web interface prediction page. CONCLUSION The web-based ice jam prediction tool is robust, user friendly, and delivers a more accurate prediction of break-up ice jams than other methods attempted at this site. The model can be used to quickly investigate various temperature, precipitation, and stream flow scenarios. While the neural network solution is still site specific, this approach shows promise for application at other sites where ice jam prediction is desirable and sufficient data is available.

8 REFERENCES Arnett, K., McBride, P. and Soofi, A. Ice Jam Prediction Model for Oil City, PA. Parsons-Brinkerhoff Environmental and Water Resource Student Design Competition. American Society of Civil Engineers, Orlando, Florida, USA (2001). Deck, D. and Gooch, G.E. Ice Jam Problems at Oil Creek, PA. Special Report U.S. Army Engineer Research and Engineering Laboratory, Hanover, New Hampshire, USA (1981). Environmental Modeling Research Laboratory. Watershed Modeling System (WMS). Brigham Young University, Provo, Utah, USA (1999). Hydrologic Engineering Center (HEC). HEC-1 Flood Hydrograph Package Users Manual. U.S. Army Corps of Engineers, Davis, California, USA (1990). Massie, D.D. Neural network fundamentals for scientists and engineer. In Proceedings, ECOS 2001, International Conference on Efficiency, Cost, Optimisation, Simulation, and Environmental Aspects of Energy and Process Systems, American Society of Mechanical Engineers, Istanbul, Turkey (2001). Massie, D.D., White, K.D., Daly, S.F. and McDonald, R.P. Predicting ice jams with neural networks. In Proceedings, OMAE2002, The 21 st International Conference on Offshore Mechanics and Arctic Engineering, Session 7.2, American Society of Mechanical Engineers, Olso, Norway (2002). Massie, D.D., White, K.D., Daly, S.F. and Soofi, A. Predicting ice jams with neural networks. In Proceedings, 11 th Workshop On River Ice, Committee on River Ice Processes and the Environment, Ottawa, Ontario, Canada (2001). Soil Conservation Service (SCS). National Engineering Handbook. U.S. Department of Agriculture, Washington, D.C., USA (1972). White, K.D. and Daly, S.F. Predicting ice jams with discriminant function analysis. In Proceedings, OMAE2002, The 21 st International Conference on Offshore Mechanics and Arctic Engineering, Session 7.2, American Society of Mechanical Engineers, Olso, Norway (2002).