The error cone is a display produced by the National Hurricane Center in order to present its predictions of the path of a hurricane. While the error cone is one of the primary tools used by officials, and the general public, to make emergency response decisions, the uncertainty underlying this display can be easily misunderstood. This paper explores the design of an alternate display that provides a continually updated set of possible hurricane tracks, whose ensemble distribution closely matches the underlying statistics of a hurricane prediction. We explain the underlying algorithm and data structures, and demonstrate how our displays compare with the error cone. Finally, we review the design and results of a user study that we conducted as a preliminary test of the efficacy of our approach in communicating prediction uncertainty.

Water resources management is a complex task. It requires accurate prediction of inflow to reservoirs for the optimal management of surface resources, especially in arid and semi-arid regions. It is in particular complicated by droughts. Markov chain models have provided valuable information on drought or moisture conditions. A complementary method, however, is required that can both evaluate the accuracy of the Markov chain models for predicted drought conditions, and forecast the values for ensuing months. To that end, this study draws on Artificial Neural Networks (ANNs) as a data-driven model. The employed ANNs were trained and tested by means of a statistically-based input selection procedure to accurately predict reservoir inflow and consequently drought conditions. Thirty three years' data of inflow volume on a monthly time resolution were selected to enable calculation of the standardized streamflow index (SSI) for the Markov chain model. Availability of hydro-climatic data from the Doroodzan reservoir in the Fars province, Iran, allowed us to develop a reservoir specific ANN model. Results demonstrated that both models accurately predicted drought conditions, by employing a randomization procedure that facilitated the selection of the required data for the ANN to forecast reservoir inflow close to the observed values over a validation period. The results confirmed that combining the two models improved short-term prediction reliability. This was in contrast to single model applications that resulted into substantial uncertainty. This research emphasized the importance of the correct selection of data or data mining, prior to entering a specific modeling routine.

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This research follows on from diverse international efforts to safeguard one of the largest natural lakes in the world, Urmia lake in North West Iran. In this research two new numerical packages based on Artificial Neural Networks (ANN) and the Least Square Support Vector Machine (LS-SVM) models were developed to estimate monthly Total Dissolved Solid (TDS) in the Aji Chay River, one the main tributaries of Urmia lake, Iran. A feed forward back propagation (FFB) model was used to obtain a set of coefficients for a linear model, and the radial basis function (RBF) kernel was employed for the LS-SVM model. The input data sets of both the ANN and LS-SVM models consists of six water quality parameters: TDS, Mg2+, Na+, Ca2+, Cl-, and SO4 2-, all collected on a monthly time scale over a period of 30 years from the Vanyar and Zarnagh stations, in the Aji Chay watershed. The research demonstrated that both models can effectively predict the variability of TDS, but for the Vanyar station with the ANN model (giving an R2 value of 0.913 and RMSE of 0.0032, a Nash-Sutcliffe Efficiency (NSE) coefficient 0.812 and as such has a more efficient and accurate estimation when compared to the LS-SVM model with R2=0.871 and RMSE =0.097 and NSE=0.86. The analysis of Zarnagh station data shows R2=0.853 and RMSE=0.0162, NSE= 0.854 for SVM and R2=0.903 and RMSE =0.0091 and NSE=0.85 for ANN.