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The main objective of this paper is to describe the characteristics of brash ice in the St. Lawrence River downstream of Montréal over a period of three winters. We used two instruments deployed in the St. Lawrence River navigation channel through Lake St. Pierre to measure ice parameters: an acoustic Doppler current profiler (ADCP) and an ice-profiling sonar (IPS). This paper discusses the capacities of these instruments to quantify ice characteristics and to predict the risk of ice congestion. It was found that wind velocity and air temperature play major roles in the variation in ice parameters and, consequently, in the occurrence of ice congestion in the navigation channel through Lake St. Pierre. Comparison of the IPS and ADCP data showed good agreement and demonstrated that these two instruments can be very effective for certain ice applications.Key words: ice characteristics, ice congestion, ADCP, IPS, fuzzy logic.

This paper evaluates the potential of using artificial neural networks to model ice parameters related to ice jams in the St. Lawrence River navigation channel through Lake St. Pierre. The artificial neural networks mapped environmental conditions onto ice parameters through multilayer feed-forward networks. The ice parameters include velocity, thickness, concentration, and unit discharge. The input to the network is based on two meteorological parameters: wind velocity and air temperature. The Levenberg–Marquardt algorithm with Bayesian regularization is used to train the feed-forward network. The artificial neural networks adequately modelled the ice parameters. The predicted ice velocity, thickness, and unit discharge were very satisfactory, but ice concentration was not. Methods to improve forecasting (particularly of ice concentration) are suggested.Key words: ice parameters, ice jam, artificial neural network, ADCP, IPS.

An Adaptive Neuro-Fuzzy Inference System, based on a jack-knife approach, is proposed for the post-calibration of weather radar rainfall estimation exploiting available raingauge observations. The methodology relies on the construction of a fuzzy inference system with three inputs (radar x coordinate, y coordinate and rainfall estimation at raingauge locations) and one output (raingauge observations). Subtractive clustering is used to generate the initial fuzzy inference system. Artificial neural network learning provides a fast way to automatically generate additional fuzzy rules and membership functions for the fuzzy inference system. Fuzzy logic enhances the generalisation of the artificial neural network system. In order to demonstrate the steps of the radar rainfall post-calibration using the Adaptive Neuro-Fuzzy Inference System, CAPPIs of one-hour rainfall accumulation and corresponding raingauge observations have been selected. Results show that the proposed approach looks for a response that is a compromise between radar rainfall estimations and raingauge observations and does not necessarily consider the raingauge observations as ground truth. The algorithm is very fast and can be implemented for real time post-calibration. This algorithm makes use of all available data—raingauge observations are usually scarce—for training and checking the neuro-fuzzy inference system. It also provides a degree of reliability of the post-calibration.