Artificial intelligence (AI) techniques have been successfully adopted in predictive modeling to capture the nonlinearity of natural systems. The high seasonal variability of rivers in cold weather regions poses a challenge to river flow forecasting, which tends to be complex and data demanding. This study proposes a novel technique to forecast flows that use a single-input sequential adaptive neuro-fuzzy inference system (ANFIS) along the Athabasca River in Alberta, Canada. After estimating the optimal lead time between four hydrometric stations, gauging data measured near the source were used to predict river flow near the mouth, over approximately 1000 km. The performance of this technique was compared to nonsequential and multi-input ANFISs, which use gauging data measured at each of the four hydrometric stations. The results show that a sequential ANFIS can accurately predict river flow (r2 = 0.99, Nash–Sutcliffe = 0.98) with a longer lead time (6 days) by using a single input, compared to nonsequential and multi-input ANFIS (2 days). This method provides accurate predictions over large distances, allowing for flow forecasts over longer periods of time. Therefore, governmental agencies and community planners could utilize this technique to improve flood prevention and planning, operations, maintenance, and the administration of water resource systems.
