Abstract
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.
Highlights
The modeling of large watersheds is challenging because of the complexity of hydroclimatic regimes due to intra- and inter-basin variations in topography, climatic patterns, land cover, basin drainage density, soil drainage capacity, and other associated factors
The results showed that the estimated flow of the Athabasca River at Fort McMurray, in terms of the Nash–Sutcliffe coefficient, is 0.72 for model validation
The results showed that the adaptive neuro-fuzzy inference system (ANFIS) model predicts daily flow more accurately compared to the artificial neural networks (ANN) and multiple nonlinear regression (MNLR) models
Summary
The modeling of large watersheds is challenging because of the complexity of hydroclimatic regimes due to intra- and inter-basin variations in topography, climatic patterns, land cover, basin drainage density, soil drainage capacity, and other associated factors. There is particular interest in understanding the variability in the Athabasca River flow, because it represents an important resource for oil and gas extraction and operational processes, as well as agricultural irrigation. Water 2020, 12, 1622 freshwater species and streamside ecosystems that contribute to the rich floodplain forests [2,3,4]. These changes may force alterations to water management regulations for multi-objective reservoirs [5,6,7]. It is important to understand the temporal and spatial variability of current and future hydrologic regimes to provide for sustainable water resource management and monitoring programs
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