Abstract Water security and urban flooding have become major sustainability issues. This paper presents a novel method to introduce rates of change as the state-of-the-art approach in artificial intelligence model development for agenda. Multi-layer perceptron (MLP) deep learning long short-term memory (LSTM) models were considered flood forecasting. Historical rainfall data from 2008 2021 at 1...

Recent studies show that multimodel combinations improve hydroclimatic predictions by reducing model uncertainty. Given that climate forecasts are available from multiple climate models, which could be ingested with multiple watershed models, what is the best strategy to reduce the uncertainty in streamflow forecasts? To address this question, we consider three possible strategies: (1) reduce t...

This paper describes a methodology, based on dynamical systems theory, to model and predict streamflow at the daily scale. The model is constructed by developing a multidimensional phase-space map from observed streamflow signals. Predictions are made by examining trajectories on the reconstructed phase space. Prediction accuracy is used as a diagnostic tool to characterize the nature, which ra...

The water balance equation dictates that streamflow may be reduced by transpiration. Yet temporal disequilibrium weakens the relationship between transpiration and streamflow in many cases where inputs and outputs are unbalanced. We address two critical knowledge barriers in ecohydrology with respect to time, scale dependence and lags. Study objectives were to correlate components of the water ...