Since artificial neural networks (ANNs) can approximate any function, they have been applied in many fields including hydrology. In hydrology, there are important issues such as flood estimation and predicting rainfall-runoff in a certain area. In this presentation, we briefly introduce a popular feed-forward neural network model, so called “multi-layer perceptron (MLP)”, and review its applica...

geographic information and analysis provide a wide range of data and techniques to monitor and manage natural resources. as an important case, in arid and semi-arid areas, water management is critical for both local governance and citizens. as a result, the estimation of water potential brought by snowmelt runoff and rainfalls seems to be very useful and important for these areas. hydrological ...

Abstract. Deep learning is becoming an increasingly important way to produce accurate hydrological predictions across a wide range of spatial and temporal scales. Uncertainty estimations are critical for actionable prediction, while standardized community benchmarks part model development research, similar tools benchmarking uncertainty estimation lacking. This contribution demonstrates that ca...

There have been several proposals to evaluate potential runoff in center pivot irrigation, through the integration of time varying infiltration–precipitation rate curves, involving complex iterative procedures. Some methods use empirical infiltration functions, such as the Kostiakov equation. Others use physically based infiltration functions, such as the Green–Ampt equation. Another option is ...