Recent advances in data-driven modeling of remote sensing applications in hydrology

Abstract

Artificial neural networks (ANNs) are very effective statistical models for (1) extracting significant features or characteristics from complex data structures and/or for (2) learning nonlinear relationships involved in any input-output mapping. Another interesting aspect of ANN modeling is the fact that overall performance of these models is not greatly hampered by the presence of error-corrupted values in some input nodes. ANNs have gained interest in remote sensing applications as valuable inverse models that can retrieve physical characteristics of interest, such as precipitation, from remote sensing measurements collected from radars or satellites. The spatial coverage and high resolution of remote sensing measurements relative to ground-based measurements can improve the hydrological modeling of the water cycle at both local and global scales. This review paper intends to present recent advances in artificial neural network modeling of remote sensing applications in hydrology. This paper focuses on precipitation and snow water equivalent (SWE) retrievals from remote sensing data.

Daily river flow forecasting using wavelet ANN hybrid models

“…The application of ANNs has been extended to their use in remote sensing data for retrieving hydrological variables such as precipitation at different scales, needed for stream flow forecasting (Evora & Coulibaly 2009). A comprehensive review of the application of ANNs in hydrology is presented in the ASCE Task Committee report (2000a,b).…”

Improving gridded snow water equivalent products in British Columbia, Canada: multi-source data fusion by neural network models

“…Binaghi et al (2013) applied radial basis function networks to estimate snow cover thickness in the Italian Central Alps, finding that this approach outperformed inverse distance weight and spline interpolation methods commonly used in similar contexts with limited numbers of homogeneously distributed measurement sites. These and other studies covered in various review papers (Gan, 1996;Evora and Coulibaly, 2009;Shi et al, 2016) demonstrate the promise of more accurate snow estimates via machine learning methods, but they do not incorporate existing SWE products directly.…”

Understanding and forecasting hypoxia using machine learning algorithms

“…Currently, machine learning algorithms are widely used for the purpose of hydrological modeling of remote sensing data using artificial neural networks (Evora & Coulibaly 2009), fuzzy inference systems for modeling river flow (Jacquin & Shamseldin 2009), and evolutionary polynomial regression to model soil moisture (Elshorbagy & El-Baroudy 2009).…”