Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

This section is composed of three basic subareas: combined sewer overflows (CSOs), sanitary sewer overflows (SSOs), and stormwater discharges. Much of the literature cited came from documents covering noteworthy global conferences (Bathala, 1996; Engineering Foundation, 1996; Hallam et al., 1996; Maxwell et al., 1996; Sieker and Verworn [Eds.], 1996; Society of Environmental Toxicology and Chemistry, 1996; U.S. EPA 1996a; Water Environment Federation, 1996a,b,c). In addition, the U.S. Environmental Protection Agency (U.S. EPA) published guidance documents (U.S. EPA, 1996,c,d,e), which are discussed in more detail in the subsection Regulatory Policies and Financial Aspects.

https://www.jstor.org/stable/pdfplus/29763265.pdf

Urban wet-weather flows

As evaporation of water is an energy-demanding process, increasing evapotranspiration rates decrease the surface temperature (Ts) of leaves and plants. Based on this principle, ground-based thermal remote sensing has become one of the most important methods for estimating evapotranspiration and drought stress and for irrigation. This paper reviews its application in agriculture. The review consists of four parts. First, the basics of thermal remote sensing are briefly reviewed. Second, the theoretical relation between Ts and the sensible and latent heat flux is elaborated. A modelling approach was used to evaluate the effect of weather conditions and leaf or vegetation properties on leaf and canopy temperature. Ts increases with increasing air temperature and incoming radiation and with decreasing wind speed and relative humidity. At the leaf level, the leaf angle and leaf dimension have a large influence on Ts; at the vegetation level, Ts is strongly impacted by the roughness length; hence, by canopy height and structure. In the third part, an overview of the different ground-based thermal remote sensing techniques and approaches used to estimate drought stress or evapotranspiration in agriculture is provided. Among other methods, stress time, stress degree day, crop water stress index (CWSI), and stomatal conductance index are discussed. The theoretical models are used to evaluate the performance and sensitivity of the most important methods, corroborating the literature data. In the fourth and final part, a critical view on the future and remaining challenges of ground-based thermal remote sensing is presented.

/pdf/estimating-evapotranspiration-and-drought-stress-with-ground-1jm9mozuoj.pdf

Estimating evapotranspiration and drought stress with ground-based thermal remote sensing in agriculture: A review

https://quantalab.ias.csic.es/pdf/paper_berni_RSE_2009.pdf

Mapping canopy conductance and CWSI in olive orchards using high resolution thermal remote sensing imagery

Assessment of input variables determination on the SVM model performance using PCA, Gamma test, and forward selection techniques for monthly stream flow prediction

Longitudinal dispersion coefficients in natural channels

Hydro-environmental modelling for bathing water compliance of an estuarine basin

Modelling the fate of faecal indicators in a coastal basin

Relationships between leaf water potential, CWSI, yield and fruit quality of sweet lime under drip irrigation

In this study artificial neural networks (ANNs) have been applied to predict faecal coliform concentration levels at compliance points along bathing water zones situated in the south west of Scotland, UK. Hydrological parameters, such as river discharges, sunshine, rainfall and tidal conditions, were used as the input data for these networks. Data collected at seven locations during the period 1990–2000 were used to train and verify the neural networks. A novel technique called the gamma test was used for data analysis to aid in the construction of ANN models. In general, the river discharges and tidal range were found to be the most important variables affecting the level of bacteria concentration at the compliance points. For compliance points close to the meteorological station, the amount of rainfall was found to be relatively significant in the model results. Relatively good correlation coefficients were obtained for the learning and verifying process for all of the ANNs and these networks confirmed ...

Seyed Mahmood Kashefipour

Papers

Longitudinal dispersion coefficients in natural channels

Hydro-environmental modelling for bathing water compliance of an estuarine basin

Modelling the fate of faecal indicators in a coastal basin

Relationships between leaf water potential, CWSI, yield and fruit quality of sweet lime under drip irrigation

Neural networks for predicting seawater bacterial levels