scholarly journals Assessment of Surface Moisture in the Catchment Area on the Base of Modelling the Hydrological Properties of Soils

10.29007/7kmr ◽  
2018 ◽  
Author(s):  
Vitaly Ilinich ◽  
Andrey Bolotov ◽  
Sergey Makarychev ◽  
Evgeny Shein
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Soil Moisture ◽  
Nonlinear Regression ◽  
Catchment Area ◽  
Water Erosion ◽  
Surface Moisture ◽  
Model Based ◽  
Catchment Modeling ◽  
Artificial Neural

The research is dedicated to estimation of soil moisture before storm rain flood for calculations of water erosion on the catchment. Modeling of hydrological properties of soils is used for characteristic of the soil moisture. The model based at technologies of multiple nonlinear regression, as well as the method of artificial neural networks.

scholarly journals Modeling the contributions of global air temperature, synoptic-scale phenomena and soil moisture to near-surface static energy variability using artificial neural networks

2017 ◽  
Author(s):  
Sara C. Pryor ◽  
Ryan C. Sullivan ◽  
Justin T. Schoof
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Soil Moisture ◽  
Air Temperature ◽  
Energy Content ◽  
Daily Maximum ◽  
Synoptic Scale ◽  
Near Surface ◽  
Artificial Neural ◽  
Static Energy

Abstract. The static energy content of the atmosphere is increasing at the global scale, but exhibits important sub-global and sub-regional scales of variability and is a useful parameter for integrating the net effect of changes in the partitioning of energy at the surface and for improving understanding of the causes of so-called warming-holes (i.e. locations with decreasing daily maximum air temperatures (T) or increasing trends of lower magnitude than the global mean). Further, measures of the static energy content (herein the equivalent potential temperature, θe) are more strongly linked to excess human mortality and morbidity than air temperature alone, and have great relevance in understanding causes of past heat-related excess mortality and making projections of possible future events that are likely to be associated with negative human health and economic consequences. A new non-linear statistical model for summertime daily maximum and minimum θe is developed and used to advance understanding of drivers of historical change and variability over the eastern USA. It is shown that soil moisture (SM) is particularly important in determining the magnitude of θe over regions that have previously been identified as exhibiting warming holes confirming the key importance of SM in dictating the partitioning of net radiation into sensible and latent heat and dictating trends in near-surface T and θe. Consistent with our a priori expectations, models built using Artificial Neural Networks (ANN) out-perform linear models that do not permit interaction of the predictor variables (global T, synoptic-scale meteorological conditions and SM). This is particularly marked in regions with high variability in min- and max-θe, where more complex models built using ANN with multiple hidden layers are better able to capture the day-to-day variability in θe and the occurrence of extreme max-θe. Over the entire domain the ANN with 3 hidden layers exhibits high accuracy in predicting max-θe > 347 K. The median hit rate for max-θe > 347 K is > 0.60, while the median false alarm rate ≈ 0.08.

Soil moisture retrieval from Sentinel-1 acquisitions in an arid environment in Tunisia: application of Artificial Neural Networks techniques

2019 ◽  
Vol 40 (24) ◽  
pp. 9159-9180 ◽  
Author(s):  
A. Hachani ◽  
M. Ouessar ◽  
S. Paloscia ◽  
E. Santi ◽  
S. Pettinato
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Soil Moisture ◽  
Arid Environment ◽  
Artificial Neural

A novel hybrid model-based MPPT algorithm based on artificial neural networks for photovoltaic applications

2017 ◽  
Author(s):  
Mahyar Khosravi ◽  
Saeed Heshmatian ◽  
Davood A. Khaburi ◽  
Cristian Garcia ◽  
Jose Rodriguez
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Hybrid Model ◽  
Model Based ◽  
Photovoltaic Applications ◽  
Artificial Neural

Elevating Model Predictive Control Using Feedforward Artificial Neural Networks: A Review

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Senthil Kumar Arumugasamy ◽  
Zainal Ahmad
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Model Based Control ◽  
Model Based ◽  
Non Linear ◽  
Artificial Neural ◽  
Neural Predictive Control

Process control in the field of chemical engineering has always been a challenging task for the chemical engineers. Hence, the majority of processes found in the chemical industries are non-linear and in these cases the performance of the linear models can be inadequate. Recently a promising alternative modelling technique, artificial neural networks (ANNs), has found numerous applications in representing non-linear functional relationships between variables. A feedforward multi-layered neural network is a highly connected set of elementary non-linear neurons. Model-based control techniques were developed to obtain tighter control. Many model-based control schemes have been proposed to incorporate a process model into a control system. Among them, model predictive control (MPC) is the most common scheme. MPC is a general and mathematically feasible scheme to integrate our knowledge about a target, process controller design and operation, which allows flexible and efficient exploitation of our understanding of a target, and thus produces optimal performance of a system under various constraints. The need to handle some difficult control problems has led us to use ANN in MPC and has recently attracted a great deal of attention. The efficacy of the neural predictive control with the ability to perform comparably to the non linear neural network strategy in both set point tracking and disturbance rejection proves to have less computation expense for the neural predictive control. The neural network model predictive control (NNMPC) method has less perturbations and oscillations when dealing with noise as compared to the PI controllers.

Direct and indirect model based control using artificial neural networks

1991 ◽  
Vol 30 (12) ◽  
pp. 2564-2573 ◽  
Author(s):  
Dimitris C. Psichogios ◽  
Lyle H. Ungar
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Model Based Control ◽  
Model Based ◽  
Artificial Neural

A new global version of M(3000)F2 prediction model based on artificial neural networks

2014 ◽  
Vol 53 (3) ◽  
pp. 371-386 ◽  
Author(s):  
S.I. Oronsaye ◽  
L.A. McKinnell ◽  
J.B. Habarulema
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Model ◽  
Model Based ◽  
Artificial Neural
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