Mapping salt diapirs and salt diapir-affected areas using MLP neural network model and ASTER data

2013 ◽  
Vol 6 (2) ◽  
pp. 143-157 ◽  
Author(s):  
Mohammad H. Tayebi ◽  
Majid H. Tangestani ◽  
Hasan Roosta
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Salt Diapir ◽  
Mlp Neural Network ◽  
Aster Data

scholarly journals Multi-layer perceptron based neural network model predicting maximum severity of Spodoptera litura (Fabricius) on groundnut in relation to climate for Dharwad region of Karnataka (India)

MAUSAM ◽  
2021 ◽  
Vol 68 (3) ◽  
pp. 537-542
Author(s):  
GIRISH K. JHA ◽  
GAJAB SINGH ◽  
S. VENNILA ◽  
M. HEGDE ◽  
M. S. RAO ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Spodoptera Litura ◽  
Weather Data ◽  
Maximum Temperature ◽  
Percentage Error ◽  
Multi Layer Perceptron ◽  
Moth Population ◽  
Mlp Neural Network

A multi-layer perceptron (MLP) neural network model for predicting adult moth population of tobacco caterpillar (Spodoptera litura (Fabricius) in groundnut cropping system of Dharwad (Karnataka) was developed and tested using the long term (24 years : 1990-2013) trap catches of the pest and weather data of Kharif season [26 to 44 standard meteorological weeks (SMW)]. The weekly male moth catches of S. litura during maximum severity observed at 34 SMW was modelled using the weather parameters viz., maximum temperature (C), minimum temperature (°C), rainfall (mm) and morning and afternoon relative humidity (%) lagged by two weeks. The principle component analysis was performed using meteorological data of preceding two weeks (32 and 33 SMW) in order to create fewer linearly independent factors. Five principal component scores which together accounted for 90 per cent of variations in data were used as input variables for neural network model. A MLP neural network with five input nodes and one hidden layer consisting of eleven hidden nodes was found to be suitable in terms of adequacy measures for modelling the population dynamics of S. litura. While data sets of 1990-2009 were used for developing the model, data of four seasons (2010-2013) were used for testing and validation. The performance of the model was assessed in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE). The validation results clearly showed that the neural network based model is effective in dealing with the apparently random behaviour of the S. litura dynamics on groundnut.

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