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.

scholarly journals Dynamic versus static neural network model for rainfall forecasting at Klang River Basin, Malaysia

2012 ◽  
Vol 16 (4) ◽  
pp. 1151-1169 ◽  
Author(s):  
A. El-Shafie ◽  
A. Noureldin ◽  
M. Taha ◽  
A. Hussain ◽  
M. Mukhlisin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Network Architecture ◽  
Rainfall Time Series ◽  
Hydrological Process ◽  
Multi Layer Perceptron ◽  
Rainfall Forecasting ◽  
Unseen Data

Abstract. Rainfall is considered as one of the major components of the hydrological process; it takes significant part in evaluating drought and flooding events. Therefore, it is important to have an accurate model for rainfall forecasting. Recently, several data-driven modeling approaches have been investigated to perform such forecasting tasks as multi-layer perceptron neural networks (MLP-NN). In fact, the rainfall time series modeling involves an important temporal dimension. On the other hand, the classical MLP-NN is a static and has a memoryless network architecture that is effective for complex nonlinear static mapping. This research focuses on investigating the potential of introducing a neural network that could address the temporal relationships of the rainfall series. Two different static neural networks and one dynamic neural network, namely the multi-layer perceptron neural network (MLP-NN), radial basis function neural network (RBFNN) and input delay neural network (IDNN), respectively, have been examined in this study. Those models had been developed for the two time horizons for monthly and weekly rainfall forecasting at Klang River, Malaysia. Data collected over 12 yr (1997–2008) on a weekly basis and 22 yr (1987–2008) on a monthly basis were used to develop and examine the performance of the proposed models. Comprehensive comparison analyses were carried out to evaluate the performance of the proposed static and dynamic neural networks. Results showed that the MLP-NN neural network model is able to follow trends of the actual rainfall, however, not very accurately. RBFNN model achieved better accuracy than the MLP-NN model. Moreover, the forecasting accuracy of the IDNN model was better than that of static network during both training and testing stages, which proves a consistent level of accuracy with seen and unseen data.

scholarly journals A neural network model for prediction of deoxynivalenol content in wheat grain based on weather data and preceding crop

2008 ◽  
Vol 53 (No. 10) ◽  
pp. 421-429 ◽  
Author(s):  
K. Klem ◽  
M. Váňová ◽  
J. Hajšlová ◽  
K. Lancová ◽  
M. Sehnalová
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Field Experiments ◽  
Weather Conditions ◽  
Weather Data ◽  
Continuous Variables ◽  
Wheat Grain ◽  
Preceding Crop ◽  
Input Variables

Deoxynivalenol (DON) is the most prevalent Fusarium toxin in Czech wheat samples and therefore forecasting this mycotoxin is a potentially useful tool to prevent it from entering into food chain. The data about DON content in wheat grain, weather conditions during the growing season and cultivation practices from two field experiments conducted in 2002–2005 were used for the development of neural network model designed for DON content prediction. The winning neural network is based on five input variables: a categorial variable – preceding crop, and continuous variables – average April temperature, sum of April precipitation, average temperature 5 days prior to anthesis, sum of precipitation 5 days prior to anthesis. The most important input parameters are the preceding crop and sum of precipitation 5 days prior to anthesis. The weather conditions in April, which are important for inoculum formation on crop debris are also of important contribution to the model. The weather conditions during May and 5 days after anthesis play only an insignificant role for the DON content in grain. The effect of soil cultivation was found inferior for model function as well. The correlation between observed and predicted data using the neural network model reached the coefficient <i>R</i><sup>2</sup> = 0.87.

scholarly journals A Short-Term Load Forecasting Method Based on GRU-CNN Hybrid Neural Network Model

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Lizhen Wu ◽  
Chun Kong ◽  
Xiaohong Hao ◽  
Wei Chen
Keyword(s):  
Neural Network ◽  
Power System ◽  
Network Model ◽  
Neural Network Model ◽  
Feature Vector ◽  
Load Forecasting ◽  
Percentage Error ◽  
Short Term ◽  
Hybrid Neural Network ◽  
Short Term Load Forecasting

Short-term load forecasting (STLF) plays a very important role in improving the economy and stability of the power system operation. With the smart meters and smart sensors widely deployed in the power system, a large amount of data was generated but not fully utilized, these data are complex and diverse, and most of the STLF methods cannot well handle such a huge, complex, and diverse data. For better accuracy of STLF, a GRU-CNN hybrid neural network model which combines the gated recurrent unit (GRU) and convolutional neural networks (CNN) was proposed; the feature vector of time sequence data is extracted by the GRU module, and the feature vector of other high-dimensional data is extracted by the CNN module. The proposed model was tested in a real-world experiment, and the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the GRU-CNN model are the lowest among BPNN, GRU, and CNN forecasting methods; the proposed GRU-CNN model can more fully use data and achieve more accurate short-term load forecasting.

scholarly journals The Effect of Different Cover and Flooring Materials on Climatic Comfort in Landscape Design

2022 ◽  
Vol 9 (sp) ◽  
pp. 2571-2580
Author(s):  
Orhun Soydan ◽  
Ahmet Benliay
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Temperature Difference ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Ambient Air ◽  
Maximum Temperature ◽  
Wide Range ◽  
Artificial Neural

In this study, it is aimed to understand the effects of structural and vegetative elements that can be used in landscape designs on the temperature factor, which will greatly affect the climatic comfort, by using artificial neural networks. In this context, measurements were carried out in the morning (08:00-09:00), noon (13:00-14:00) and evening (17:00-18:00) of a total of 100 days, 50 days in each of the winter and summer seasons, at 7 randomly selected points in the Akdeniz University Campus. In these measurements, the temperature difference values of 11 cover elements on 7 different floor covering types were measured, and the ambient air temperature, humidity and wind values were also determined. The temperature differences between the areas where the flooring elements are exposed to direct sun and the shadow effect of different plant and cover elements were determined using an infrared laser thermometer. These values were processed with Neural Designer software and possible temperature difference prediction values were created for 57.750 different alternatives with the help of artificial neural network model from 837 sets of data. Evaluation shows that the maximum temperature difference is 15.6°C at noon in the summer months in the red tartan flooring material and Callistemon viminalis cover material. While the artificial neural network model predicts that there will be a high 2-3° C temperature difference for the alternatives, it has made predictions for temperature differences between 0-10°C in winter and 0-16°C in summer months. Although the temperature differences that will occur in the noon hours are distributed over a wide range of values, it seems that the morning and evening forecasts are concentrated between 0-7°C values. Also, it has been determined that the wind and humidity in the environment are more important factors than the ambient temperature in terms of temperature differences.

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