scholarly journals Wheat yield prediction based on weather parameters using multiple linear, neural network and penalised regression models

2022 ◽  
Vol 24 (1) ◽  
Author(s):  
K. S. ARAVIND ◽  
ANANTA VASHISTH ◽  
P. KRISHANAN ◽  
B.DAS
Keyword(s):  
Neural Network ◽  
Wheat Yield ◽  
Principal Component ◽  
Cost Effective ◽  
Elastic Net ◽  
Weather Data ◽  
Yield Data ◽  
Growing Period ◽  
Weather Parameters ◽  
Linear Neural Network

Wheat yield production is largely attributed by weather parameters. Model developed by multiple linear, neural network and penalised regression techniques using weather data have the potential to provide reliable, timely and cost-effective prediction of wheat yield. Wheat yield data and weather parameter during crop growing period (46th to 15th SMW) for more than 30 years were collected for study area and model was developed using stepwise multiple linear regression (SMLR), principal component analysis (PCA) in combination with SMLR, artificial neural network (ANN) alone and in combination with PCA, least absolute shrinkage and selection operator (LASSO) and elastic net (ENET) techniques.  Analysis was carried out by fixing 70% of the data for calibration and remaining dataset for validation. On examining these models, LASSO and elastic net are performing excellent having nRMSE value less than 10 % for four out of five location and good for one location, because of prevention in over fitting and reducing regression coefficient by penalization.

Applied-Information Technology in Power System with Short-Term Load Forecasting Based on SPSS and BP Neural Network

2014 ◽  
Vol 1022 ◽  
pp. 241-244 ◽  
Author(s):  
Jian Ping Chen ◽  
Chang Hao Xia ◽  
Zhi Peng Tian
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Predictive Accuracy ◽  
Load Forecasting ◽  
Influence Factors ◽  
Principal Component ◽  
Weather Data ◽  
Power Load ◽  
Bp Neural Network Model ◽  
Bp Model

In the study of power load forecasting, the factors influencing power load have data redundancy and data nonlinearity. The traditional load forecasting methods can’t eliminate redundant or nonlinear law between data, which result in reduced accuracy. In order to improve the predictive accuracy of power load, a prediction model based on BP neural network and SPSS (SPSS-BP) is established. The paper first analyzes the correlation and principal component of influence factors of electric power load, which eliminates the redundancy between various factors, accelerates the speed of BP neural network forecasting and improves predictive accuracy; then model the processed data and forecast through the BP neural network model. One-month weather data and load data of Yichang city have been confirmatory tested and analyzed through application of SPSS-BP model. The results show that SPSS-BP model significantly improves the accuracy, verify the feasibility and effectiveness of the model.

Monitoring effluent quality in hypertrophic wastewater reservoirs using remote sensing

1996 ◽  
Vol 33 (8) ◽  
pp. 23-29 ◽  
Author(s):  
I. Dor ◽  
N. Ben-Yosef
Keyword(s):  
Water Quality ◽  
Theoretical Model ◽  
Principal Component ◽  
Cost Effective ◽  
Operational Parameters ◽  
Effluent Quality ◽  
Optical Images ◽  
Routine Monitoring ◽  
Cost Effective Method ◽  
Wastewater Quality

About one hundred and fifty wastewater reservoirs store effluents for irrigation in Israel. Effluent qualities differ according to the inflowing wastewater quality, the degree of pretreatment and the operational parameters. Certain aspects of water quality like concentration of organic matter, suspended solids and chlorophyll are significantly correlated with the water column transparency and colour. Accordingly optical images of the reservoirs obtained from the SPOT satellite demonstrate pronounced differences correlated with the water quality. The analysis of satellite multispectral images is based on a theoretical model. The model calculates, using the radiation transfer equation, the volume reflectance of the water body. Satellite images of 99 reservoirs were analyzed in the chromacity space in order to classify them according to water quality. Principal Component Analysis backed by the theoretical model increases the method sensitivity. Further elaboration of this approach will lead to the establishment of a time and cost effective method for the routine monitoring of these hypertrophic wastewater reservoirs.

scholarly journals Performance Evaluation of Neural Network-Based Short-Term Solar Irradiation Forecasts

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3030
Author(s):  
Simon Liebermann ◽  
Jung-Sup Um ◽  
YoungSeok Hwang ◽  
Stephan Schlüter
Keyword(s):  
Neural Network ◽  
Goodness Of Fit ◽  
Renewable Energy Sources ◽  
Weather Conditions ◽  
Solar Irradiation ◽  
Weather Data ◽  
Short Term ◽  
Climate Conditions ◽  
Lstm Network ◽  
The Time Domain

Due to the globally increasing share of renewable energy sources like wind and solar power, precise forecasts for weather data are becoming more and more important. To compute such forecasts numerous authors apply neural networks (NN), whereby models became ever more complex recently. Using solar irradiation as an example, we verify if this additional complexity is required in terms of forecasting precision. Different NN models, namely the long-short term (LSTM) neural network, a convolutional neural network (CNN), and combinations of both are benchmarked against each other. The naive forecast is included as a baseline. Various locations across Europe are tested to analyze the models’ performance under different climate conditions. Forecasts up to 24 h in advance are generated and compared using different goodness of fit (GoF) measures. Besides, errors are analyzed in the time domain. As expected, the error of all models increases with rising forecasting horizon. Over all test stations it shows that combining an LSTM network with a CNN yields the best performance. However, regarding the chosen GoF measures, differences to the alternative approaches are fairly small. The hybrid model’s advantage lies not in the improved GoF but in its versatility: contrary to an LSTM or a CNN, it produces good results under all tested weather conditions.

scholarly journals Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1486
Author(s):  
Chris Cavalaris ◽  
Sofia Megoudi ◽  
Maria Maxouri ◽  
Konstantinos Anatolitis ◽  
Marios Sifakis ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Model Development ◽  
Wheat Yield ◽  
Growth Period ◽  
Yield Data ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Sentinel 2

In this study, a modelling approach for the estimation/prediction of wheat yield based on Sentinel-2 data is presented. Model development was accomplished through a two-step process: firstly, the capacity of Sentinel-2 vegetation indices (VIs) to follow plant ecophysiological parameters was established through measurements in a pilot field and secondly, the results of the first step were extended/evaluated in 31 fields, during two growing periods, to increase the applicability range and robustness of the models. Modelling results were examined against yield data collected by a combine harvester equipped with a yield-monitoring system. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were examined as plant signals and combined with Normalized Difference Water Index (NDWI) and/or Normalized Multiband Drought Index (NMDI) during the growth period or before sowing, as water and soil signals, respectively. The best performing model involved the EVI integral for the 20 April–31 May period as a plant signal and NMDI on 29 April and before sowing as water and soil signals, respectively (R2 = 0.629, RMSE = 538). However, model versions with a single date and maximum seasonal VIs values as a plant signal, performed almost equally well. Since the maximum seasonal VIs values occurred during the last ten days of April, these model versions are suitable for yield prediction.

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