The Estimation of Reference Evapotranspiration based on Gamma Test and Gene Expression Programming Using the Weather Data Set from Different Climatic Zones in China

2015 ◽  
Keyword(s):  
Gene Expression ◽  
Reference Evapotranspiration ◽  
Gene Expression Programming ◽  
Weather Data ◽  
Gamma Test ◽  
Data Set ◽  
Climatic Zones

scholarly journals Evapotranspiration data to determine agro-climatic zones in Egypt

2017 ◽  
Vol 32 (1) ◽  
pp. 79-86 ◽  
Author(s):  
Samiha A. H. Ouda ◽  
Tahany A. Norledin
Keyword(s):  
Nile Delta ◽  
Reference Evapotranspiration ◽  
Irrigation Scheduling ◽  
Weather Data ◽  
Climatic Zones ◽  
Average Value ◽  
Scheduling Model ◽  
Significant Difference ◽  
Difference Test ◽  
Climatic Zoning

Abstract The objective of this paper was to compare between agro-climatic zones developed from 10-year interval of weather data from 2005-2014, 20-year interval of weather data from 1995-2014 and the zoning developed by [NORELDIN et al. 2016] using 30-year interval from 1985-2014 in the old cultivated land of Egypt in the Nile Delta and Valley. Monthly means of weather data were calculated for each year, and then monthly values for 10-year and 20-years were calculated for each governorate. Basic Irrigation scheduling model (BISm) was used to calculate reference evapotranspiration (ETo). Analysis of variance was used and the means was separated and ranked using least significant difference test (LSD0.05). Our results showed that agro-climatic zoning using 20-year values of ETo was similar to the zones developed with 30-year values of ETo, with different values of average ETo in each zone. Furthermore, using 10-year values of ETo resulted in higher values of ETo in each zone, compared to 20-year and 30-year ETo values. However, the average value of ETo over the three classifications was close to each other. Thus, depending on the availability of weather data, either zoning can be sufficient to develop agro-climatic zones.

scholarly journals Prediction of the depth of local scouring at a bridge pier using a gene expression programming method

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Waqed H. Hassan ◽  
Halah K. Jalal
Keyword(s):  
Gene Expression ◽  
Mean Squared Error ◽  
Human Life ◽  
Gene Expression Programming ◽  
Absolute Error ◽  
Width Ratio ◽  
Statistical Parameters ◽  
Data Set ◽  
Total Data ◽  
Local Depth

AbstractLocal scouring around the piers of a bridge is the one of the major reasons for bridge failure, potentially resulting in heavy losses in terms of both the economy and human life. Prediction of accurate depth of local scouring is a difficult task due to the many factors that contribute to this process, however. The main aim of this study is thus to offer a new formula for the prediction the local depth of scouring around the pier of a bridge using a modern fine computing modelling technique known as gene expression programming (GEP), with data obtained from numerical simulations used to compare GEP performance with that of a standard non-linear regression (NLR) model. The best technique for prediction of the local scouring depth is then determined based on three statistical parameters: the determination coefficient (R2), mean absolute error (MAE), and root mean squared error (RMSE). A total data set of 243 measurements, obtained by numerical simulation in Flow-3D, for intensity of flow, ratio of pier width, ratio of flow depth, pier Froude number, and pier shape factor is divided into training and validation (testing) datasets to achieve this. The results suggest that the formula from the GEP model provides better performance for predicting the local depth of scouring as compared with conventional regression with the NLR model, with R2 = 0.901, MAE = 0.111, and RMSE = 0.142. The sensitivity analysis results further suggest that the ratio of the depth of flow has the greatest impact on the prediction of local scour depth as compared to the other input parameters. The formula obtained from the GEP model gives the best predictor of depth of scouring, and, in addition, GEP offers the special feature of providing both explicit and compressed arithmetical terms to allow calculation of such depth of scouring.

scholarly journals A simple formulation for early-stage cost estimation of building construction projects

2021 ◽  
Vol 73 (08) ◽  
pp. 819-832
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Construction Projects ◽  
Early Stage ◽  
Gene Expression Programming ◽  
Cost Calculation ◽  
Data Set ◽  
Algorithm Configuration ◽  
Artificial Neural

This study is aimed at improving a formula that enables easy, correct, and fast estimation of an Early-Stage Cost of Buildings (ESCE). This formula, enabling estimation of ESCE, was developed by the authors based on artificial neural networks and gene expression programming. A quantity survey was conducted for a hundred construction projects, and a data set was created. This data set was analysed with many Artificial Neural Networks to determine the variables that affect ESCE. An algorithm configuration was made with Gene Expression Programming, and the ESCE formula was created using this algorithm configuration. This formula estimates ESCE with satisfactory precision. The use of the proposed formula in the early-stage building cost calculations is important not only for faster and easier cost calculation but also to prevent any differences that may arise due to the individual making the calculations.

scholarly journals COMPRESSIVE STRENGTH PREDICTION OF LIGHTWEIGHT SHORT COLUMNS AT ELEVATED TEMPERATURE USING GENE EXPRESSION PROGRAMING AND ARTIFICIAL NEURAL NETWORK

2020 ◽  
Vol 26 (2) ◽  
pp. 189-199
Author(s):  
Ahmed Ashteyat ◽  
Yasmeen T. Obaidat ◽  
Yasmin Z. Murad ◽  
Rami Haddad
Keyword(s):  
Gene Expression ◽  
Compressive Strength ◽  
Elevated Temperature ◽  
Gene Expression Programming ◽  
Data Set ◽  
Short Column ◽  
Short Columns ◽  
Ann Models ◽  
Artificial Neural ◽  
Concrete Elements

The experimental behavior of reinforced concrete elements exposed to fire is limited in the literature. Although there are few experimental programs that investigate the behavior of lightweight short columns, there is still a lack of formulation that can accurately predict their ultimate load at elevated temperature. Thus, new equations are proposed in this study to predict the compressive strength of the lightweight short column using Gene Expression Programming (GEP) and Artificial neural networks (ANN). A total of 83 data set is used to establish GEP and ANN models where 70% of the data are used for training and 30% of the data are used for validation and testing. The predicting variables are temperature, concrete compressive strength, steel yield strength, and spacing between stirrups. The developed models are compared with the ACI equation for short columns. The results have shown that the GEP and ANN models have a strong potential to predict the compressive strength of the lightweight short column. The predicted compressive strengths of short lightweight columns using the GEP and ANN models are closer to the experimental results than that obtained using the ACI equations.

Estimation of reference evapotranspiration using Aquacrop model for agro-climatic conditions of Madhya Pradesh

2017 ◽  
Vol 51 (06) ◽  
Author(s):  
Deepika Yadav ◽  
M. K. Awasthi ◽  
R. K. Nema
Keyword(s):  
Temporal Variability ◽  
Reference Evapotranspiration ◽  
Madhya Pradesh ◽  
Weather Data ◽  
Accurate Estimation ◽  
Spatial And Temporal Variability ◽  
Statistical Parameters ◽  
Climatic Zones ◽  
Aquacrop Model ◽  
Daily Weather Data

Accurate estimation of evapotranspiration is necessary step for better management and allocation of water resources. The United Nations Food and Agriculture Organization (FAO) adopted the Penman Moneith method as a global standard to estimate reference crop evapotranspiration (ETo). The study aimed to estimate FAO P-M reference evapotranspiration for different district of five agro climatic zones of Madhya Pradesh state by using Aquacrop model. Daily weather data including maximum and minimum temperature, precipitation, relative humidity, wind speed and solar radiation were collected for the period of 1979 to 2013 which were used as input data in Aquacrop. Several statistical parameters were used for characterizing the spatial and temporal variability of ETo. The average monthly ETo was found maximum in month of May (10.67 mm day-1) in all district of different agro climatic zones for the average period considered for the study and also for each years, whereas average minimum ETo was estimated in month of December (3.23 mm day-1) in Kymore Plateau and August (2.44 mm day-1) in Satpura Plateau. The mean daily reference evapotranspiration ranges from 4 mm day-1 to 10 mm day-1 for all districts. From the statistical analysis it was found that spatial variability of ETo lower than the temporal variability. It means the bigger differentiation of ETo in the years than in the space.

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