scholarly journals Forecasting Daily Water Consumption: a Case Study in Torun, Poland

2018 ◽  
Author(s):  
Adam Piasecki ◽  
Jakub Jurasz ◽  
Bartosz Kaźmierczak
Keyword(s):  
Water Consumption ◽  
Percentage Error ◽  
Ann Model ◽  
Explanatory Variables ◽  
Daily Water ◽  
Working Day ◽  
Artificial Neural Network Ann ◽  
Public Holidays ◽  
Daily Water Consumption

This paper presents Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) methods for predicting future daily water consumption values based on three antecedent records of water consumption and humidity forecast for a given day, which are considered as independent variables. Mean Absolute Percentage Error (MAPE) is obtained for different configurations of the input sets and of the ANN model structure. Additionally, sets of explanatory variables are enhanced with dummy variables indicating typical days: working day, Saturday, Sunday/public holidays. The results indicated the superiority of the ANN approach over MLR, although the observed difference in performance was very limited.

PENGARUH PENGGUNAAN VIRGIN COCONUT OIL (VCO) DALAM RANSUM TERHADAP PERFORMANS TERNAK BABI FASE STARTER

ZOOTEC ◽  
2015 ◽  
Vol 35 (2) ◽  
pp. 86
Author(s):  
Alfred Tamawiwy ◽  
M. Najoan ◽  
J S Mandey ◽  
F. N Sompie
Keyword(s):  
Water Consumption ◽  
Feed Intake ◽  
Control Diet ◽  
Coconut Oil ◽  
Growing Pigs ◽  
Virgin Coconut Oil ◽  
Daily Water ◽  
Pig Performance ◽  
Daily Gain ◽  
Daily Water Consumption

ABSTRACT   EFFECT OF UTILIZATION OF VIRGIN COCONUT OIL (VCO) IN THE DIET ON PIG PERFORMANCE. Utilization of fats and oils in pig diets is of great importance due to their high energy value. VCO is obtained by cold press processing of the kernel from the coconut fruit. Utilization of virgin coconut oil (VCO) in the diets on pig performance.  The present study was designed to elaborate the effect of utilization of VCO in the diets on energy and protein digestibility of growing pigs. The experiment was conducted using 20 castrated male pigs aged 1.5 - 2.0 months weighing 12,0±2,0 kg. The data were analyzed according to the linear model procedure for ANOVA appropriate for Randomized Block Design with 5 treatments and 4 replications. Treatments were formulated as follow: R0 = 100% control diet + 0% VCO; R1 = 99.5% control diet + 1.0% VCO; R2 = 98.0% control diet + 2.0% VCO; R3 = 97.0% control diet + 3.0% VCO; and R4 = 96.0% control diet + 4.0% VCO. Parameters measured were: daily feed intake, daily gain, daily water consumption. The results showed that the utilization of VCO up to 4% in the diets had no significant effect (P > 0.05) on daily feed intake, daily gain, daily water consumption of pigs. It can be concluded that the addition of VCO up to 4.0% in the diets has no significant meaning on pig performance.   Key words: Virgin coconut oil (VCO), Performance, Growing pigs  

scholarly journals Daily Water Consumption and its Contribution to Calcium Intake in Korean Adults

2019 ◽  
Vol 24 (1) ◽  
pp. 18 ◽  
Author(s):  
Eun-Sun Park ◽  
Yeon-Kyung Lee ◽  
Mi-Hyun Kim ◽  
Mi-Kyeong Choi
Keyword(s):  
Water Consumption ◽  
Calcium Intake ◽  
Korean Adults ◽  
Daily Water ◽  
Daily Water Consumption

Predicting wildfire vulnerability using logistic regression and artificial neural networks: a case study in Brazil's Federal District

2019 ◽  
Vol 28 (1) ◽  
pp. 35 ◽  
Author(s):  
Pablo Pozzobon de Bem ◽  
Osmar Abílio de Carvalho Júnior ◽  
Eraldo Aparecido Trondoli Matricardi ◽  
Renato Fontes Guimarães ◽  
Roberto Arnaldo Trancoso Gomes
Keyword(s):  
Logistic Regression ◽  
Characteristic Curve ◽  
Federal District ◽  
Burned Area ◽  
Ann Model ◽  
Wildfire Management ◽  
Explanatory Variables ◽  
Burned Areas ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Predicting the spatial distribution of wildfires is an important step towards proper wildfire management. In this work, we applied two data-mining models commonly used to predict fire occurrence – logistic regression (LR) and an artificial neural network (ANN) – to Brazil’s Federal District, located inside the Brazilian Cerrado. We used Landsat-based burned area products to generate the dependent variable, and nine different anthropogenic and environmental factors as explanatory variables. The models were optimised via feature selection for best area under receiver operating characteristic curve (AUC) and then validated with real burn area data. The models had similar performance, but the ANN model showed better AUC (0.77) and accuracy values when evaluating exclusively non-burned areas (73.39%), whereas it had worse accuracy overall (66.55%) when classifying burned areas, in which LR performed better (65.24%). Moreover, we compared the contribution of each variable to the models, adding some insight into the main causes of wildfires in the region. The main driving aspects of the burned area distribution were land-use type and elevation. The results showed good performance for both models tested. These studies are still scarce despite the importance of the Brazilian savanna.

scholarly journals Influence of Land Use Patterns on Evapotranspiration and Its Components in a Temperate Grassland Ecosystem

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Yuzhe Li ◽  
Jiangwen Fan ◽  
Zhongmin Hu ◽  
Quanqin Shao ◽  
Liangxia Zhang ◽  
...  
Keyword(s):  
Land Use ◽  
Water Consumption ◽  
Vapor Pressure Deficit ◽  
Land Use Patterns ◽  
Area Index ◽  
Use Patterns ◽  
Daily Water ◽  
Explained Variation ◽  
Infrared Gas Analyzer ◽  
Daily Water Consumption

To better understand variation in response of components of ecosystem evapotranspiration (ET) to grassland use differences, we selected three typical land use patterns in a temperate steppe area: grazed steppe (G), steppe with grazers excluded (GE), and steppe cultivated to cropland (C). ET was divided into its components evaporation (E) and canopy transpiration (T) using herbicide and a chamber attached to a portable infrared gas analyzer (Li-6400). The results indicated that daily water consumption by ET in G was 3.30 kg m−2d−1; compared with G, ET increased significantly in GE at 13.4% and showed a trend of 6.73% increase in C. Daily water consumption by E increased 24.3% in GE relative to G, and C showed 20.2% more than GE. At 0.46, E/ET in C was significantly higher than G at 0.35. Air temperature and the vapor pressure deficit were closely correlated with variation in diurnal ET, E, and T. The leaf area index (LAI) was also positively correlated with daily ET and E varied among grassland use patterns and explained variation in E/ET (81%). Thus, variation in LAI strongly influences the overall magnitude of ecosystem ET and the composition of its components under different grassland use patterns.

scholarly journals Fluctuations of Daily Water Consumption in Sheep.

1967 ◽  
Vol 1967 (4) ◽  
pp. 23-25
Author(s):  
Yasushi ASAHIDA ◽  
K^|^ocirc; MIMURA
Keyword(s):  
Water Consumption ◽  
Daily Water ◽  
Daily Water Consumption

scholarly journals Use of Empirical Mode Decomposition in Improving Neural Network Forecasting of Paddy Price

MATEMATIKA ◽  
2019 ◽  
Vol 35 (4) ◽  
pp. 53-64
Author(s):  
Siti Nabilah Syuhada Abdullah ◽  
Ani Shabri ◽  
Ruhaidah Samsudin
Keyword(s):  
Neural Network ◽  
Empirical Mode Decomposition ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Percentage Error ◽  
Forecast Errors ◽  
Ann Model ◽  
Mode Decomposition ◽  
The Neural Network ◽  
Artificial Neural Network Ann

Since rice is a staple food in Malaysia, its price fluctuations pose risks to the producers, suppliers and consumers. Hence, an accurate prediction of paddy price is essential to aid the planning and decision-making in related organizations. The artificial neural network (ANN) has been widely used as a promising method for time series forecasting. In this paper, the effectiveness of integrating empirical mode decomposition (EMD) into an ANN model to forecast paddy price is investigated. The hybrid method is applied on a series of monthly paddy prices fromFebruary 1999 up toMay 2018 as recorded in the Malaysian Ringgit (MYR) per metric tons. The performance of the simple ANN model and the EMD-ANN model was measured and compared based on their root mean squared Error (RMSE), mean absolute error (MAE) and mean percentage error (MPE). This study finds that the integration of EMD into the neural network model improves the forecasting capabilities. The use of EMD in the ANN model made the forecast errors reduced significantly, and the RMSE was reduced by 0.012, MAE by 0.0002 and MPE by 0.0448.

scholarly journals Guidelines for irrigation scheduling of banana crop in São Francisco Valley, Brazil. II - Water consumption, crop coefficient, and physiologycal behavior

2004 ◽  
Vol 26 (3) ◽  
pp. 464-467 ◽  
Author(s):  
Luís Henrique Bassoi ◽  
Antonio Heriberto de Castro Teixeira ◽  
José Moacir Pinheiro Lima Filho ◽  
José Antonio Moura e Silva ◽  
Emanuel Elder Gomes da Silva ◽  
...  
Keyword(s):  
Water Consumption ◽  
Water Availability ◽  
Crop Coefficient ◽  
Irrigation Scheduling ◽  
Northeastern Brazil ◽  
Evaporative Demand ◽  
Irrigation Water Management ◽  
Growing Seasons ◽  
Daily Water ◽  
Daily Water Consumption

The water consumption and the crop coefficient of the banana cv. Pacovan were estimated in Petrolina County, northeastern Brazil, in order to establish guidelines to irrigation water management. Evaluations were carried out since planting in January 1999 to the 3rd harvest in September 2001 on a microsprinkler irrigated orchard, with plants spaced in a 3 x 3 m grid. Average daily water consumption was 3.9, 4.0, and 3.3 mm in the 1st, 2nd and 3rd growing seasons, respectively. Crop coefficient values increased from 0.7 (vegetative growth) to 1.1 (flowering). Even with high soil water availability, transpiration was reduced due to high evaporative demand.

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