scholarly journals APPLICATION OF HYDROLOGICAL MODELS IN A SMALL AGRICULTURAL CATCHMENT FOR WATER MANAGEMENT: CASE STUDY ON WADI EL-RAML WATERSHED-NORTH WESTERN COAST OF EGYPT

2021 ◽  
Vol 21 (2) ◽  
Author(s):  
M.A.I. Abdalla ◽  
M.K. Abd El- Wahab ◽  
M.A. Tawfik ◽  
I.M.M. Khater
Keyword(s):  
Management Practices ◽  
Satellite Image ◽  
Winter Season ◽  
Weather Data ◽  
Coefficient Of Determination ◽  
Western Coast ◽  
Hydrological Models ◽  
Data Set ◽  
Supplementary Irrigation ◽  
North Western

North Western Coast of Egypt as a semi-arid region suffers from lack of rainfall most of the year except during rainfall events during the winter season which may even culminate with flash floods that causes acceleration of soil erosion and water losses resulting in degradation of the cultivation lands and consequently the agri-food productivity. This region Suffer lack of water reservoirs and terraces construction. Hydrological models are very useful tools for simulating the effect of natural processes and management practices on soil and water resources. Hence, the aim of study is calibrating and validating hydrological models KINEROS2(K2), ARCSWAT, to select the appropriate model and applying it to establish water and Soil conservation strategies such as terraces construction and building reservoirs. A yearly data set was used consisting of weather data, water content measurements and Gerlesh trough technique. Satellite image Digital elevation model (DEM), Land Cover classifications and soil map were used as layers input in models. The Nash–Sutcliffe coefficient (NSE) and coefficient of determination (R²) were used to evaluate model’s performance. The results reveal that the K2 are better than ARCSWAT, with acceptable NSE and R2 values, where the NSE, R2 were 0.86, 0.84 fork 2 and NSE, R2 were 0.55, 0.47 for ARCSWAT respectively. The K2 model was applied in sub-catchment where the estimated result of surface runoff was ranged from 21.92 to 169.695 m 3 ha-1 year-1. Hence, it is recommended that a reservoir can be constructed inside field approximately about 200 m3 ha-1 y-1 for using as Supplementary irrigation during dry period and the result indicated to areas with a high risk of soil loss that ranged from 38.88 to 3,860 kg. ha-1. year-1 .

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

scholarly journals Correlation between the structure and skin permeability of compounds

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ruolan Zeng ◽  
Jiyong Deng ◽  
Limin Dang ◽  
Xinliang Yu
Keyword(s):  
Large Data ◽  
Qsar Model ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Skin Permeability ◽  
Data Set ◽  
Test Set ◽  
Svm Algorithm ◽  
Svm Model ◽  
Toxicity Relationship

AbstractA three-descriptor quantitative structure–activity/toxicity relationship (QSAR/QSTR) model was developed for the skin permeability of a sufficiently large data set consisting of 274 compounds, by applying support vector machine (SVM) together with genetic algorithm. The optimal SVM model possesses the coefficient of determination R2 of 0.946 and root mean square (rms) error of 0.253 for the training set of 139 compounds; and a R2 of 0.872 and rms of 0.302 for the test set of 135 compounds. Compared with other models reported in the literature, our SVM model shows better statistical performance in a model that deals with more samples in the test set. Therefore, applying a SVM algorithm to develop a nonlinear QSAR model for skin permeability was achieved.

Climate resilience in rural Zambia: evaluating farmers’ response to El Niño-induced drought

2021 ◽  
pp. 1-23
Author(s):  
Federica Alfani ◽  
Aslihan Arslan ◽  
Nancy McCarthy ◽  
Romina Cavatassi ◽  
Nicholas Sitko
Keyword(s):  
El Niño ◽  
Management Practices ◽  
Unobserved Heterogeneity ◽  
El Nino ◽  
Control Measures ◽  
Data Set ◽  
Climate Resilience ◽  
Diversification Strategies ◽  
Household Incomes ◽  
Rural Zambia

Abstract This paper aims at identifying whether and how sustainable land management practices and livelihood diversification strategies have contributed to moderating the impacts of the El Niño-related drought in Zambia. This is done using a specifically designed survey called the El Niño Impact Assessment Survey, which is combined with the Rural Agricultural Livelihoods Surveys, as well as high resolution rainfall data at the ward level over 34 years. This unique panel data set allows us to control for the time-invariant unobserved heterogeneity to understand the impacts of shocks like El Niño, which are expected to become more frequent and severe as a result of climate change. We find that maize yields were substantially reduced and that household incomes were only partially protected from the shock thanks to diversification strategies. Mechanical erosion control measures and livestock diversification emerge as the only strategies that provided yield and income benefits under weather shock.

scholarly journals Evaluation of NASA POWER Reanalysis Products to Estimate Daily Weather Variables in a Hot Summer Mediterranean Climate

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1207
Author(s):  
Gonçalo C. Rodrigues ◽  
Ricardo P. Braga
Keyword(s):  
Wind Speed ◽  
Bias Correction ◽  
Goodness Of Fit ◽  
Mediterranean Climate ◽  
Weather Data ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Weather Variables ◽  
Weather Stations

This study aims to evaluate NASA POWER reanalysis products for daily surface maximum (Tmax) and minimum (Tmin) temperatures, solar radiation (Rs), relative humidity (RH) and wind speed (Ws) when compared with observed data from 14 distributed weather stations across Alentejo Region, Southern Portugal, with a hot summer Mediterranean climate. Results showed that there is good agreement between NASA POWER reanalysis and observed data for all parameters, except for wind speed, with coefficient of determination (R2) higher than 0.82, with normalized root mean square error (NRMSE) varying, from 8 to 20%, and a normalized mean bias error (NMBE) ranging from –9 to 26%, for those variables. Based on these results, and in order to improve the accuracy of the NASA POWER dataset, two bias corrections were performed to all weather variables: one for the Alentejo Region as a whole; another, for each location individually. Results improved significantly, especially when a local bias correction is performed, with Tmax and Tmin presenting an improvement of the mean NRMSE of 6.6 °C (from 8.0 °C) and 16.1 °C (from 20.5 °C), respectively, while a mean NMBE decreased from 10.65 to 0.2%. Rs results also show a very high goodness of fit with a mean NRMSE of 11.2% and mean NMBE equal to 0.1%. Additionally, bias corrected RH data performed acceptably with an NRMSE lower than 12.1% and an NMBE below 2.1%. However, even when a bias correction is performed, Ws lacks the performance showed by the remaining weather variables, with an NRMSE never lower than 19.6%. Results show that NASA POWER can be useful for the generation of weather data sets where ground weather stations data is of missing or unavailable.

scholarly journals Genetic and Genome-Wide Association Analysis of Yearling Weight Gain in Israel Holstein Dairy Calves

Genes ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 708
Author(s):  
Moran Gershoni ◽  
Joel Ira Weller ◽  
Ephraim Ezra
Keyword(s):  
Weight Gain ◽  
Milk Fat ◽  
Variance Components ◽  
Complete Data ◽  
Variance Component Analysis ◽  
Coefficient Of Determination ◽  
Birth Date ◽  
Analysis Model ◽  
Data Set ◽  
Genetic Evaluations

Yearling weight gain in male and female Israeli Holstein calves, defined as 365 × ((weight − 35)/age at weight) + 35, was analyzed from 814,729 records on 368,255 animals from 740 herds recorded between 1994 and 2021. The variance components were calculated based on valid records from 2008 through 2017 for each sex separately and both sexes jointly by a single-trait individual animal model analysis, which accounted for repeat records on animals. The analysis model also included the square root, linear, and quadratic effects of age at weight. Heritability and repeatability were 0.35 and 0.71 in the analysis of both sexes and similar in the single sex analyses. The regression of yearling weight gain on birth date in the complete data set was −0.96 kg/year. The complete data set was also analyzed by the same model as the variance component analysis, including both sexes and accounting for differing variance components for each sex. The genetic trend for yearling weight gain, including both sexes, was 1.02 kg/year. Genetic evaluations for yearling weight gain was positively correlated with genetic evaluations for milk, fat, protein production, and cow survival but negatively correlated with female fertility. Yearling weight gain was also correlated with the direct effect on dystocia, and increased yearling weight gain resulted in greater frequency of dystocia. Of the 1749 Israeli Holstein bulls genotyped with reliabilities >50%, 1445 had genetic evaluations. As genotyping of these bulls was performed using several single nucleotide polymorhphism (SNP) chip platforms, we included only those markers that were genotyped in >90% of the tested cohort. A total of 40,498 SNPs were retained. More than 400 markers had significant effects after permutation and correction for multiple testing (pnominal < 1 × 10−8). Considering all SNPs simultaneously, 0.69 of variance among the sires’ transmitting ability was explained. There were 24 markers with coefficients of determination for yearling weight gain >0.04. One marker, BTA-75458-no-rs on chromosome 5, explained ≈6% of the variance among the estimated breeding values for yearling weight gain. ARS-BFGL-NGS-39379 had the fifth largest coefficient of determination in the current study and was also found to have a significant effect on weight at an age of 13–14 months in a previous study on Holsteins. Significant genomic effects on yearling weight gain were mainly associated with milk production quantitative trait loci, specifically with kappa casein metabolism.

scholarly journals Development of a semi-parametric PAR (Photosynthetically Active Radiation) partitioning model for the United States, version 1.0

2014 ◽  
Vol 7 (5) ◽  
pp. 2477-2484 ◽  
Author(s):  
J. C. Kathilankal ◽  
T. L. O'Halloran ◽  
A. Schmidt ◽  
C. V. Hanson ◽  
B. E. Law
Keyword(s):  
United States ◽  
Elevation Angle ◽  
Model Performance ◽  
Diffuse Radiation ◽  
The United States ◽  
Polynomial Model ◽  
Coefficient Of Determination ◽  
Data Set ◽  
Cubic Polynomial ◽  
Solar Elevation Angle

Abstract. A semi-parametric PAR diffuse radiation model was developed using commonly measured climatic variables from 108 site-years of data from 17 AmeriFlux sites. The model has a logistic form and improves upon previous efforts using a larger data set and physically viable climate variables as predictors, including relative humidity, clearness index, surface albedo and solar elevation angle. Model performance was evaluated by comparison with a simple cubic polynomial model developed for the PAR spectral range. The logistic model outperformed the polynomial model with an improved coefficient of determination and slope relative to measured data (logistic: R2 = 0.76; slope = 0.76; cubic: R2 = 0.73; slope = 0.72), making this the most robust PAR-partitioning model for the United States currently available.

Biochemical responses of the marine mussel Mytilus galloprovincialis to petrochemical environmental contamination along the North-western coast of Portugal

Chemosphere ◽  
2007 ◽  
Vol 66 (7) ◽  
pp. 1230-1242 ◽  
Author(s):  
Inês Lima ◽  
Susana M. Moreira ◽  
Jaime Rendón-Von Osten ◽  
Amadeu M.V.M. Soares ◽  
Lúcia Guilhermino
Keyword(s):  
Environmental Contamination ◽  
Mytilus Galloprovincialis ◽  
Western Coast ◽  
Marine Mussel ◽  
Biochemical Responses ◽  
The North ◽  
North Western

Field survey on frost damage to roof tiles under climatic conditions

2016 ◽  
Vol 34 (2) ◽  
pp. 135-149 ◽  
Author(s):  
Chiemi Iba ◽  
Ayumi Ueda ◽  
Shuichi Hokoi
Keyword(s):  
Field Survey ◽  
Winter Season ◽  
Frost Damage ◽  
Climatic Conditions ◽  
Weather Data ◽  
Measured Temperature ◽  
Content Type ◽  
Roof Tile ◽  
Deterioration Process ◽  
The Impact

Purpose – Frost damage is well-known as the main cause of roof tile deterioration. The purpose of this paper is to develop an analytical model for predicting the deterioration process under certain climatic conditions. This paper describes the results of a field survey conducted to acquire fundamental information useful to this aim. Design/methodology/approach – A field survey of roof tile damage by freezing was conducted in an old temple precinct in Kyoto, Japan. Using detailed observations and photographic recordings, the damage progress was clarified. To examine the impact of climatic conditions upon the damage characteristics, weather data and roof tile temperatures were measured and logged in the winter season. Findings – The deterioration process was observed under the climatic conditions associated with the measured temperature of the roof tiles. In particular, it was revealed that the orientation has a significant influence on increasing or decreasing the risk of frost damage. For certain distinctive forms of damage, the deterioration mechanisms were estimated from the viewpoint of the moisture flow and temperature distribution in the tile. Originality/value – This study contributes to the elucidation of the mechanism behind frost damage to roof tiles. The findings will guide the construction of a numerical model for frost damage prediction.

Artificial Intelligence Coreflooding Simulator for Special Core Data Analysis

2021 ◽  
pp. 1-29
Author(s):  
Eric Sonny Mathew ◽  
Moussa Tembely ◽  
Waleed AlAmeri ◽  
Emad W. Al-Shalabi ◽  
Abdul Ravoof Shaik
Keyword(s):  
Artificial Intelligence ◽  
Steady State ◽  
History Matching ◽  
Water Saturation ◽  
Flow Behavior ◽  
Model Performance ◽  
Analysis Data ◽  
Coefficient Of Determination ◽  
Water Drainage ◽  
Data Set

Two of the most critical properties for multiphase flow in a reservoir are relative permeability (Kr) and capillary pressure (Pc). To determine these parameters, careful interpretation of coreflooding and centrifuge experiments is necessary. In this work, a machine learning (ML) technique was incorporated to assist in the determination of these parameters quickly and synchronously for steady-state drainage coreflooding experiments. A state-of-the-art framework was developed in which a large database of Kr and Pc curves was generated based on existing mathematical models. This database was used to perform thousands of coreflood simulation runs representing oil-water drainage steady-state experiments. The results obtained from the corefloods including pressure drop and water saturation profile, along with other conventional core analysis data, were fed as features into the ML model. The entire data set was split into 70% for training, 15% for validation, and the remaining 15% for the blind testing of the model. The 70% of the data set for training teaches the model to capture fluid flow behavior inside the core, and then 15% of the data set was used to validate the trained model and to optimize the hyperparameters of the ML algorithm. The remaining 15% of the data set was used for testing the model and assessing the model performance scores. In addition, K-fold split technique was used to split the 15% testing data set to provide an unbiased estimate of the final model performance. The trained/tested model was thereby used to estimate Kr and Pc curves based on available experimental results. The values of the coefficient of determination (R2) were used to assess the accuracy and efficiency of the developed model. The respective crossplots indicate that the model is capable of making accurate predictions with an error percentage of less than 2% on history matching experimental data. This implies that the artificial-intelligence- (AI-) based model is capable of determining Kr and Pc curves. The present work could be an alternative approach to existing methods for interpreting Kr and Pc curves. In addition, the ML model can be adapted to produce results that include multiple options for Kr and Pc curves from which the best solution can be determined using engineering judgment. This is unlike solutions from some of the existing commercial codes, which usually provide only a single solution. The model currently focuses on the prediction of Kr and Pc curves for drainage steady-state experiments; however, the work can be extended to capture the imbibition cycle as well.

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