Coefficient Of Determination R2 Values Research Articles

Analysis of Adequacy of Infrastructural Facilities and Access to Technical Vocational Education and Training in Trans Nzoia County, Kenya

Background : Technical and Vocational Education and Training (TVET) refers to a range of learning experiences which are relevant to the world of work and contemporary workplaces. The core role of Technical and Vocational Education and Training (TVET) is developing professional skills in youth; equip them with basic knowledge and scientific principles to prepare them for work. Based on this vital role it is expected that youth enroll in TVET for self-reliance. However, the preliminary data from Trans-Nzoia County TVET office showed that there is low enrolment in TVET institutions prompting this study to determine the influence of infrastructural facilities on access to TVET. Materials and Methods : The study adopted The Production Function Theory which suggests that an increase in access to TVET is dependent on the inputs. The scope of the study was 28 County Vocational Training Centers with 161 trainers and 2931 trainees. Stratified random sampling was used in sampling out the VTCs across Trans-Nzoia County where 15 VTCs were sampled. The sample size had 464 respondents; 1 VTC director, 15 head of VTCs, 108 trainers and 340 trainees. Purposive sampling was used to sample VTC heads & director while simple random sampling was used to obtain trainers and trainees. Questionnaires and interview guide was used to collect data. The validity of the research instruments was ascertained through expert judgment. The reliability of the research instruments was determined using the test- retest method. The instruments produced reliability coefficient of 0.82 hence the tools were considered reliable. Results : Quantitative data was analyzed using inferential statistics; PPMC at α = 0.05 and simple regression analysis was used to test the hypothesis. The Pearson product moment correlation index obtained on the relationship between Classrooms and Access to TVET was the highest (r= 0. 983, ρ<0.0001) at α= 0.05),). However, Staff quarters (r = -0.047, ρ= 0.437) at α= 0.05) did not correlate with access to TVET. The coefficient of determination R2 value was 0.791 for regression analysis. This showed that 79.1 per cent of the dependent variable can be predicted by the independent variable. The F-statistics produced (F = 785.056) was significant at 5 per cent level (ρ<0.0001), thus confirming that at least one of the predictors was useful for predicting access to TVET.  Conclusion: The study found out that the availability of infrastructure in VTCs was inadequate. The study recommends that infrastructural facilities be addressed so as to increase access to TVET. These findings will assist the County Government, Ministry of Education and donors on priority areas of funding to increase access to TVET.

KINETICS, EQUILIBRIUM AND THERMODYNAMICS STUDY ON THE ADSORPTION OF PB2+ IONS BY WATER HYACINTH POWDER FROM BOTH INDUSTRIAL WASTEWATER AND AQUEOUS SOLUTION

In this study, water hyacinth powder was an adsorbent for the removal of Pb2+ from an aqueous solution in batch. The individual adsorption capacity of Pb2+ by oven dried water hyacinth powder was studied. The study showed that the adsorption of Pb2+ is better described by the Langmuir isotherm model and the sorption capacity was found to be 16mg/g. Batch adsorption experiments were conducted were used to examine the effects of particle size, pH, contact time and adsorbent dosage on the removal of Pb2+ from aqueous solution by water hyacinth powder. The adsorption efficiency increased with pH and the optimum adsorption was observed at pH 4. Also, the adsorption efficiency of water hyacinth decreased with increase in particle size in the order: <300, >300<425, >425<2800 μm. The optimum time for adsorption of Pb2+ ions was 30 minutes while the optimal adsorption was obtained with 2.5g of the adsorbent. Assessment of kinetics studies showed that the removal of Pb2+ followed pseudo-first-order rate equations based on the coefficient of determination R2 values. The study showed that the use of water hyacinth powder in the removal of Pb2+ from aqueous solution is feasible.

Experimental and kinetic modeling study of multistage freezing-melting process and salt rejection of seawater

Multistage freezing-melting process using successive indirect freezing-melting cycles of seawater samples was investigated to obtain potable water. The salinity of seawater samples experiences a reducing geometric progressive change with the freezing-melting cycles. The salinity of each stage term decreases by a constant factor of ≈ 0.61. The higher coefficient of determination R2 value of the geometric progressive model suggests that this model can be used to explain the experimental data and predict the salinity of the produced frozen water at different initial salt concentrations and different stages. A gradual melting/sweating of frozen water layers under the effect of a temperature gradient was studied to achieve a better understanding of salt rejection and the mechanism of salts formation inside the crystallized region. It is found that the salt distribution in a concentration gradient flowing from cold to hot outside of the ice surface associated with the temperature gradient in salt ice is exponential inversely proportional to the distance axis of the ice phase.

Application of Response Surface Methodology For Modeling and Optimization of A Bio Coagulation Process (Sewage Wastewater Treatment Plant).

Cactus has shown great capabilities as a bio coagulant/flocculent in the treatment of wastewater and as a factor for sustainable development of the environment, due to its abundance and non-toxicity for human health. This has encouraged the present study based on the design of experiments to optimize the two operating factors: the bio coagulant dosage and initial pH. The effect of these considered factors on turbidity and chemical oxygen demand (COD) reduction performances was investigated to treat sewage wastewater from plants by the coagulation/flocculation process using the response surface methodology (RSM) based on a central composite faced design (CCFD). The effect of the pH on the supernatant turbidity removal and the COD reduction was very significant whereas that of the coagulant dosage was insignificant on the COD removal efficiency. Experimental results revealed that the maximum reduction of turbidity and COD could be reached at a coagulant dosage of 28 mg/l and a pH of 12. At these optimal conditions, the removal efficiency of turbidity and COD was 98.33% and 96.55% respectively. By the end of the treatment, final values of 0.84 NTU and 20.8 mg/l were obtained for turbidity and COD, respectively. A notable decrease of orthophosphate (O- PO4-2), nitrite (N-NO3-), ammonium (N-NH4+) and suspended matter (SM) was observed. The study also showed that the quadratic regression model could be used as a theoretical basis for the process based on a high coefficient of determination R2 value > 0.96, obtained from the analysis of variances (ANOVA).

Quantification of bromide ion in biological samples using headspace gas chromatography-mass spectrometry.

ObjectivesIn this study, we aimed to establish a method for quantifying bromide ions (Br−) in blood and urine using gas chromatograph‐mass spectrometer (GC‐MS) equipped with a headspace sampler, for biological monitoring of workers exposed to methyl bromide.MethodsSamples were mixed with dimethyl sulfate, and Br− ions were detected using GC‐MS with a headspace sampler. The validity of the proposed method was evaluated based on most of the US FDA guidance. The values obtained were compared with reference values by analysis using SeronormTM Trace Elements Whole Blood L‐1 RUO.ResultsThe calibration curve showed good linearity in the Br− concentration range of 0.1−20.0 mg/L, and the coefficient of determination R2 value was >.999. Intraday and interday accuracy values were 99.3%−103.1% and 97.4%−101.8%, respectively. The measured and reference values of Seronorm were concordant. Herein, eight urine and serum samples of workers were analyzed; the samples' Br− concentrations were known. The correlation coefficients of urine and serum samples were 0.97 and 0.96, respectively, and results were consistent.ConclusionsThis study established a simple and rapid method for the determination of Br− concentration in biological samples using GC‐MS with a headspace sampler. Moreover, it can be used for biological monitoring of occupational exposure to methyl bromide and for the determination of Br− concentration in a wide range of biological samples.

Detection of Very Small Tree Plantations and Tree-Level Characterization Using Open-Access Remote-Sensing Databases

Highly fragmented land property hinders the planning and management of single species tree plantations. In such situations, acquiring information about the available resources is challenging. This study aims to propose a method to locate and characterize tree plantations in these cases. Galicia (Northwest of Spain) is an area where property is extremely divided into small parcels. European chestnut (Castanea sativa) plantations are an important source of income there; however, it is often difficult to obtain information about them due to their small size and scattered distribution. Therefore, we selected a Galician region with a high presence of chestnut plantations as a case study area in order to locate and characterize small plantations using open-access data. First, we detected the location of chestnut plantations applying a supervised classification for a combination of: Sentinel-2 images and the open-access low-density Light Detection and Ranging (LiDAR) point clouds, obtained from the untapped open-access LiDAR Spanish national database. Three classification algorithms were used: Random Forest (RF), Support Vector Machine (SVM), and XGBoost. We later characterized the plots at the tree-level using the LiDAR point-cloud. We detected individual trees and obtained their height applying a local maxima algorithm to a point-cloud-derived Canopy Height Model (CHM). We also calculated the crown surface of each tree by applying a method based on two-dimensional (2D) tree shape reconstruction and canopy segmentation to a projection of the LiDAR point cloud. Chestnut plantations were detected with an overall accuracy of 81.5%. Individual trees were identified with a detection rate of 96%. The coefficient of determination R2 value for tree height estimation was 0.83, while for the crown surface calculation it was 0.74. The accuracy achieved with these open-access databases makes the proposed procedure suitable for acquiring knowledge about the location and state of chestnut plantations as well as for monitoring their evolution.

Use of Machine Learning and Infrared Spectra for Rheological Characterization and Application to the Apricot

Fast advancement of machine learning methods and constant growth of the areas of application open up new horizons for large data management and processing. Among the various types of data available for analysis, the Fourier Transform InfraRed (FTIR) spectroscopy spectra are very challenging datasets to consider. In this study, machine learning is used to analyze and predict a rheological parameter: firmness. Various statistics have been gathered including both chemistry (such as ethylene, titrable acidity or sugars) and spectra values to visualize and analyze a dataset of 731 biological samples. Two-dimensional (2D) and three-dimensional (3D) principal component analyses (PCA) are used to evaluate their ability to discriminate for one parameter: firmness. Partial least squared regression (PLSR) modeling has been carried out to predict the rheological parameter using either sixteen physicochemical parameters or only the infrared spectra. We show that (i) the spectra alone allows good discrimination of the samples based on rheology, (ii) 3D-PCA allows comprehensive and informative visualization of the data, and (iii) that the rheological parameters are predicted accurately using a regression method such as PLSR; instead of using chemical parameters which are laborious to obtain, Mid-FTIR spectra gathering all physicochemical information could be used for efficient prediction of firmness. As a conclusion, rheological and chemical parameters allow good discrimination of the samples according to their firmness. However, using only the IR spectra leads to better results. A good predictive model was built for the prediction of the firmness of the fruit, and we reached a coefficient of determination R2 value of 0.90. This method outperforms a model based on physicochemical descriptors only. Such an approach could be very helpful to technologists and farmers.

Assessing the Impact of Barrack Environment on Independent Mobility of Children in the Military Barrack of Nigeria

Independent mobility of children living in the barrack is declining over the last four decades due to its unfriendly environment. There are limited studies on children-environment relationships in Nigeria, especially in the barrack. Available studies from social sciences and public health studies lack policy connection with the child-friendly environment for improved independent mobility. Hence, this study aimed to develop a child-friendly barrack environment policy framework for enhanced independent movement of children. The safety and security, barrack environmental quality, basic services, quality of housing, and children’s participation were five characteristics of a child-friendly environment considered. They exogenously predicted improved independent mobility of children in the barrack. On the other hand, the endogenous construct consists of the land-use change, destination accessibility, home range, travel time, and population density of the barrack. A total of 390 parent and primary school children aged 5-12 living in the Nigerian Air Force Barrack in Kaduna jointly assessed the five barrack environment characteristics and five indicators of children’s independent mobility. The survey questionnaire was analysed using SPSS and PLS-SEM. Consequently, the principal component analysis showed high factor loading for the five characteristics and four independent mobility indicators except for the land use that fell below 0.7 thresholds. The outer weights of the reflexive measurement items were high which signified strong correlations. Hence, barrack environment characteristics strongly predicted improved independent mobility of children. Thus, the structural model’s path coefficient of 0.585 explained 58.5% variance in the independent mobility of children in the barrack residential area. The coefficient of determination R2 value is 0.342, and the p-value is at 0.000 level of significance. It provided an environment planning policy framework to monitor interventions and halt the declining rates of children's independent mobility in the military barrack of Nigeria. The conceptual framework developed is applicable in the para-military barrack, housing estates and compact cities in African countries including Nigeria.

Visualizing the Energy of Scattered Data by Using Cubic Timmer Triangular Patches

This paper discusses the application of the new cubic Timmer triangular patches constructed by Ali et al. [1] to interpolate the irregularly scattered data with C1 continuity. In order to apply the cubic Timmer triangular patches for scattered data interpolation, the data is first triangulated by using the Delaunay algorithm, and then the sufficient condition for C1 continuity is derived along the adjacent triangles. Two methods will be used to calculate the cubic Timmer ordinates on each triangle. The convex combination between three local schemes Ti, i = 1,2,3 will be used to produce the C1 surface everywhere. The proposed scheme will be tested to visualize one energy data set with irregular shape properties. Numerical and graphical results are presented by using MATLAB. Comparisons between the proposed scheme and existing schemes such as cubic Ball and cubic Bézier triangular patches are also carried out. The results indicate that the surface produced by cubic Timmer triangular patch is better than surface produced using cubic Ball and cubic Bezier triangular patches with overall coefficient of determination R2 value obtained to be larger than 0.9234.

The Relationship between Precipitation and Elevation of the Watershed in the Khirthar National Range

Precipitation plays a pivotal role in the evolution of the ecosystems. Many watersheds in Pakistan and particularly in parts of Sindh are ungagged, which urge to estimate the precipitation and other parameters using feasible models for effective planning and management of water resources. Regression analysis is usually employed to understand the hydro-climatic relationships of a watershed. The present study is aimed to investigate the relationship between precipitation and elevation through developing linear regression models for various levels of precipitation data and map their spatial variability in the KNR (Khirthar National Range), Sindh, Pakistan. The precipitation data of 7 meteorological stations covering the study area were used with DEM (Digital Elevation Model) data of 90 m resolution for altitudinal trend analysis. The results of the linear regression model showed a close relationship between the precipitation of various time-scales and elevation, i.e. coefficient of determination R2 values ranging within 0.82060.9604. The correlation was significant at P<0.05 for mean annual, mean monthly, and mean seasonal (Rabi) precipitation. The spatial variation in precipitation was between 127.78-697.98 mm at annual and from 10.84-58.09 mm at mean monthly level. The lapse rate of increase in precipitation with elevation was 27.03 mm per 100 m for annual and 2.24 mm per 100 m for monthly level. At seasonal level, precipitation varied from 24.17-137.87 mm during Rabi (winter) and between 105.78-525.36 mm during Kharif (summer) season. Similarly, the monsoon precipitation (July-September) ranged between 92.24359.93 mm in the study area. The higher precipitation estimated over elevated areas of the region at seasonal and annual levels needs to be harvested through adopting adequate water management techniques for improving agriculture productivity in the downstream areas.

Free fatty acid optimization and modeling of biodiesel production from high viscous rubber seed oil– A comparative study of RSM and ANN

ABSTRACT Free fatty acid (FFA) optimization consequent to transesterification of high viscous rubber seed oil by solid base catalyst studied is presented in this work. Presence of more FFA in the feedstock leads to soap formation in the case of base catalysis and hence, pre-treatment is required to reduce the high acid value of raw rubber seed oil. FFA of raw rubber seed oil is reduced to 1.64% by acid catalyzed esterification and the FFA of esterified oil is minimized to 0.115% after biodiesel formation using fluorite (CaF2) derived from the calcination of fluorspar. Response surface methodology (RSM) equipped with central composite design (CCD) is used for FFA optimization. From the RSM analysis, the FFA of synthesized biodiesel is expressed as a quadratic model having a coefficient of determination, R2of value 0.8179. From the 3D-surface plots and 2D contour plots obtained from RSM analysis, it is observed that reaction time is the most significant process parameter that influences FFA optimization. Artificial Neural Network (ANN) modeling with the Levenberg-Marquardt algorithm is used for model network training in rubber seed oil FFA optimization. Mean square error (MSE) of 7.04 × 10−4 and coefficient of determination R2 value of 0.993 was observed at the best validation performance of 0.0019395 at epoch 5. On comparing the coefficient of determination of R2 values observed for both RSM and ANN, it is observed that the value obtained from ANN model fits the data well with minimum error.

Investigating the potential of Calophylluminophyllum plant base oil for oil and gas drilling mud operations

The environmental and cost advantage of non-edible plant oil for potential base oil in oil and gas drilling mud formulation is a drive for its use. The seed of Calophylluminophyllumthe plant oil was processed, pulverized, and oil extracted using chemical method. The extracted plant oil and commercial synthetic oil was used to formulate drilling mud and comparative analysis were made using the physicochemical properties of the oil samples, mud rheological properties under sixteen hours and 240 °F aging and non-aging effect for a 7 and 9 g viscosifier, and rheological models in describing the mud. The commercial synthetic oil and Calophylluminophyllum oil shows a flash point of 101 ± 0.1 and 164 ± 0.1; density of 108 and ; viscosity index of 192 and 163; acid value of NIL and 24.24; and oil yield of NIL and 71 % respectively. The rheological properties of Calophylluminophyllum oil-based mud were higher than the synthetic oil-based mud. It was also observed that the increase in temperature and viscosifer decreases and increases the rheological properties respectively of all mud samples. The synthetic and Calophylluminophyllum oil-based mud increased in the rheological properties after aging test. In the overall estimation of the root mean square error (RMSE) values, coefficient of determination R2 values, and the fitted plots analysis. The Herschel Bulkley and the Sisko model had a much better description in predicting the experimental data for the synthetic oil-based mud. The hyperbolic, Herschel Bulkley and Sisko model had good description for the experimental data of the Calophylluminophyllum oil-based mud.

An image layer difference index method to extract light area from NPP/VIIRS nighttime light monthly data

ABSTRACTNighttime light data have been proven to perform well for economic measurements and built-up area extractions. However, the new generation of monthly NPP/VIIRS data series products, which collected from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (NPP) Satellite contains background values, which relatively restrict the potential application of nighttime images. Therefore, the image layer difference index (LDI) was constructed to obtain high-intensity pixels that have radiance mainly caused by urban light glow. In addition, an empirical threshold was used to extract low-intensity pixels. Thirty-eight counties in Chongqing Province in China were used as an example for this experimental research. To verify the accuracy of the proposed method, a linear regression model, which was based on the quantitative statistics of the pixels, exhibited the coefficient of determination R2 values of 0.980 and 0.947. Considering the interrelation between the light intensity and economic level, statistical data of the county-level economy was also used for further confirmation; the R2 values of the linear regression between the computed indexes were 0.924 and 0.909.

Predicting Surface Roughness and Moisture of Bare Soils Using Multiband Spectral Reflectance Under Field Conditions

Soil surface roughness, denoted by the root mean square height (RMSH), and soil moisture (SM) are critical factors that affect the accuracy of quantitative remote sensing research due to their combined influence on spectral reflectance (SR). In regards to this issue, three SM levels and four RMSH levels were artificially designed in this study; a total of 12 plots was used, each plot had a size of 3 m × 3 m. Eight spectral observations were conducted from 14 to 30 October 2017 to investigate the correlation between RMSH, SM, and SR. On this basis, 6 commonly used bands of optical satellite sensors were selected in this study, which are red (675 nm), green (555 nm), blue (485 nm), near infrared (845 nm), shortwave infrared 1 (1600 nm), and shortwave infrared 2 (2200 nm). A negative correlation was found between SR and RMSH, and between SR and SM. The bands with higher coefficient of determination R2 values were selected for stepwise multiple nonlinear regression analysis. Four characterized bands (i.e., blue, green, near infrared, and shortwave infrared 2) were chosen as the independent variables to estimate SM with R2 and root mean square error (RMSE) values equal to 0.62 and 2.6%, respectively. Similarly, the four bands (green, red, near infrared, and shortwave infrared 1) were used to estimate RMSH with R2 and RMSE values equal to 0.48 and 0.69 cm, respectively. These results indicate that the method used is not only suitable for estimating SM but can also be extended to the prediction of RMSH. Finally, the evaluation approach presented in this paper highly restores the real situation of the natural farmland surface on the one hand, and obtains high precision values of SM and RMSH on the other. The method can be further applied to the prediction of farmland SM and RMSH based on satellite and unmanned aerial vehicle (UAV) optical imagery.

Kinetic and equilibrium adsorption studies of Pb and Zn ions from aqueous solution onto plantain stalk (Musa paradisiaca)

This study presents the sorption of Pb2+ and Zn2+ from aqueous solution by Musa Paradisiaca (plantain stalk) with respect to its equilibrium and kinetic behaviour. The optimum pH was found to be 5.0 and 6.0 for the sorption of the Pb (II) and Zn (II) ions respectively. At pH 5.0, the biomass sorbed 91.7% Pb(II) and 89.85% Zn(II), while at pH 6.0, the biomass sorbed 91.16% Pb (II) and 89.55% Zn (II). The contact time for the sorption was found to be rapid in the first 5mins. The amount of metal ion sorbed was found to be rapid from the lowest concentration, 10 mg/L to the highest concentration, 500 mg/L. The Langmuir and Freundlich model for dynamic metal ion uptake proposed were well fitted for Pb2+ and not well fitted for Zn2+. The structural groups of the adsorbent were characterized by Fourier transform infrared (FTIR) Spectrometry. The following were present; hydroxyl, carbonyl, amine and phosphate groups which confirms the potential processes of sorption of the biosorbent. The first order and second order rate equation were tested and it was found that pseudo-second-order is more suitable for the sorption, having the linear coefficient of determination R2 value of 0.999. The result obtained from this study indicated that plantain stalk (Musa Paradisiaca) could be employed for the removal of heavy metal contaminants from industrial effluents.Keywords: Plantain stalk, kinetics, adsorption, biosorbent

Seasonal-Temporal Variability of Sea Surface Temperature Derived from Satellite and In Situ Data: Case Study of Indian Ocean Western Part of Sumatera Island

It has been widely knowing that one of the important parameters to study about processes and phenomena in oceans is sea surface temperature (SST). This study is about the use of satellite and in situ data to find the seasonal and temporal SST value in Indian Ocean western part of Sumatera Island during 2010 to 2016. This result focuses on changes of SST and the accuracy of SST value derived from MODIS Satellite and ARGO Float. We divide the data into the dry and wet season based on a seasonal condition in Indonesia. The result shows the same pattern of SST value both from satellite and in situ data. The changes trend of SST value (Celsius) is increasing from 2010 to 2016 both in situ and satellite data. SST value increases 0.093 for in situ data and SST value increases 0.132 measured by MODIS satellite. The accuracy assessment (RMSE) value is 0,555. The coefficient of determination r2 value shows a positive correlation between MODIS and ARGO data especially in dry season, while in the wet season the R2 is smaller than in dry season.

Evaluation of Moving Average Model and Autoregressive Moving Average Model (ARMA) for Prediction of Industrial Electricity Consumption in Nigeria

In this paper, evaluation of moving average model and autoregressive moving average model (ARMA) for prediction of industrial electricity consumption in Nigeria is presented. Industrial electricity consumption data obtained from Central Bank of Nigeria (CBN) Statistical Bulletin for the year 1979-2014 is used to determine the model parameters and prediction performance in terms of Root Mean Square Error (RMSE) and Coefficient of determination r2 values. The results show that the Autoregressive Moving Average (ARMA) model with coefficient of determination value of 66.0% and RMSE value of 68.628 gives better prediction performance than the Moving Average with coefficient of determination value of 42.6% and value of 84.749. However, coefficient of determination value of 66% is not particularly adequate for acceptable prediction accuracy. In that case, for better prediction accuracy for the industrial electricity consumption in Nigeria, other models may need to be examined apart from the two models considered in this paper.

An approach of pig weight estimation using binocular stereo system based on LabVIEW

The change of the daily weight of the pig is significant to the pig’s health and process of real production. This study combined the binocular stereo vision system with the LabVIEW development platform to analyze and estimate body size and live body weight using 10 landrace aged from 14 to 25 weeks old at the end under indoor farm condition. The experimental facility was composed of data collection system, image acquisition system, and image analysis system. Data collection system including binocular camera and specially designed limitation fence was used to ensure the pig’s ID recognition and image acquisition were implemented automatically. The images were acquired, saved, and processed by the other two systems to analysis the relationship between body weight and body size. Images acquired by binocular cameras were segmented by the depth threshold. The system verified that the body length (BL) and withers height (WH) were estimated with the R2 ranged from 0.91 to 0.98. The back area and the body weight data obtained by a ground scale were fitted into linear model. According to the result of regression by least square method, the parameters of the function were calculated. Results revealed that live body weight was closely related to the sum of pixel values (SPV) which was calculated from the reconstructed image. Coefficient of determination R2 value was as high as 0.9931. It meant this procedure performed well in predicting live weight. The whole automatic systems have the potential to be applied in the real farm environment.

Projected Change in Climate Under A2 Scenario in Dal Lake Catchment Area of Srinagar City in Jammu and Kashmir

The present study was done to study the effect of climate change on weather parameters like highest possible temperature, lowest possible temperature, average temperature and precipitation. Multiple linear Regression (MLR), Artificial Neural Network (ANN) and Statistical Downscaling Model (SDSM) models were tested in the Dal lake catchment area of Jammu and Kashmir State. Twenty seven year weather data (1985-2012) obtained from SKUAST-Kashmir weather station was used for the study. The modeling results showed a first-rate agreement between the observed data and predicted values for temperature series with high coefficient of determination R2 values varying from (0.87-0.97) for different models. In case of precipitation R2 values varied from (0.112-219) for different models. The low values of coefficient of determination in precipitation time series are due to lot of uncertainty in occurrence of precipitation which could not be defined by the selected models. The SDSM showed the best results of the three models tested for prediction of weather parameters. Thus SDSM was used for climate scenario generation. By comparing daily precipitation and temperature series for 1985-2012 with 2015-2030, an overall increasing pattern of 0.46%, 1.96%, 0.95% and 2.66% was observed for monthly, highest possible temperature, lowest possible temperature, average temperature and precipitation.

On the prediction of the daily global solar radiation intensity on south-facing plane surfaces inclined at varying angles

Over the years, some authors have used different models to estimate the solar radiation intensity over inclined surfaces, but none of them has tested and evaluated the accuracy of these models for various inclinations. In this study, by using the acquired data from Meteonorm, five best models studied previously by the authors have been used in order to predict the daily global solar radiation intensity DGSRI received on an inclined surface to the south and to evaluate the day by day performance of these models using a statistical analysis performed by several statistical indicators. The analysis of results revealed that the selected models haven’t the same accuracy, and it is concluded that there are models that can be preferred for the prediction of the DGSRI received on a tilted surface with different angle of inclination at the considered site, Tetuan city in northern Morocco. Empirical correlations between solar radiation intensity fallen on south-facing plane surfaces with various inclination and that receive on horizontal surfaces have been developed. As mean of results of the statistical indicator errors and excluding 20°, 60° and 70° inclinations and the coefficient of determination R2 values, El Mghouchi model is performing comparatively better than the others for all inclinations ranging from 0° (horizontal plane) to 90° (vertical plane) in steps of 10°.