Root Of The Mean Square Error Research Articles

Quantifying Geobacter sulfurreducens growth: A mathematical model based on acetate concentration as an oxidizing substrate.

We developed a mathematical model to simulate dynamics associated with the proliferation of Geobacter and ultimately optimize cellular operation by analyzing the interaction of its components. The model comprises two segments: an initial part comprising a logistic form and a subsequent segment that incorporates acetate oxidation as a saturation term for the microbial nutrient medium. Given that four parameters can be obtained by minimizing the square root of the mean square error between experimental Geobacter growth and the mathematical model, the model underscores the importance of incorporating nonlinear terms. The determined parameter values closely align with experimental data, providing insights into the mechanisms that govern Geobacter proliferation. Furthermore, the model has been transformed into a scaleless equation with only two parameters to simplify the exploration of qualitative properties. This allowed us to conduct stability analysis of the fixed point and construct a co-dimension two bifurcation diagram.

177 Determining the Body Weight to Body Fat Conversion Factor for Angus, Charolais, and Brahman Growing Steers

Abstract The profitability of beef feedlot operations depends on accurately predicting the energy and nutrient requirements of the animal to optimize animal growth and carcass quality to meet market specifications. The composition of the gain (i.e., fat and protein) dictates the growth of the animal, and it is highly correlated with the body composition of the mature animal, assuming that the composition of body gain is constant for animals at the same maturity degree. Once the mature weight for a given body composition (i.e., fat) is known, the maturity degree can be estimated, and the equivalent body weight (EqBW) can be used to calculate energy requirements for growth. Several standard feeding systems and nutrition models use the adjusted final body weight (AFBW) at a known fat composition to estimate EqBW. The AFBW is the body weight (BW) at, usually, 28% empty body fat (EBF). Thus, AFBW can be estimated if a given empty BW (EBW) and composition are known, and an EBW-to-EBF factor is used to calculate AFBW from the current BW. Several nutrition models, including the Cattle Value Discovery System, have adopted the 14.26 kg of EBW for each % of EBF. Therefore, this study aimed to determine the amount of body mass deposited per change in body fat content of different breeds commonly used in Australia. We randomly allocated 30 Angus, 30 Brahman, and 29 Charolais into six slaughter groups (6 animals per breed per slaughter group) fed for up to 200 days. Internal organs and carcasses were dissected into physically separable fat, lean, and bones. When we regressed EBW on EBF, the overall regression yielded a slope of 11.2 ± 0.69 kg/%EBF [n = 89, r2 = 0.75, and root of the mean square error (RMSE) = 55.7 kg], likely not different from the 14.26 kg/% EBF. However, slopes were different among breeds (P < 0.001), i.e., there was an interaction between breeds and EBF when the breed was added to the ordinary least-square mean regression as a classificatory variable. The slopes were 16.5 ± 0.84 kg/% for Charolais (n = 29, r2 = 0.933, RMSE = 33.2 kg), 13.2 ± 0.73 kg /% for Angus (n = 30, r2 = 0.921, RMSE = 34.4 kg), and 10.1 ± 1.06 kg/% for Brahman (n = 30, r2 = 0.765, RMSE = 38.3 kg). When we regressed the amount of EBF on EBW, we obtained 0.40, 0.58, and 0.52 kg EBF/kg EBW for Charolais, Angus and Brahman, respectively. This confirms that Charolais deposited less EBF per unit of gain compared with Angus and Brahman. Given the similarity between the 14.26 kg/% to the overall value of 11.2 kg/%, we recommend using the 14.26 EBW-to-EBF factor to estimate AFBW when the genetic group is unknown.

Demand Side Control for Energy Saving in Renewable Energy Resources Using Deep Learning Optimization

Convolutional neural networks a type of deep learning technology, are used for forecasting future power usage. The mean absolute error, mean square error, root mean square error, and mean constant percentage error are used to evaluate the performance of the models. These metrics are used to rank the models. Among the models tested, the CNN stack demonstrates the highest precision in estimating energy consumption and solar power output, with a mean absolute error of 0.015% points, a root mean square error of 0.23% points, and an average absolute percentage deviation of 1.71% points. However, for wind turbine (WT) energy generation, the recurrent neural network proves for most accurate model, achieving 0.070 as the median absolute percentage error is 2.65, Both the root of the mean square error and the average absolute error are 0.38. The training and validation data utilized in this study comprise the International Renewable Energy Agency’s data on solar power production, WT power generation, and the “ensue” dataset, which includes hourly power consumption information from the Pennsylvania, New Jersey, and Maryland interconnection.

Artificial neural network approach for predicting the sesame (Sesamum indicum L.) leaf area: A non-destructive and accurate method

The estimative of the leaf area using a nondestructive method is paramount for successive evaluations in the same plant with precision and speed, not requiring high-cost equipment. Thus, the objective of this work was to construct models to estimate leaf area using artificial neural network models (ANN) and regression and to compare which model is the most effective model for predicting leaf area in sesame culture. A total of 11,000 leaves of four sesame cultivars were collected. Then, the length (L) and leaf width (W), and the actual leaf area (LA) were quantified. For the ANN model, the parameters of the length and width of the leaf were used as input variables of the network, with hidden layers and leaf area as the desired output parameter. For the linear regression models, leaf dimensions were considered independent variables, and the actual leaf area was the dependent variable. The criteria for choosing the best models were: the lowest root of the mean squared error (RMSE), mean absolute error (MAE), and absolute mean percentage error (MAPE), and higher coefficients of determination (R2). Among the linear regression models, the equation yˆ=0.515+0.584*LW was considered the most indicated to estimate the leaf area of the sesame. In modeling with ANNs, the best results were found for model 2-3-1, with two input variables (L and W), three hidden variables, and an output variable (LA). The ANN model was more accurate than the regression models, recording the lowest errors and higher R2 in the training phase (RMSE: 0.0040; MAE: 0.0027; MAPE: 0.0587; and R2: 0.9834) and in the test phase (RMSE: 0.0106; MAE: 0.0029; MAPE: 0.0611; and R2: 0.9828). Thus, the ANN method is the most indicated and accurate for predicting the leaf area of the sesame.

Post-Pandemic Steel Production Scenarios for Poland Based on Forecasts of Annual Steel Production Volume

Abstract The paper presents the results of forecasts made for the volume of steel production in Poland based on actual data for the period from 2006 to 2021 with forecasting until 2026. The actual data used for the forecasts included annual steel production volumes in Poland (crude steel) in millions of tons. Basic adaptive methods were used to forecast the volume of steel production for the next five years. When selecting the methods, the course of the trend of the studied phenomenon was taken into account. In order to estimate the level of admissibility of the adopted forecasting methods, as well as to select the best forecasts, the errors of apparent forecasts (ex post) were calculated. Errors were calculated in the work: RMSE Root Mean Square Error being the square root of the mean square error of the ex-post forecasts yt for the period 2006-2021; ? as the mean value of the relative error of expired forecasts y*t (2006-2021) – this error informs about the part of the absolute error per unit of the real value of the variable yt. Optimization of the forecast values was based on the search for the minimum value of one of the above-mentioned errors, treated as an optimization criterion. In addition, the value of the point forecast (for 2022) obtained on the basis of the models used was compared with the steel production volume obtained for 3 quarters of 2022 in Poland with the forecast for the last quarter. Forecasting results obtained on the basis of the forecasting methods used, taking into account the permissible forecast errors, were considered as the basis for determining steel production scenarios for Poland until 2026. To determine the scenarios, forecast aggregation was used, and so the central forecasts were determined separately for decreasing trends and for increasing trends, based on the average values of the forecasts obtained for the period 2022-2026. The central forecasts were considered the baseline scenarios for steel production in Poland in 2022-2026 and the projected production volumes above the baseline forecasts with upward trends were considered an optimistic scenario, while the forecasted production volumes below the central scenario for downward trends were considered a pessimistic scenario for the Polish steel industry.

Reliability Estimation under Normal Operating Conditions for Progressively Type-II XLindley Censored Data

This paper assumes constant-stress accelerated life tests when the lifespan of the test units follows the XLindley distribution. In addition to the maximum likelihood estimation, the Bayesian estimation of the model parameters is acquired based on progressively Type-II censored samples. The point and interval estimations of the model parameters and some reliability indices under normal operating conditions at mission time are derived using both estimation methods. Using the Markov chain Monte Carlo algorithm, the Bayes estimates are calculated using the squared error loss function. Simulating the performances of the different estimation methods is performed to illustrate the proposed methodology. As an example of how the proposed methods can be applied, we look at two real-life accelerated life test cases. According to the numerical outcomes and based on some criteria, including the root of the mean square error and interval length, we can conclude that the Bayesian estimation method based on the Markov chain Monte Carlo procedure performs better than the classical methods in evaluating the XLindley parameters and some of its reliability measures when a constant-stress accelerated life test is applied with progressively Type-II censoring.

Fuzzy-logic Color Recognition System Using a Fast Defuzzifier

Purpose of research. The research presented in this article is aimed at improving the accuracy of determining the color shade. The developed fuzzy-logical color recognition system was used as the subject of the study. The efficiency indicator was the result of calculating the sensitivity area percentage and RMSE of the developed method.Methods. A method based on fuzzy logic has been developed and implemented, namely, on the structure of Mamdani's fuzzy inference, which consists of the following stages: fuzzification, fuzzy logical inference, defuzzification. Triangular membership functions were used at the fuzzification stage. As a compositional rule, 12 input variables were used, combined on the basis of Zadeh's compositional rule in 27. At the defuzzification stage, the area ratio method was used. The object of the study was the developed mathematical model for determining color.Results. A mathematical model has been developed, consisting of 4 steps, which guarantees a clear definition of 9 colors and their shades. Based on the estimation of the root of the mean square error, it was concluded that the proposed model is better than traditional options. It is expressed by the fact that the developed method reacts on the interval of the entire surface of output variables, while traditional methods have dead zones to changes in input variables.Conclusion. A fuzzy-logical color recognition system was developed. In the course of experimental studies, it was found that the RMSE and sensitivity indicators have better results in relation to other systems.

Forecasting of Canopy Temperatures Using Machine Learning Algorithms

Highlights This study analyzes the feasibility of using Artificial Neural Networks (ANNs) to estimate canopy temperatures. A methodology is introduced to forecast canopy temperatures using historical canopy temperatures. ANNs can predict canopy temperatures with satisfactory accuracy for plant stress-based irrigation scheduling. The methodology can be useful to add redundancy to plant stress-based irrigation scheduling methods. Abstract. Recent advances can provide farmers with irrigation scheduling tools based on crop stress indicators to assist in the management of Variable Rate Irrigation (VRI) center pivot systems. These tools were integrated into an Irrigation Scheduling Supervisory Control and Data Acquisition System (ISSCADAS) developed by scientists with the USDA-Agricultural Research Service (ARS). The ISSCADAS automates the collection of data from a network of wireless infrared thermometers (IRTs) distributed on a center pivot’s lateral and in the field irrigated by the center pivot, as well as data from a wireless soil water sensor network and a microclimate weather station. This study analyzes the use of Artificial Neural Networks (ANNs), a type of machine learning algorithm, for the forecasting of canopy temperatures obtained by a wireless network of IRTs mounted on a three-span VRI center pivot irrigating corn near Bushland, TX, during the summer of 2017. Among the predictors used by the ANNs were weather variables relevant to the estimation of evapotranspiration (i.e., air temperature, relative humidity, solar irradiance, and wind speed), irrigation management variables (irrigation treatment, irrigation scheduling method, and the amount of water received during the last 5 days as irrigation or rainfall), and days after planting. Two case studies were conducted using data collected from periodic scans of the field performed during the growing season by running the pivot dry. In the first case, data from the first three scans were used to train an ANN, and canopy temperatures estimated using the ANN were then compared against canopy temperatures measured by the network of IRTs during the fourth scan. In the second case, data from the first six scans were used to train ANNs, and canopy temperatures estimated using the ANN were then compared against canopy temperatures measured by the network of IRTs during the seventh scan. The Root of the Mean Squared Error (RMSE) of ANN predictions in the first case ranged from 1.04°C to 2.49°C, whereas the RMSE of ANN predictions in the second case ranged from 2.14°C to 2.77°C. To assess the impact of ANN accuracy on irrigation management, estimated canopy temperatures were fed to a plant-stress-based irrigation scheduling method, and the resulting prescription maps were compared against prescription maps obtained by the same method using the canopy temperatures measured by the network of IRTs. In the first case, no difference was found between both prescription maps. In the second case, only one plot (out of 26) was assigned a different prescription. Results of this study suggest that machine learning techniques can be used to assist the ISSCADAS in situations where canopy temperatures cannot be measured by the network of IRTs due to poor visibility conditions, or because the center pivot cannot traverse the field within a reasonable amount of time. Keywords: Artificial Neural Network, Canopy temperature sensing, Center pivot irrigation, Irrigation scheduling, Machine learning, Metamodeling, Variable rate irrigation.

¿PUEDEN LAS MEDIDAS OSTEOMÉTRICAS PREDECIR EL PESO VIVO Y CARACTERÍSTICAS DE LA CANAL EN CONEJOS NUEVA ZELANDA BLANCA EN CRECIMIENTO?

<p><strong>Background</strong>: The identification of the relationship between the particular traits of the body development of the rabbit could help the understanding of the biological growth and the application in different areas of livestock and veterinary study. <strong>Objective</strong>. To evaluate the relationship between osteometry of the limbs, live weight (LW), hot carcass weight (HCW) and anatomical cuts; and generate equations to predict PV, PCC and anatomical cuts in New Zealand White (NZW) rabbits at different ages. <strong>Methodology</strong>. A total of 113 rabbits of seven different ages (9, 11, 14, 17, 20, 24 and 28 weeks) were used. The data obtained were LW, carcass characteristics: PCC and anatomical cuts (front, middle and back); and osteometry: length and diameter of the humerus (LH and DH), radio-ulna (LR-C and DR-C), femur (LF and DF) and tibia-fibula (LT-P and DT-P). The information was analyzed using Pearson's correlation coefficients (r) and linear and multiple regression models. The accuracy of the models was evaluated using the coefficients of determination (r2) and the square root of the mean square error (RCCME). <strong>Results</strong>. The correlations expressed between LW, carcass characteristics and bone lengths were higher (r= 0.90 – 0.95; p= < 0.0001) compared to diameters (r= 0.38 – 0.72; p= < 0.0001). The regression equations for PV had a RCCME that varied from 186.86 to 197.70 g using LR-C and LF. While for the PCC it presented RCCME from 130.4 to 138.58 g with LH, LR-C and DH. Regarding the anatomical cuts, the progressive order of rear, middle and front using LF and LH was RCCME= 21.66 to 25.67. The LF and LH explained 92 and 91% of the variation of the PV and PCC, although it decreased slightly in the anatomical cuts from 85 to 92%. <strong>Implications</strong>. The osteometry of the extremities reflects security in the application of equations to calculate general and particular data of body mass in the rabbit, this contribution of knowledge can be extrapolated for animal production and forensic veterinary studies. <strong>Conclusion</strong>. The close associations obtained between limb lengths (exception LT-P) with PV, PCC and anatomical cuts gave equations with high capacity to estimate these body changes of growing NZB rabbits.</p>

Predicción de la contaminación atmosférica generada por las emisiones del CO2 en el Perú utilizando los métodos ARIMA y Redes Neuronales

Carbon Dioxide is the main greenhouse gas (GHG) that leads to global warming and consequently, climate and environmental change. It brings negative effects to economic development, human life and the environment. It is extremely important to be able to accurately measure and predict the emission of carbon dioxide, since this way we can carry out a good sustainable policy for our environment. The main objective of this research is to find the best prediction model of carbon dioxide (CO2) emissions in Peru, through the comparative evaluation of the ARIMA and Artificial Neural Networks methods. The annual data of the emissions of (CO2) from the World Bank were used, which were analyzed by programming the free software R studio. To determine the best model, forecast errors such as: square root of the mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used. The results reveal that the most appropriate model between these two methods for predicting (CO2) emissions in Peru is the neural network ANN (5-10-1), that is, the neural network with five lagged values as input connected by ten nodes in the hidden layer and a single output layer, which had higher precision with RMSE = 1125.82, MAE = 1040.68 and MAPE = 1.90 in the test phase compared to the best ARIMA model (0,1,10) that had an RMSE = 4223.73, MAE = 3143.40 and MAPE = 5.80 in the test phase. In conclusion, neural networks can be used to predict emissions of (CO2), which clearly showed that annual emissions of (CO2) in Peru will increase in the coming years. These real insights will be useful for policy makers to bring significant changes in major environmental areas in our country

MIXED-EFFECT MODELS FOR VOLUMETRIC ESTIMATION OF LUMBER FROM NATIVE AMAZONIAN SPECIES

The objective of the present work is to test fixed effect models and compare them with different configurations of mixed models to predict lumber volume. The database came from a medium-sized sawmill located in the rural area of the municipality of Porto Grande, Amapá, Brazil. A total of 50 logs of 9 native species were used, with their volume being measured by the Smalian method. A fixed effect model proposed by Lima et al. (2018) was fitted andcompared to mixed linear models with species as a random effect. The fitting of the fixed model and the mixed models were performed using the maximum restricted likelihood method. Akaike information criteria (AIC), correlation coefficient, Maximum Likelihood Ratio Test, Square Root of the Mean Square Error (RMSE), absolute mean distance and bias were used to select the best model. According to the established criteria, the mixed models obtained better fits when compared to the fixed model, providing a reduction in the RMSE of 0.3 for an average between the mixed models of 0.26. According to the results achieved, the mixed linear models were more efficient and accurate for volume estimation. The M7 mixed model was the best fit model. The use of mixed models enables greater accuracy in estimating volume.

Development of Adaptive Neuro-Fuzzy Inference System for Assessing Industry Leadership in Accident Situations

Petroleum activity is characterized as a high-risk activity due to the probability of accidents with material and human losses. The leaders of this segment assume, besides the complex routine tasks, the challenge of making assertive decisions during an accident. This study aims to present an evaluation model of the Industry Leadership Index for Emergencies Situations ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ILIE</i> ), using the Adaptive Neuro-Fuzzy System (ANFIS). The model was composed of 4 input variables, namely: knowledge, behavior, skill, and attitude; and one output variable, Industry Leadership. The data collection took place in petroleum production units in Brazil, with a sample of 151 respondents through the application of a survey. The observed data were treated in an Excel tabulator and used in the development of the ANFIS model. From this model, it was possible to carry out simulations to predict the impact, which the increase or decrease in the value of each input variable can influence the leader’s profile. The model performed satisfactorily in the Root of the Mean Square Error (RMSE) analysis, being 0.199 in data training and 1.217 in data verification. The results suggest that the ANFIS method can be successfully applied to establish a model to analyze industry leaders prepared for assertive responses in crisis scenarios.

AN APPLICATION OF THE POISSON-WEIBULL MODEL FOR CENSURED DATA

In this paper, we proposed the Poisson-Weibull distribution for the modeling of survival data. The motivation to study this model since, in addition to generalizing the Weibull distribution, which is widely used in several areas of knowledge among them the Survival and Reliability analysis, it presents great exibility in the forms of the hazard function. The Poisson-Weibull distribution was created in a composition of discrete and continuous distributions where there is no information about which factor was responsible for the component failure, only the minimum lifetime value among all risks is observed. The maximum likelihood approach was used to estimate the parameters of the model. Also was conducted a simulation study to examine the mean, the bias, and the root of the mean square error of the maximum likelihood estimates of the proposed model according to the censoring percentages and sample sizes. The model selection criteria were also applied, in addition to graphic techniques such as TTT-Plot and Kaplan-Meier. Application to the real data set was used to illustrate the usefulnessof the distribution.

Cinética de extracción acuosa de compuestos fenólicos a partir de hojas de yerba mate elaborada

In this study, the kinetics of aqueous extraction of phenolic compounds from the yerba mate leaves were analyzed. A temperature range between 40°C and 70°C and a ratio of 25 g of mate leaves in 200 mL of water were used. The extraction kinetic was studied adjustment of the kinetics models of first order, second order kinetic model and the second order diffusive model. The goodness of the fit was controlled by the magnitude of the coefficient of determination (R2), the percentage error (EP %) and the square root of the mean square error (RMSE).The second order kinetic models adequately described the extraction process, achieving the best fit with the second order diffusive model (EP%= 0,81 to 2,9; EP%= 11,24 to 14,99 and 4,72 and 6,92) that provided important information about the initial extraction process and the mechanism that occurs in the final extraction stage. The temperature influenced the kinetic parameters; however, the final equilibrium concentration of phenolic compounds was not affected by it. The results of this study allow us to obtain better overall knowledge of the times and temperatures for improved extraction rates and energy consumption for the industrialization of these compounds.

Readily dispersible clay in soils from different Brazilian regions by visible, near, and mid-infrared spectral data

ABSTRACT Unstable soil aggregates can easily be dispersed when mechanical energy is applied, denominated as readily dispersible clay (RDC) which is usually measured by turbidimetry. Faster methods that require minimum preparation at reduced costs are needed, such as the spectroscopy technique. Nevertheless, the technique has not yet been tested on soils. Electromagnetic radiation has strong interaction with soil physical elements (sand, silt, and clay) and with selective elements of the clay fraction, such as mineralogy. Thus, this study assessed the potential of 400–16,680 nm range (visible – Vis; near-infrared – NIR; short-wave infrared – SWIR; and medium infrared – MIR) spectroscopy to estimate RDC in some tropical soils. We also evaluated the wavelengths and regions that contribute to this prediction. We collected 68 samples from the underlying horizon of three different soils from three important states in Brazil The soil analyses were performed (physical and chemical) in the laboratory. RDC quantification was compared with the Vis-NIR-SWIR and MIR wavelengths models. The random forest (RF) algorithm was used to generate the prediction in the soil database. For RDC, the Vis-NIR-SWIR and MIR configuration presented a better relationship (coefficient of correlation (r) = 0.659 and root of the mean square error (RMSE) = 0.469%), although the results were similar when the regions were evaluated separately (Vis-NIR-SWIR – 350 to 2500 nm and MIR 2500 to 16,680 nm). RDC determination is more related to bands from 1980 to 2040, 6840 to 7360, and 7750 to 9820 nm. Spectroscopy showed its importance in RDC quantification, as it is fast and does not require soil preparations. The results show that spectral wavelengths are appropriate to assist on the estimative of RDC in the studied soils and may provide a basis for future research.

Reproducing vertical human walking loads on rigid level surfaces with a damped bipedal inverted pendulum

This study is motivated by the lack of a systematic way to reproduce vertical human walking loads with a damped bipedal inverted pendulum (DBIP). Measurements of vertical human walking loads for a sample of 16 Chinese test subjects were carried out with a non-commercial force plate on rigid surfaces, in which 2393 single-step forces were recorded and analyzed. The mean value of all the test subjects’ average step frequency of free walking is 1.73 Hz, which is lower than the reported data for other populations. Most test subjects are naturally slow walkers, whose first four harmonics dynamic load factors are found to be less than the well-reported data. Only the first harmonic dynamic load factor has a clear trend to increase with the step frequency. The best linear fitting curve is given in the form of y = a(x + b), in which a = 0.24 and b = −0.82. A previously developed DBIP, which can be described by four dimensionless parameters, i.e. the dimensionless energy input, leg stiffness, leg damping, and the attack angle, is then applied to reproduce these measured forces. The simulated force with a minimum root of the mean square error to the measured force is selected and calibrated to the required step frequency and amplitude, which usually has a smaller peak force compared with the measured data. The adopted model inputs are illustrated as functions of step frequencies. The DBIP is applicable for modelling part of the test subjects’ walking loads on rigid surfaces at different step frequencies.

IndoorPlant: A Model for Intelligent Services in Indoor Agriculture Based on Context Histories.

The application of ubiquitous computing has increased in recent years, especially due to the development of technologies such as mobile computing, more accurate sensors, and specific protocols for the Internet of Things (IoT). One of the trends in this area of research is the use of context awareness. In agriculture, the context involves the environment, for example, the conditions found inside a greenhouse. Recently, a series of studies have proposed the use of sensors to monitor production and/or the use of cameras to obtain information about cultivation, providing data, reminders, and alerts to farmers. This article proposes a computational model for indoor agriculture called IndoorPlant. The model uses the analysis of context histories to provide intelligent generic services, such as predicting productivity, indicating problems that cultivation may suffer, and giving suggestions for improvements in greenhouse parameters. IndoorPlant was tested in three scenarios of the daily life of farmers with hydroponic production data that were obtained during seven months of cultivation of radicchio, lettuce, and arugula. Finally, the article presents the results obtained through intelligent services that use context histories. The scenarios used services to recommend improvements in cultivation, profiles and, finally, prediction of the cultivation time of radicchio, lettuce, and arugula using the partial least squares (PLS) regression technique. The prediction results were relevant since the following values were obtained: 0.96 (R2, coefficient of determination), 1.06 (RMSEC, square root of the mean square error of calibration), and 1.94 (RMSECV, square root of the mean square error of cross validation) for radicchio; 0.95 (R2), 1.37 (RMSEC), and 3.31 (RMSECV) for lettuce; 0.93 (R2), 1.10 (RMSEC), and 1.89 (RMSECV) for arugula. Eight farmers with different functions on the farm filled out a survey based on the technology acceptance model (TAM). The results showed 92% acceptance regarding utility and 98% acceptance for ease of use.

Modeling of Lake Malombe Annual Fish Landings and Catch per Unit Effort (CPUE)

Forecasting, using time series data, has become the most relevant and effective tool for fisheries stock assessment. Autoregressive integrated moving average (ARIMA) modeling has been commonly used to predict the general trend for fish landings with increased reliability and precision. In this paper, ARIMA models were applied to predict Lake Malombe annual fish landings and catch per unit effort (CPUE). The annual fish landings and CPUE trends were first observed and both were non-stationary. The first-order differencing was applied to transform the non-stationary data into stationary. Autocorrelation functions (AC), partial autocorrelation function (PAC), Akaike information criterion (AIC), Bayesian information criterion (BIC), square root of the mean square error (RMSE), the mean absolute error (MAE), percentage standard error of prediction (SEP), average relative variance (ARV), Gaussian maximum likelihood estimation (GMLE) algorithm, efficiency coefficient (E2), coefficient of determination (R2), and persistent index (PI) were estimated, which led to the identification and construction of ARIMA models, suitable in explaining the time series and forecasting. According to the measures of forecasting accuracy, the best forecasting models for fish landings and CPUE were ARIMA (0,1,1) and ARIMA (0,1,0). These models had the lowest values AIC, BIC, RMSE, MAE, SEP, ARV. The models further displayed the highest values of GMLE, PI, R2, and E2. The “auto. arima ()” command in R version 3.6.3 further displayed ARIMA (0,1,1) and ARIMA (0,1,0) as the best. The selected models satisfactorily forecasted the fish landings of 2725.243 metric tons and CPUE of 0.097 kg/h by 2024.

Influence of climate type on the predictive capabilities of stochastic models applied to monthly dam inflows

This contribution assesses the predictive capacity of monthly inflow of two stochastic models called autoregressive integrated moving average (ARIMA) and TBATS and highlights the influence of climate type on their performances. These are the inflows to three dams in three distinct climates: semi-arid, subhumid and humid. The actual inflows are deduced from the water balance equation for 132-month period. The first ten corresponding years of each series are used for training of the two models and the last one is then used for test. Model performances are evaluated using three commonly used metrics: the square root of the mean square error (RMSE), the mean of the absolute errors (MAE), and the mean absolute error in percentage (MAPE). The results show that the TBATS model performs better than the ARIMA model and its predictive capabilities decrease depending on whether the climate is semi-arid, sub-humid and humid (MAPE = 50.47%, 34.79% and 29.99%, respectively).

Comparison of Digital Elevation Models for the designing water reservoirs: a case study Pskom water reservoir

This study investigates the accuracy of various DEMs (SRTM DEM, ASTER GDEM, and ALOS PALSAR DEM) for the area of the designing Pskom water reservoir (recommended to construction in Pskom River, in Tashkent region. DEMs are compared for the study area using the Global Mapper application and selection Ground Control Points (GCP). The RMSE we calculate is the most easily interpreted statistic as the square root of the mean square error because it has the same units as the quantity drawn on the vertical axis. Results show that SRTM based measurements of ground control points (GCPs) exhibit RMSE of 15.72 m while ASTER DEM based measurements exhibits and RMSE of 18.47 m, ALOS PALSAR exhibit RMSE of 14.02 m for the Water reservoir located in the plain. There are AOS PALSAR outperforms SRTM and ASTER DEM in detecting vertical accuracy. Based on the capabilities of the Global Mapper program, we can build the longitudinal profile of the approximate location where the dam can be built in each DEM and compare. The results obtained show that the dam height is 187 m at ALOS PALSAR DEM, 168 m at ASTER GDEM, and 175 m at SRTM. The study found that using ALOS PALSAR data in the design of the proposed Pskom Reservoir for construction leads to a more accurate result. Comparing the DEMs data shows that there is more difference between the vertical accuracy; the horizontal accuracy level is almost the same. The results were obtained using ALOS PALSAR data in determining the storage volume (W=479368568 m3) and area (F=8.31 sq., km) of the water reservoir.