Historical Series Data Research Articles

Assessing the history-based predictability of regional monthly precipitation data using statistical and fuzzy methods

This paper points out two procedures for verifying the statistical independence between the historical and actual data series. They are based respectively on the computation of an empirical distance correlation coefficient, and the use of Hamming distance between binary encoded phase signals corresponding to Log-Gabor filtered time-series. Both procedures are applied to the monthly precipitation series recorded during 480 successive months at 49 meteorological stations in Dobrogea region (Romania) and on 4 and 5-valued fuzzified versions of the initial data represented using linguistic labels. The results show that the crisp data and their fuzzified versions cannot support the hypothesis of history-based predictability from their history. Hence, the two statistical independence tests are robust with respect to the k-means fuzzification of data and cross-validate each other, being applicable to any long-term analysis of precipitation series, to any other signals in general, and to their fuzzified versions, as well.

Drought events analysis and spatial distribution for the states of Rio Grande do Norte and Paraiba, Brazil

The application of the Standardized Precipitation Index was established for 13 rain gauge stations selected in the catchment sub-basins of number 37 and 38, which correspond to the area of the states Rio Grande do Norte and Paraiba. The monthly rainfall data of the stations was analyzed, and the missing data was filled by simple linear regression. A 6-month time scale was adopted for the method; the data was organized in a moving cumulative distribution of precipitation and applied to the Standardized Precipitation Index (SPI). In the application of the method, the cumulative values are processed into a normal distribution with the mean zero and variance one. That way, a drought event begins when the cumulative precipitation in the historical data series reaches an SPI value smaller than or equal to -1.00. Between 22 and 26 events for the stations of the sub-basin 37, and between 18 and 22 events for the stations of sub-basin 38 were identified. Medium-term tendencies were observed, with the occurrence of events every year or two, proving certain seasonality. The obtained values were then transformed into a return period of 20 years, and an isohyetal map was developed with the purpose to show the spatial variation of the event precipitation. By the outlined map, becomes evident an area of higher precipitation rates, situated on the coast along the states, and a drier region, located in the inland territory.

Constraint in the midst of growth: decadal-scale Adélie penguin (Pygoscelis adeliae) population change at Scullin and Murray Monoliths diverges from widespread increases across East Antarctica

Understanding spatio-temporal variation in the abundance of species’ populations is an issue of central importance in ecology. Recent studies of decadal-scale change in Adelie penguin (Pygoscelisadeliae) populations around the Antarctic continent have led to insights into our understanding of the species’ population dynamics and to exploration of the ecosystem processes driving them. However, gaps in knowledge of population change still exist for many populations, especially at breeding sites distant from research stations. In this study, we estimate population change for the large Adelie penguin populations at the seldom visited and data-poor Scullin and Murray Monoliths in East Antarctica by critically assessing historical population data and extending the historical time series of population data to the present day with a recent survey. Unlike Adelie penguin populations studied elsewhere in East Antarctica, we find the Monolith populations have remained stable or decreased slightly over the past 3–4 decades. The results indicate that spatially divergent processes are driving Adelie penguin population dynamics, and that these factors may not be sufficiently represented in current explanatory or predictive population models for this species. The methods employed here provide a viable means of undertaking regular population monitoring of highly visible seabird species such as the Adelie penguin in remote and ecologically important areas such as Scullin and Murray Monoliths in the future.

Distribution of rainfall probability in the Tapajos River Basin, Amazonia, Brazil

Studies on the probability of rainfall and its spatiotemporal variations are important for the planning of water resources and optimization of the calendar of agricultural activities. This study identifies the occurrence of rain by first-order Markov Chain (MC) and by two states in the Tapajos River Basin (TRB), Amazon, Brazil. Cluster analysis (CA), based on the Ward method, was used to classify homogeneous regions and select samples for checking the probability of rainfall occurrence by season. The historical series of daily rainfall data of 80 stations were used for the period 1990-2014. The CA technique identified 8 homogeneous regions and their probability of occurrence of rainfall, helping to determine which regions and periods have greater need of irrigation. Results of the probability of occurrence of dry and rainy periods in the TRB were used to define the dry (May thru September) and rainy seasons (October thru April). Elements of the matrix transition probabilities showed variability in relation to time and, in addition, the influence of geographical position of seasonal rainfall in determining dry and rainy periods at specific sites in the TRB.

Capacity credits of wind and solar generation: The Spanish case

This paper analyses the capacity credits (CCs) of renewable photovoltaic (PV), concentrated solar power (CSP) and wind technologies in the Spanish power system. This system has steadily increased the share of renewables, reaching a penetration level of over 30%. The predictions made by ENTSO-e suggest that this level will increase to 50% by 2030. Therefore, different scenarios are studied in this paper to investigate the evolution of renewable integration and assess the corresponding contributions to reliability. The assessment is performed using a sequential Monte Carlo (SMC) method considering the seasonality of renewable generation and the uncertainties related to renewable sources, failure issues and the maintenance of thermal-based units. The baseline for SMC is provided by historical annual time series of irradiance and wind power data from the Spanish system. In the solar case, these time series are transformed into power time series with models of CSP and PV generation. The former includes different thermal storage strategies. For wind generation, a moving block bootstrap (MBB) technique is used to generate new wind power time series. The CC is assessed based on the equivalent firm capacity (EFC) using standard reliability metrics, namely, the loss of load expectation (LOLE). The results highlight the low contribution of renewables to power system adequacy when the Spanish power system has a high share of renewable generation. In addition, the results are compared with those of similar studies.

A Vehicle Type Dependent Car-following Model Based on Naturalistic Driving Study

In this paper, a car-following model considering the preceding vehicle type is proposed to describe the longitudinal driving behavior closer to reality. Based on the naturalistic driving data sampled in real traffic for more than half a year, the relation between ego vehicle velocity and relative distance was analyzed by a multi-variable Gaussian Mixture model, from which it is found that the driver following behavior is influenced by the type of leading vehicle. Then a Hidden Markov model was designed to identify the vehicle type. This car-following model was trained and tested by using the naturalistic driving data. It can identify the leading vehicle type, i.e., passenger car, bus, and truck, and predict the ego vehicle velocity and relative distance based on a series of limited historical data in real time. The experimental validation results show that the identification accuracy of vehicle type under the static and dynamical conditions are 96.6% and 83.1%, respectively. Furthermore, comparing the results with the well-known collision avoidance model and intelligent driver model show that this new model is more accurate and can be used to design advanced driver assist systems for better adaptability to traffic conditions.

Stochastic Estimation of Potential and Depleted Productivity of Soybean Grain and Oil

Soybean is one of the most important food crops around the world. Despite its economic importance, the effect of sowing date and climatic conditions on soybean development and productivity of grain and oil has not yet been studied in detail. Such a study can yield valuable information regarding the interaction of this crop with its environment. In this context, the aim of this study was to estimate the soybean potential productivity of grains and oil for eight sowing dates, using historical series of climate data from Piracicaba (SP), Brazil. For this a stochastic model is proposed, with truncated normal distribution for maximum, minimum and average temperature data. The information generated in this study is important in order to provide farmers with relevant information about the importance of an adequate sowing date, as well as to further understand of the influence of the climate variations on soybean production. The September 1 sowing date, without lack of water, provides more adequate conditions for accumulated dry matter, grain and oil productivity. It was concluded that: (i) the stochastic model (Potential and Depleted Model for Soybean Grain and Oil Production) can be used as a valuable tool for analyzing variability and complex interactions between plants and climate conditions. In this model, the air temperature and global radiation parameters were adequate to estimate the duration of the crop cycle, dry matter production, photoassimilates partition, and grain and oil productivity of soybean plants grown in different sowing dates in Piracicaba–SP; (ii) considering the historical data of air temperature, global radiation and other optimal ecophysiological needs for the crop, the model results define September as best period for sowing soybean in Piracicaba (SP), Brazil.

A Forecasting Framework Based on Kalman Filter Integrated Multivariate Local Polynomial Regression: Application to Urban Water Demand

In this study, a forecasting framework for daily urban water demand has been proposed. It was developed based on the extended Kalman filter (EKF) which consists of state estimation, forecasting and error correction. The forecasting and error correction models can be substituted. As an example, a multivariate local polynomial regression (MLPR) was used to linearize the complex system which is essential for EKF. A correctional prediction of residual based on relevance vector regression was employed to update and substitute error estimation value in the EKF. To improve the precision of the forecasts, the historical data series was decomposed into low- and high-frequency subseries using discrete wavelet transformation. Five category forecasts with the lead time of 1-day were assessed in comparison of the proposed model: MLPR, multi-scale relevance vector regression, autoregressive moving average, Back Propagation neural network and multiple linear regression. According to the performance criteria, the MLPR is slightly beneficial in capturing the basic dynamics of the daily urban water demand in the short term, but the state estimation and error correction can greatly improve the results. The proposed model obtains better forecasting performances than existing models, which is attributed to good state estimation from the Kalman transmission gain and favorable feature learning performance using MLPR.

A dryland cover state mapping using catastrophe model in a spectral endmember space of OLI: a case study in Minqin, China

ABSTRACT Alternative state theory with catastrophe conceptual model is very attractive for dryland zero net land degradation (ZNLD) management, but practically challenging for mapping cover state and pathway with conventional approaches. This paper developed a methodological framework in the generic spectral endmember space (SVD, soils and substrates, vegetation, and dark surface endmembers ) of Landsat-8 OLI (operational land imager) imagery to concretize the catastrophe model of dryland cover state based on the co-development of plant and soil variables in a landscape. A cover function index (LCI) was proposed together with cover type growth form as cover variables in multi-seasonal SVD space. Furthermore, ecological site (ES) and topographic wetness index (TWI) were used as the landscape variables. The four variables as the explanatory variates, soil organic matter (SOM) as a slow response variable of dryland cover state, were explored by classification and regression trees (CART) for the state space. Using the Minqin dryland system in China as a case study, CART explored 65.9% amounts of SOM variance of 85 training samples with the four explanatory variables, and the measured SOM had liner relationship with CART predicted SOM at 0.05 significant level with the explained 39% SOM variance for 10 test samples. In the learned transparent tree, the two alternative stable states were identified correctly and validated by another soil intrinsic clay content variable independently, one was the current highest average SOM (26.757 g kg−1) of perennial forest/shrub covers, the other was the bared or sparse vegetation cover with low average SOM (≤8.518 g kg−1) as a critical SOM limit in a given ecological site type. The mean clay content of the undesirable alternative states was all below 60 g kg−1 with severe soil physical degradation, and had a clear breaking gap with other states. Moreover, the SOM-LCI plane projected the unstable states being folded in the basins of the attraction of two alternative stable states (i.e. 0.39–0.76 LCI) with relative resilience analysis of pathways. In the 10 test sites, only 1 site of the state was not predicted correctly. The results suggest the model has a level of predictability for dryland cover state and its pathway. According to the estimated states, there has 12.74% study area improved and continued to recovering the desirable states, and the study area has already net improvement at the landscape level. Therefore, without the comparable series of historical data for adaptive management, the developed framework in SVD of Landsat imagery is a cost-effective and time-saving tool, which helps managers to assess and develop appropriate management with a more spatially diversified strategy for zero net land degradation using a space-for-time substitution in an era of unprecedented change.

Relevance and Scale Dependence of Hydrological Changes in Glacierized Catchments: Insights from Historical Data Series in the Eastern Italian Alps

Glaciers have an important hydrological buffering effect, but their current rapid reduction raises concerns about future water availability and management. This work presents a hydrological sensitivity analysis to different climatic and glacier cover conditions, carried out over four catchments with area between 8 and 1050 km2, and with glacierization between 2% and 70%, in the Italian Alps. The analysis is based on past observations, and exploits a unique dataset of glacier change and hydro-meteorological data. The working approach is aimed at avoiding uncertainties typical of future runoff projections in glacierized catchments. The results highlight a transition from glacial to nival hydrological regime, with the highest impacts in August runoff over smaller catchments. The buffering effect of current glaciers has largely decreased if compared to the Little Ice Age, up to 75% for larger catchments, but it is still important during warm and dry summers like 2003. We confirm a non-linear relationship between glacier contribution in late summer and catchment area/percent glacierization. The peak in runoff attributable to glacier melt, expected in the next 2–3 decades on highly glacierized alpine catchments, has already passed in the study area.

Efficacy of Rainfall-Runoff Models in Loose Coupling Spacial Decision Support Systems Modelbase

The need for a prognosis for water resources management in Brazil means that studies with hydrological models are required, as part of a Spatial Decision Support System (SDSS). Choosing the model in a modelbase requires knowledge of the performance of the existing models in the specific situations in which they are to be applied. This paper evaluated the performance of two physically-based models and two numerical-based models in their ability to represent the rainfall-runoff process. The study occurred in a watershed in southern Brazil. Historical data series from the same periods were taken to calibrate and train the models, using two different periods to validate their efficacy. The results show that the SWAT and TOPMODEL models presented an inferior performance compared to the numerical-based models (RT and ANN). However, all the models presented satisfactory levels of efficacy and the potential for use in different management situations.

A TEMPORAL HISTORICAL ANALYSIS ON HOW SELECTIVE COLLECTION OF RECYCLABLE WASTE IS INFLUENCED BY EDUCATION-RELATED AND REGIONAL FACTORS AT A UNIVERSITY

The aim of the study was to determine the relation between the composition of university recyclable material collected (plastic, paper, metal and glass), education-related factors such as the academic calendar and work stoppages, and regional factors such as climate, regional economy and composition of the urban waste collected over an 8-year period. The study was conducted based on the composition of recyclable dry solid wastes at the Federal University of Rio de Janeiro, quantified after their selective collection between 2010 and 2017. The average proportions of the materials collected were: 63% paper; 19% metal; 8% plastic; 5% glass; and 5% other materials with recycling potential, including Tetrapak packaging. The amount of waste was around 20% lower during vacations and long weekends compared to academic periods. There was also a relation between local ambient temperature and plastic composition, which declined in cooler months. The recyclable materials collected in Rio de Janeiro, where the university is located, have different compositions from those of the university, with 41% plastic, 34% paper, 3% metal, 7% glass and 14% of other recyclable materials. For the historical series assessed, it remained unclear whether the economy affected the composition and amount of waste collected. A significant amount of paper was collected at the academic facility, so efforts to decrease its use and/or increase its recycling should be prioritized. This work was one of the first to evaluate an 8-year historical series of waste data collected within a university.

Model rainfall-runoff in the constraints Amazon

The aim of this study was based on different samples of settings for calibration and validation of IPH II model, using limited historical series of data of daily average water flow registered in Caiabi river hydrologic basin, a tributary stream of Teles Pires river- Mato Grosso. The total area of the study location is 440.98 km2, and there were installed three meteorological automatic stations for climate monitoring, and one linigraph in the basin end for monitoring the altimetric quotas and also to estimate the daily water flow of the river. The precipitation data, evapotranspiration and water flow used to feed the IPH II model were collected between 09/18/2015 and 04/30/2016, using sixty percent of the initial historical data to calibrate and forty percent to validate the model has shown better statistic performance, however rearranging the data and establishing the sixty percent of the central data for the calibration was verified that there was an increase in the statistical performance of the model making the simulations of the IPH II model were successful. It was indicated by the results that the sample methodology for calibration of hydrologic models can bring substantial improvement to the performance

Wind power forecasting based on time series ARMA model

Destruction of the ecological environment such as global warming and poor atmospheric pollution has forced carbon emission reduction and renewable energy utilization to accelerate. Under this circumstance, wind power generation as a clean renewable energy source has been valued by many countries and has increased globally, and domestic wind power generation is also being developed and promoted on a large scale. In order to further study the intermittent and unstable wind power, this paper uses the autoregressive moving average (ARMA) model based on time series algorithm to predict short-term wind power. The ARMA model is established based on the time series of wind power historical data, and the data is subjected to ADF test to prove the rationality of the selected data. The prediction model is analyzed and verified by using the measured wind power data of a wind farm from 0:00 am to 23:45 on the 1st of the month. In this paper, the prediction error is analyzed by MAE, RMSE and MdAPE. The analysis results show that the proposed model has less prediction error. The wind power prediction method based on ARMA model can effectively predict the wind power.

Impact of conservation agriculture on the agronomic and environmental performances of maize cropping under contrasting climatic conditions of the Brazilian Cerrado

We calibrated the crop model STICS and used it in a simulation experiment to assess the effect of no-tillage cropping systems, with and without millet as cover crop (NTM and NT), on the water-limited yield of maize, its inter-annual variability, water runoff and drainage below the root zone, compared to conventional tillage-based cropping systems (CT). We ran simulations for two contrasting climates in the Brazilian Cerrado, Goiânia with 1600 mm of annual rain and Barreiras with 1160 mm, using a series of historical weather data and considering two soil types with different soil water storage capacities (i.e. different maximum rooting depths of 90 and 180 cm). We identified the best suitable sowing windows based on agronomic (yield and yield variability) and environmental (runoff and drainage) performances of the cropping systems. Compared to CT, NT and NTM systems increased water-limited maize grain yield and its stability. For example, for the simulations with 90 cm set as maximum rooting depth, grain yields for CT, NT and NTM, averaged over all sowing dates, were 3.41, 4.35 and 4.28 t ha−1 in Goiâna, and 7.02, 7.67 and 7.5 t ha−1 in Barreiras. No-tillage systems markedly decreased water runoff and hence erosion risks. For example, simulated runoff was on average 4% of the annual rain for NT and NTM against 13% for CT at both locations. In contrast, no-tillage systems increased water drainage below the rooting zone, hence with risks of increased nutrient leaching. For example, for the 90 cm maximum rooting depth, simulated drainage was 48% and 41% of the annual rain for NT and NTM against 32% for CT in Goiânia, and 29% and 26% against 17% in Barreiras. Reconciling the objective of maximizing yield and its stability with that of minimizing both water runoff and drainage was only achieved under no-tillage with millet as cover crop (NTM) for a very narrow range of sowing dates. Sowing all fields of the farm within a narrow optimal window requires, however, important investment in machinery and preferential allocation of available farm resources for sowing activities at the start of the cropping season. This must be weighed against other farm activities.

A Hybrid Forecasting Method for Solar Output Power Based on Variational Mode Decomposition, Deep Belief Networks and Auto-Regressive Moving Average

Due to the existing large-scale grid-connected photovoltaic (PV) power generation installations, accurate PV power forecasting is critical to the safe and economical operation of electric power systems. In this study, a hybrid short-term forecasting method based on the Variational Mode Decomposition (VMD) technique, the Deep Belief Network (DBN) and the Auto-Regressive Moving Average Model (ARMA) is proposed to deal with the problem of forecasting accuracy. The DBN model combines a forward unsupervised greedy layer-by-layer training algorithm with a reverse Back-Projection (BP) fine-tuning algorithm, making full use of feature extraction advantages of the deep architecture and showing good performance in generalized predictive analysis. To better analyze the time series of historical data, VMD decomposes time series data into an ensemble of components with different frequencies; this improves the shortcomings of decomposition from Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) processes. Classification is achieved via the spectrum characteristics of modal components, the high-frequency Intrinsic Mode Functions (IMFs) components are predicted using the DBN, and the low-frequency IMFs components are predicted using the ARMA. Eventually, the forecasting result is generated by reconstructing the predicted component values. To demonstrate the effectiveness of the proposed method, it is tested based on the practical information of PV power generation data from a real case study in Yunnan. The proposed approach is compared, respectively, with the single prediction models and the decomposition-combined prediction models. The evaluation of the forecasting performance is carried out with the normalized absolute average error, normalized root-mean-square error and Hill inequality coefficient; the results are subsequently compared with real-world scenarios. The proposed approach outperforms the single prediction models and the combined forecasting methods, demonstrating its favorable accuracy and reliability.

Travel time prediction for highway network based on the ensemble empirical mode decomposition and random vector functional link network

Travel time prediction supplies data support for the management and operation of the highway network. To deal with this problem, a model based on ensemble empirical mode decomposition and random vector functional link network is proposed in this paper. Ensemble empirical mode decomposition is firstly employed to decompose the complex travel time data series into several simple functions, which are then represented by the same number of random vector functional link networks. Finally, the outputs of all networks are combined by linear addition as the prediction results. A historical travel time data series (from 1 August 2016 to 1 November 2016) of two highways in China is investigated by the proposed models. For comparison, five individual prediction models and their respective ensemble variants are implemented for the same task. The results show that the proposed model outperforms all the other models in terms of symmetric mean absolute percentage error and normalized root mean square error. As for computational speed, the proposed model ranks the first among all the ensemble models. Moreover, the ensemble empirical mode decomposition is better than the empirical mode decomposition. The Friedman statistical test also confirms the results of the comparison. Experimental results reveal that the proposed model reaches the best overall performance and is a very promising model for complex travel time prediction.

Local and global characteristics-based kernel hybridization to increase optimal support vector machine performance for stock market prediction

In this paper, a novel multi-kernel support vector machine (MKSVM) combining global and local characteristics of the input data is proposed. Along with, a parameter tuning approach is developed using the fruit fly optimization (FFO), which is applied to stock market movement direction prediction problem. At first, factor analysis is used for identifying reduced key features called as factor scores from the raw stock index data which when applied to the model contributes to improvement in prediction performance. Subsequently, a hybrid kernel method combining local and global characteristics of input data is proposed, where polynomial is used for global kernel and radial basis function is utilized for local kernel. Additionally, FFO-based parameter tuning scheme is proposed to enhance the prediction performance further. Lastly, the evolving MKSVM with best feature subset and optimal parameters is used to predict stock market movement direction based upon historical data series. For evaluation and illustration purposes, three significant stock databases, NYSE, DJI and S&P 500 are used as testing targets. The effectiveness of this proposed approach is proved by three different stock market datasets, which demonstrate that the proposed approach outperforms the MKSVM with default parameter, MKSVM with PSO, MKSVM with GA and other methods. In addition, our findings reveal that the optimization strategy proposed here may be used as a promising choice forecasting tool for better generalization ability higher forecasting accuracy.

RADIAL GROWTH DYNAMICS OF Khaya ivorensis TREES FROM EXPERIMENTAL PLANTATION

ABSTRACT The objective of this study was to characterize the formation of growth rings in Khaya ivorensis wood, as well as to analyze the variation in the morphology of the vessel elements, aiming to understand the growth of this species in experimental plantations. Disks from the bases of five trees were collected, and after polishing the cross-section, demarcation of the growth rings limits was carried out. The anatomical study of the wood was conducted with the purpose of characterizing the rings, and their width was measured by means of scanned images of the disks. A historical series of data was obtained concerning the diameter of the trees to establish a correlation with the width of the rings measured over the years. The growth rings limits of African mahogany wood are demarcated by a thin layer of marginal parenchyma, and the presence of false rings has been observed. Significant annual growth correlation of 0.62 was observed among the trees, being the radial increment of irregular wood in the first years (wide and narrow rings), and highlighting the 3rd and 11th year of growth in which the largest increases were observed due to a biological adaptation tendency to the environment and the occurrence of thinning carried out in the planting area, respectively.

Demand forecasting: proposal of a model for a glass tempering industry

The given article aims to evaluate different quantitative demand forecast methods through a case study on a glass tempering company. The analysis were held based on historical data series, which allowed the use of a part of this data for method application and another part for comparison and validation of the model`s results. The methods were compared based on obtaining the mean absolute error. In the studied company, the raw material request for the suppliers was made when new orders are ordered (pulled production). This method results in longer responsiveness, mainly due to the waiting time of raw material arrival. The application of those different demand forecasting models were analysed over three types of products on the tempered glass category, which represents a total volume of 65% of the company's costs. As a result, two methods were better adapted to the real data, providing absolute errors between 0.25 and 0.29. This given work showed that the application of the demand forecasting methods would reduce orders delivery time, what could lead to real gains to the analyzed company.