Metode razvoja i adaptacije regresionih modela bazirane na genetskim algoritmima

Većina postojećih regresionih metoda modeliranja pretpostavlja vremensku nepromenljivost modeliranih objekata i zahteva stalan skup ulaznih parametara. U realnim aplikacijama, stalne promene objekata i otkazi merne opreme mogu dovesti do situacija u kojima usvojeni model postaje neupotrebljiv. Iz tog razloga je neophodno razviti metode i sisteme za automatsko generisanje što adekvatnijih modela za datu situaciju. U okviru ove disertacije su razvijena dva hibridna metoda koji nude deo rešenja za navedene probleme. MLR/GA hibrid omogućava generisanje linearnog regresionog modela (MLR) koji je, za date uslove, optimizovan pomoću genetskih algoritama po kriterijumu tačnosti i kriterijumu kompleksnosti. Za razliku od postojećih metoda, MLR/GA metod omogućava generisanje adaptivnih modela koji su otporni na promenljivost skupa ulaznih promenljivih i promenljivost skupa izmerenih vrednosti. Razvijeni MLR/GA metod je implementiran u vidu GenReg softverskog agenta, čije performanse su testirane u postupku modeliranja radijalnog pomeranja odabranih tačaka betonske brane Bočac, na reci Vrbas u Republici Srpskoj. Modeli generisani korišćenjem MLR/GA metoda su u slučaju otkaza pojedinih senzora pokazali značajno bolju sposobnost za predikciju u odnosu na MLR modele koji podrazumevaju stalan skup ulaznih promenljivih. Dodatno, hibridni metod je pokazao sposobnost da pri generisanju modela odbacuje prediktore koji nisu od značaja za opisivanje posmatranog objekta. ANN/GA je hibridni metod za razvoj i adaptaciju regresionih modela zasnovanih na veštačkim neuronskim mrežama (ANN). Korišćenjem genetskih algoritama ANN/GA metod optimizuje strukturu i parametre neuronske mreže u skladu sa aktuelnim skupovima ulaznih i izlaznih promenljivih, i merenih vrednosti. Za razliku od sličnih postojećih rešenja, ANN/GA metod optimizuje skoro sve elemente neuronske mreže. Hibrid vrši samopodešavanje modela tako što optimizuje broj skrivenih slojeva, broj neurona u tim slojevima, izbor aktivacione funkcije, algoritam učenja, kao i vrednosti parametara učenja u skladu sa odabranim algoritmom. Razvijeni ANN/GA metod je implementiran u vidu DEVONNA softverskog agenta koji je validovan kroz studiju slučaja brane Grančarevo, na reci Tebišnjici u Republici Srpskoj, a rezultati su poređeni sa rezultatima dobijenim korišćenjem ekvivalentnog MLR/GA hibrida. Realizovani testovi su pokazali da modeli generisani ANN/GA hibridom mogu dati predikcije strukturnog ponašanja brane sa većom tačnošću od MLR modela. Međutim, za razliku od modela u obliku MLR, koji su otporni na temperaturne fazne pomake prisutne na različitim geografskim lokacijama, modeli u formi ANN pokazuju nestabilno ponašanje pod takvim okolnostima. Pored toga, generisanje ANN modela je vremenski znatno zahtevnije. Komparativna analiza modela generisanih na osnovu MLR/GA i ANN/GA metoda sa jedne, i modela u formi postepenih regresija, sa druge strane, je pokazala da predstavljeni metodi u pojedinim aspektima prevazilaze mogućnosti postojećih metoda za generisanje regresionih modela. Uz primenu tehnika redukcije dimenzija prostora istraživanja, predloženi hibridni metodi i razvijeni softverski agenti predstavljaju moćan alat za modeliranje realnih objekata i sistema.Most of existing regression modeling methods presuppose the time immutability of the modeled objects and require a constant set of input parameters. In real applications, the constant changes of the objects and failures of measuring equipment can lead to situations in which the adopted model becomes unusable. For this reason it is necessary to develop methods and systems for automatic generation of the most adequate models for the given situation. In this dissertation two hybrid methods that offer part of the solution to the above problems have been developed. MLR/GA hybrid is able to generate a linear regression model (MLR) which is, for the given conditions, optimized by using genetic algorithms according to the criterion of accuracy and complexity criterion. Unlike the existing methods, MLR/GA method is enable to generate the adaptive models that are resistant to the variability of the set of input variables and the growing set of measured values. The developed MLR/GA method is implemented in the form of GenReg software agent, whose performances have been tested in the process of modeling the radial displacement of the selected points of Bočac concrete dam on the Vrbas river, in the Republic of Srpska. In the case of failure of individual sensors, models generated by using MLR/GA method showed a significantly better prediction compared to the MLR models that implied a constant set of input variables. In addition, the hybrid method has shown the capability of rejecting predictors that have no influence on the modeled object. ANN/GA is a hybrid method for the development and adaptation of regression models based on artificial neural networks (ANN). Using genetic algorithms ANN/GA method optimizes the structure and parameters of neural network in accordance with the current sets of input and output variables and measured values. Unlike similar existing solutions, ANN/GA method optimizes nearly all the elements of a neural network. The hybrid performs self-tuning of the model by optimizing the number of hidden layers, the number of neurons in these layers, the choice of activation function, learning algorithm, as well as the values of learning parameters of the selected algorithm. The developed ANN/GA method was implemented in the form of DEVONNA software agent that was validated through a case study Grancarevo, on the Tebisnjica river, in the Republic of Srpska, and the results were compared to the results obtained using the equivalent MLR/GA hybrid. Completed tests showed that the models generated by ANN/GA hybrid could give predictions of structural behavior of the dam with a higher accuracy than the MLR model. However, unlike the models in the form of MLR, which are resistant to temperature phase offsets present at different geographical locations, the models in the form of ANN exhibit unstable behavior under such circumstances. In addition, the generation of an ANN model has shown much higher computational demands. The comparative analysis of the models generated by the MLR/GA and ANN/GA methods on the one hand, and the models in the form of stepwise regression, on the other hand, has shown that the presented methods in some aspects surpass the capabilities of existing methods for generating the regression models. With the application of the research space dimension reduction the proposed hybrid methods and the developed software agents represent a powerful tool for modeling real objects and systems

Methodology of Reconstuction of Pleistocene Mountain Glaciation in Dinaric-Prokletije Mountains

Највиши делови Динарско-проклетијских планина су током плеистоцена били захваћени глацијалним процесом и формирањем долинских ледника. Њихови морфолошки трагови су и данас очувани и јасно изражени на највишим планинама Балканског полуоства...During the Pleistocene, the highest parts of the Dinaric-Prokletije Mountains were subject to glaciation process and the formation of valley-type glaciers. Their morphological traces are still preserved and clearly observable on the highest mountains of the Balkan Peninsula..

CLIMATE REGIONALIZATION OF SERBIA ACCORDING TO KÖPPEN CLIMATE CLASSIFICATION

The paper presents a concise overview of the theoretical framework on which climate classifications are based. Beside short review of climate classifications, namely climatic regionalization for Serbia (or wider area including Serbia), main deficiency of these research was ascertained (which primarily relate to the period on the basis of which climate regionalization was carried out). The criteria of the Köppen climate classification are presented, on the basis of which the climate regionalization of Serbia has been carried out. The methodology of making maps of air temperatures and precipitation amounts has been described, on the basis of which a map of the climate regions of Serbia has been created. Spatial distribution of the types and subtypes of the climates in Serbia has been briefly described. It has been pointed to the constraints of the climate regionalization that arise from the theoretical bases of the climate classifications, but also from nature of the collected data and the applied methodology

Mogućnosti gajenja crne i crvene ribizle u Srbiji - sortiment i sistemi gajenja

According to extent production of worldwide, currant is on the second place among small fruits, after strawberry. The economy of currant growing is mainly based on its early cropping, highly and regularity yields and relative lesser investments in establishment and maintenance planting. However, the present state in currant production is not so good in our Country, although there are demands for this fruit species on marketplace and favorable natural conditions for its growing. Black and red currants are mainly grown on small plots in form of bushes. In regard to good productive properties and currant state on marketplace it is recommended its widely growing with application of new assortment and modern growing technologies. In that way, it can be ensured higher yields and decreasing import of this high quality fruits that are responded consumer requirements.Po obimu proizvodnje u svetskim razmerama ribizla zauzima drugo mesto među jagodastim voćem, odmah iza jagode. Njeno gajenje je vrlo rentabilno zahvaljujući ranom stupanju u period plodonošenja, postizanju visokih i redovnih prinosa i relativno niskim ulaganjima u podizanje i održavanje zasada. Kod nas je ribizla kao vrsta neopravdano zapostavljena, iako postoji tražnja na tržištu za ovom voćnom vrstom, a agroekološki uslovi za gajenje su pogodni na širem području Srbije. Pretežno se gaje crna i crvena ribizla uglavnom sporadično u malim zasadima i na okućnicama po sistemu žbunova. Uzimajući u obzir dobre proizvodne karakteristike ribizle i stanje na tržištu može se preporučiti njeno masovnije gajenje, podizanjem novih zasada sa inoviranim sortimentom i primenom savremene tehnologije gajenja, što će za rezultat imati povećanje prinosa. Na taj način bi se eliminisala potreba za uvozom ovog visokokvalitetnog voća i omogućilo plasiranje ribizle kao prateće vrste malini, jagodi i kupini

SPATIAL-TEMPORAL VARIABILITY OF AIR TEMPERATURES IN SERBIA IN THE PERIOD 1961–2010

The aim of this paper is to examine the spatial and temporal variability of the average monthly, seasonal and annual air temperatures in Serbia. Therefore, data from 64 climatologic stations were analyzed in the period from 1961 to 2010. Based on the data, on the position of the stations (their latitude, longitude, altitude), and the characteristics of the terrain in their vicinity (inclination and terrain exposure in a radius of 10 km around the station), a regression model was constructed based on which air temperatures are interpolated for the territory of Serbia. The rootmean-square error (RMSE) of the regression model ranged from 0.2 ºC in January, February and November to 1.1 ºC in August. Spatial distribution of air temperatures is shown (maps of mean monthly, mean seasonal and mean annual air temperatures are made), and the Sen's procedure was used to calculate trends of air temperatures (maps of average monthly, mean seasonal and mean annual trends of air temperatures). The Mann-Kendall test was used to test the significance of air temperature trends. Apart from the southeast, the whole territory of Serbia has practically experienced a statistically significant rise in the average annual air temperature, with the highest increase in the summer and winter months

THUFUR MORPHOLOGY WITHIN THE PONOR DEPRESSION (STARA PLANINA, SERBIA)

Thufur (earth hummocks) are small periglacial landforms typical for subpolar latitudes, as well as for the high alpine areas at lower latitudes. Their presence in the mountains of the Balkan Peninsula was spotted during the mid-20th century. In this paper we analyze morphometry and morphology of thufur in the context of physio-geographical conditions for their formation. The main aims are to inventorize the thufur in the study area and to determine the physio-geographical factors which enabled their formation at non-zonal elevations. Statistical analysis was performed on the sample of 305 thufur mapped in the field, measuring their circumference, height, and delineating their areas. Classification of the results revealed morphological varieties in terms of horizontal and vertical development. The elevation of the sampling location Ponor is 1,410 m a.s.l., which is considerably lower than the zonal periglaciation in Serbia, at approx. 1,900 m. Therefore, the role of relief as a climate modifier is analyzed in the context of conditions for the azonal development of periglaciation process. Topographical conditions for thufur formation were analyzed through slope inclinations and vertical dissection, determined using the Digital Elevation Model over Europe with 25 m resolution

Uticaj BA i BA+GA4+7 na formiranje prevremenih grančica na jednogodišnjim sadnicama sorti jabuke

This study presents the influence of BA (6-benzyladenine) and BA+GA4+7 (6-benzyladenine + gibberellic acids 4 and 7) on feathering of one-year-old apple trees of two cultivars Jonagold and Čadel. Different concentrations of BA (300, 600, 1,200 and 1,800 mg L-1) and BA+GA4+7 (500, 1,000, 1,500 and 2,000 mg L-1) were applied, and two treatments for both chemicals were performed. The first treatment was applied at 70-cm height of nursery trees and the second 2 weeks later. Comparison was performed in relation to untreated control. An application of BA and BA+GA4+7 did not affect both rootstock and nursery tree diameter at 10 cm above the grafting union. Nursery trees of cultivar Jonagold were not influenced by treatments applied, whereas in cultivar Čadel, the treatment with BA+GA4+7 decreased apical growth of nursery trees. The development of sylleptic shoots in both cultivars tested was influenced by the type of growth regulator and concentration applied. Treatment with BA at 300 mg L-1 concentration in both cultivars tested did not influence total length and number of sylleptic shoots, as well as the number of sylleptic shoots longer than 20 cm. The most positive influence on all studied parameters was observed on nursery trees treated with the concentration of 1,200 mg L-1 BA. The lowest concentration of BA+GA4+7 (500 mg L-1) caused the low feathering of both studied cultivars. The higher concentrations (1,000, 1,500 and 2,000 mg L-1) similarly increased the number and total length of sylleptic shoots of nursery trees.U ovom radu je ispitivan uticaj BA (6-benziladenin) i BA+GA4+7 (6-benziladenin + giberelinska kiselina 4 i 7) na bočno grananje jednogodišnjih sadnica dve sorte jabuke Jonagold i Čadel. Primenjene su različite koncentracije BA (300, 600, 1.200 i 1.800 mg L-1) i BA+GA4+7 (500, 1.000, 1.500 i 2.000 mg L-1) pri čemu su kod oba hemijska jedinjenja tretmani izvedeni dva puta. Prvi tretman je izveden kada su sadnice bile visine 70 cm, a drugi tretman je izveden dve nedelje kasnije. Kontrola je bila bez tretiranja. Primena BA i BA+GA4+7 nije ispoljila uticaj na prečnik podloge i sadnice na visini od 10 cm iznad spojnog mesta. Kod sadnica sorte Jonagold nisu registrovane razlike u vršnom porastu pod uticajem primenjenih tretmana, dok je kod sorte Čadel tretman sa BA+GA4+7 uticao na smanjenje vršnog porasta sadnica. Ustanovljeno je da su tip hemijskog regulatora i primenjena koncentracija uticali na razvoj prevremenih grančica kod obe ispitivane sorte. Tretman sa BA u koncentraciji od 300 mg L-1 nije ispoljio uticaj na ukupnu dužinu i broj prevremenih grančica, kao i na broj prevremenih grančica dužih od 20 cm. Najpozitivniji uticaj na sve ispitivane parametre je zabeležen kod sadnica tretiranih sa BA u koncentraciji od 1.200 mg L-1. Najniža koncentracija BA+GA4+7 (500 mg L-1) je izazvala slabo grananje sadnica kod obe ispitivane sorte. Veće koncentracije (1.000, 1.500 i 2.000 mg L-1) su uslovile slično povećanje broja i ukupne dužine prevremenih grančica na sadnicama

Frequency Analysis of Absolute Maximum Air Temperatures in Serbia

This paper describes the frequency analysis of absolute maximum air temperatures, using annual maximum series (AMS) in the period 1961–2010 from 40 climatological stations in Serbia with maximum likelihood estimation of distribution parameters. For the goodness of fit testing of General Extreme Value (GEV), Normal, Log-Normal, Pearson 3 (three parameters), and Log-Pearson 3 distribution, three different tests were used (Kolmogorov-Smirnov, Anderson-Darling, chi-square). Based on the results of these tests (best average rank of certain distribution), the appropriate distribution is selected. GEV distribution proved to be the most appropriate one in most cases. The probability of exceedance of absolute maximum air temperatures on 1%, 0.5%, 0.2%, and 0.1% levels are calculated. A spatial analysis of the observed and modeled values of absolute maximum air temperatures in Serbia is given. The absolute maximum air temperature of 44.9 °C was recorded at Smederevska Palanka station, and the lowest value of maximum air temperature 35.8 °C was recorded at Zlatibor station, one of the stations with the highest altitude. The modeled absolute maximum air temperatures are the highest at Zaječar station with 44.5 °C, 45.6 °C, 47.0 °C, and 48.0 °C and the lowest values are calculated for Sjenica station with 35.5 °C, 35.8 °C, 36.1 °C, and 36.2 °C for the return periods of 100, 200, 500, and 1000 years, respectively. Our findings indicate the possible occurrence of much higher absolute maximum air temperatures in the future than the ones recorded on almost all of the analyzed stations

Frequency Analysis of Absolute Maximum Air Temperatures in Serbia

This paper describes the frequency analysis of absolute maximum air temperatures, using annual maximum series (AMS) in the period 1961–2010 from 40 climatological stations in Serbia with maximum likelihood estimation of distribution parameters. For the goodness of fit testing of General Extreme Value (GEV), Normal, Log-Normal, Pearson 3 (three parameters), and Log-Pearson 3 distribution, three different tests were used (Kolmogorov-Smirnov, Anderson-Darling, chi-square). Based on the results of these tests (best average rank of certain distribution), the appropriate distribution is selected. GEV distribution proved to be the most appropriate one in most cases. The probability of exceedance of absolute maximum air temperatures on 1%, 0.5%, 0.2%, and 0.1% levels are calculated. A spatial analysis of the observed and modeled values of absolute maximum air temperatures in Serbia is given. The absolute maximum air temperature of 44.9 °C was recorded at Smederevska Palanka station, and the lowest value of maximum air temperature 35.8 °C was recorded at Zlatibor station, one of the stations with the highest altitude. The modeled absolute maximum air temperatures are the highest at Zaječar station with 44.5 °C, 45.6 °C, 47.0 °C, and 48.0 °C and the lowest values are calculated for Sjenica station with 35.5 °C, 35.8 °C, 36.1 °C, and 36.2 °C for the return periods of 100, 200, 500, and 1000 years, respectively. Our findings indicate the possible occurrence of much higher absolute maximum air temperatures in the future than the ones recorded on almost all of the analyzed stations

Metode razvoja i adaptacije regresionih modela bazirane na genetskim algoritmima

Većina postojećih regresionih metoda modeliranja pretpostavlja vremensku nepromenljivost modeliranih objekata i zahteva stalan skup ulaznih parametara. U realnim aplikacijama, stalne promene objekata i otkazi merne opreme mogu dovesti do situacija u kojima usvojeni model postaje neupotrebljiv. Iz tog razloga je neophodno razviti metode i sisteme za automatsko generisanje što adekvatnijih modela za datu situaciju. U okviru ove disertacije su razvijena dva hibridna metoda koji nude deo rešenja za navedene probleme. MLR/GA hibrid omogućava generisanje linearnog regresionog modela (MLR) koji je, za date uslove, optimizovan pomoću genetskih algoritama po kriterijumu tačnosti i kriterijumu kompleksnosti. Za razliku od postojećih metoda, MLR/GA metod omogućava generisanje adaptivnih modela koji su otporni na promenljivost skupa ulaznih promenljivih i promenljivost skupa izmerenih vrednosti. Razvijeni MLR/GA metod je implementiran u vidu GenReg softverskog agenta, čije performanse su testirane u postupku modeliranja radijalnog pomeranja odabranih tačaka betonske brane Bočac, na reci Vrbas u Republici Srpskoj. Modeli generisani korišćenjem MLR/GA metoda su u slučaju otkaza pojedinih senzora pokazali značajno bolju sposobnost za predikciju u odnosu na MLR modele koji podrazumevaju stalan skup ulaznih promenljivih. Dodatno, hibridni metod je pokazao sposobnost da pri generisanju modela odbacuje prediktore koji nisu od značaja za opisivanje posmatranog objekta. ANN/GA je hibridni metod za razvoj i adaptaciju regresionih modela zasnovanih na veštačkim neuronskim mrežama (ANN). Korišćenjem genetskih algoritama ANN/GA metod optimizuje strukturu i parametre neuronske mreže u skladu sa aktuelnim skupovima ulaznih i izlaznih promenljivih, i merenih vrednosti. Za razliku od sličnih postojećih rešenja, ANN/GA metod optimizuje skoro sve elemente neuronske mreže. Hibrid vrši samopodešavanje modela tako što optimizuje broj skrivenih slojeva, broj neurona u tim slojevima, izbor aktivacione funkcije, algoritam učenja, kao i vrednosti parametara učenja u skladu sa odabranim algoritmom. Razvijeni ANN/GA metod je implementiran u vidu DEVONNA softverskog agenta koji je validovan kroz studiju slučaja brane Grančarevo, na reci Tebišnjici u Republici Srpskoj, a rezultati su poređeni sa rezultatima dobijenim korišćenjem ekvivalentnog MLR/GA hibrida. Realizovani testovi su pokazali da modeli generisani ANN/GA hibridom mogu dati predikcije strukturnog ponašanja brane sa većom tačnošću od MLR modela. Međutim, za razliku od modela u obliku MLR, koji su otporni na temperaturne fazne pomake prisutne na različitim geografskim lokacijama, modeli u formi ANN pokazuju nestabilno ponašanje pod takvim okolnostima. Pored toga, generisanje ANN modela je vremenski znatno zahtevnije. Komparativna analiza modela generisanih na osnovu MLR/GA i ANN/GA metoda sa jedne, i modela u formi postepenih regresija, sa druge strane, je pokazala da predstavljeni metodi u pojedinim aspektima prevazilaze mogućnosti postojećih metoda za generisanje regresionih modela. Uz primenu tehnika redukcije dimenzija prostora istraživanja, predloženi hibridni metodi i razvijeni softverski agenti predstavljaju moćan alat za modeliranje realnih objekata i sistema.Most of existing regression modeling methods presuppose the time immutability of the modeled objects and require a constant set of input parameters. In real applications, the constant changes of the objects and failures of measuring equipment can lead to situations in which the adopted model becomes unusable. For this reason it is necessary to develop methods and systems for automatic generation of the most adequate models for the given situation. In this dissertation two hybrid methods that offer part of the solution to the above problems have been developed. MLR/GA hybrid is able to generate a linear regression model (MLR) which is, for the given conditions, optimized by using genetic algorithms according to the criterion of accuracy and complexity criterion. Unlike the existing methods, MLR/GA method is enable to generate the adaptive models that are resistant to the variability of the set of input variables and the growing set of measured values. The developed MLR/GA method is implemented in the form of GenReg software agent, whose performances have been tested in the process of modeling the radial displacement of the selected points of Bočac concrete dam on the Vrbas river, in the Republic of Srpska. In the case of failure of individual sensors, models generated by using MLR/GA method showed a significantly better prediction compared to the MLR models that implied a constant set of input variables. In addition, the hybrid method has shown the capability of rejecting predictors that have no influence on the modeled object. ANN/GA is a hybrid method for the development and adaptation of regression models based on artificial neural networks (ANN). Using genetic algorithms ANN/GA method optimizes the structure and parameters of neural network in accordance with the current sets of input and output variables and measured values. Unlike similar existing solutions, ANN/GA method optimizes nearly all the elements of a neural network. The hybrid performs self-tuning of the model by optimizing the number of hidden layers, the number of neurons in these layers, the choice of activation function, learning algorithm, as well as the values of learning parameters of the selected algorithm. The developed ANN/GA method was implemented in the form of DEVONNA software agent that was validated through a case study Grancarevo, on the Tebisnjica river, in the Republic of Srpska, and the results were compared to the results obtained using the equivalent MLR/GA hybrid. Completed tests showed that the models generated by ANN/GA hybrid could give predictions of structural behavior of the dam with a higher accuracy than the MLR model. However, unlike the models in the form of MLR, which are resistant to temperature phase offsets present at different geographical locations, the models in the form of ANN exhibit unstable behavior under such circumstances. In addition, the generation of an ANN model has shown much higher computational demands. The comparative analysis of the models generated by the MLR/GA and ANN/GA methods on the one hand, and the models in the form of stepwise regression, on the other hand, has shown that the presented methods in some aspects surpass the capabilities of existing methods for generating the regression models. With the application of the research space dimension reduction the proposed hybrid methods and the developed software agents represent a powerful tool for modeling real objects and systems