Wyniki wyszukiwania - BazTech

Ograniczanie wyników

Powiadomienia systemowe

Sesja wygasła!

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: certainty

Sortuj według:

Ogranicz wyniki do:

Scenario planning as a new application area for TOPSIS

Gaspars-Wieloch Helena

Operations Research and Decisions

2023

Vol. 33, No. 2

23--34

TOPSIS is a well-known approach applied to multi-criteria decision-making under certainty (M-DMC). However, recently, some analogies between this domain and scenario-based one-criterion decision-making under uncertainty (1-DMU) have been revealed in the literature. Thus, the similarities aforementioned give the possibility to adjust TOPSIS to another area. The goal of the paper is to create a new method for problems with non-deterministic parameters on the basis of TOPSIS ideas. In the suggested approach criteria weights (declared within TOPSIS) are replaced by subjective chances of occurrence which are estimated for each scenario. The novel method has an advantage over existing classical decision rules designed for 1-criterion decision-making under uncertainty since within this procedure each payoff connected with a given option is compared with the positive and negative-ideal solutions.

Validity, reliability and certainty of PERSIANN and TRMM satellite-derived daily precipitation data in arid and semiarid climates

Khojand Keyvan, Shaghaghian Mahmood Reza, Ghadampour Zahra, Sabzevari Touraj

Acta Geophysica

2022

Vol. 70, no 4

1745--1767

Having doubts about the adequacy of reliability level of satellite-derived precipitation products, along with their application in large number of hydrological models, has led to many studies on evaluating the efficiency of such data. In this study, two new procedures were proposed to compute reliability and certainty degrees of PERSIANN and TRMM 3B42RT data sets, and six traditional indicators were used to evaluate their validation. In addition, the cumulative density function (cdf) of the above-mentioned data sets was compared with the ground-based observations in 23 synoptic stations in Fars, Iran. The Kolmogorov–Smirnov test was performed using the data sets at 5% significance level which led to the result of null hypothesis that was not being rejected, suggesting that the satellite-derived daily precipitation data (SDDPD) and ground based observations are drawn from the same distribution. Results indicated that TRMM and PERSIANN follow quite similar probability pattern of ground-based observations in arid and semiarid climate, respectively. However, data probability pattern of TRMM cannot be considered similar to ground-based observations in arid region, neither can PERSIANN in semiarid climate. Among common cross-validating attributes, the values of ME and BIAS, in addition to RMSE and MAE, led to the conclusion that in PERSIANN, the rainfall daily rates are almost underestimated while TRMM overestimates the values mainly in semiarid regions. Moreover, the PERSIANN was found to be significantly correlated with IDM (De Martonne aridity Index), and the values of underestimation increased with growth of the index. The reliability values of SDDPD over the study area, for both TRMM and PERSIANN, show the reverse trend with increasing IDM in almost all acceptable error intervals. Along with effects of climate conditions, the reliability degrees of PERSIANN seem quite more consistent at different acceptable error intervals in comparison with the corresponding values of TRMM. In addition to validity and reliability, the error entropy of SDDPD, as an index for uncertainty degree, increases as the IDM rises, which is theoretically corresponds with reliability concept. However, in comparison with PERSIANN, TRMM data set, overall, has higher degree of uncertainty. In addition, to evaluate effect of daily rainfall intensity on the uncertainty degree of SDDPD, the uncertainty degree slightly increases as daily rainfall intensifies to about 15 mm/day. But for higher daily rainfall intensities, on the other hand, the uncertainty degree seems to gradually decline as the daily rainfall increases.