Mean Standard Deviation Ratio Research Articles

Assessing dissolved oxygen dynamics in Pasig River, Philippines: A HEC-RAS modeling approach during the COVID-19 pandemic

Abstract The water quality in Pasig River has been impacted by urbanization, industrialization, and the pandemic conditions. This study involved comparative analysis of dissolved oxygen (DO) levels before and during the pandemic. Field measurements, including water temperature, salinity, and DO, were conducted during neap and spring tides. To further understand and simulate DO distribution, a one-dimensional mathematical model, specifically Hydrologic Engineering Center-River Analysis System, was employed. Results showed that DO increased during the pandemic, ranging from 3.26 to 7.25 and 1.08 to 5.69 mg/L during neap and spring tide. From the minimum standard DO level, out of eight stations, seven passed during neap tide, while one station passed during spring tide. Salinity levels were higher during spring tide, which may have affected the DO. Salt intrusion was evident until 4 km from the confluence. The hydraulic model yielded average water level differences of 5.1 and 8.3 cm during calibration and validation, respectively. The DO calibration and simulation exhibited very good statistical evaluation outcomes, with values of 0.9 for Nash-Sutcliffe Efficiency and 0.3 for Root Mean Standard Deviation Ratio. These findings hold significant potential for formulation of water quality enhancement plans and implementation of solid waste management and pollution policies.

Моделирование стока горных рек Илейского Алатау с применением модели HBV-light

The research aimed to evaluate the possibility of applying the HBV model for assessing the flow of the Ile Alatau Mountain Range rivers. The main part of the corresponding water resources forms on the northern slopes of the Ile Alatau making them a significant water balance and water supply factor for the major cities of Almaty, Kaskelen, Talgar, and Yesik. The article includes a brief description of the HBV model, as well as hydrometeorologicaland topographical inputs, and continues with the description of the flow simulation outputs for the rivers with the catchment area varying between 71-601 km2. For the periods of 2000-2016, the model parameters were calibrated using the GAP optimization algorithm. The model’s performance was evaluated based on several criteria: Nash-Sutcliffe Efficiency (NSE), Percent bias (PBIAS), and Root Mean Standard Deviation Ratio (RSR). The selection of optimal parameters allowed obtaining the following model efficiency values: 0.80-0.93 (as per NSE), -0.78 to -15.33% (as per PBIAS), and 0.27–0.80 (as per RSR). The calculated model efficiencies point to the sufficient correlation between the dynamics of the observed and simulated runoffs during the calibration period. The study likewise included the assessment of the HBV model applicability as a forecasting technique based on the ratio. The outcomes of that exercise confirmed the facility of using the model for predicting the runoff of the Kaskelen, Talgar, Ulken Almaty, and Kishi Almaty Rivers. Due to the fact that the observation data were available only for 2020, the HBV model parameters of all the studied rivers, except the Talgar River, underwent validation for the periods of 2017-2020. The calibrated and validated parameters of the obtained Ile Alatau Mountain River Model can be recommended for runoff modeling based on the HBV-light model as well as for runoff forecasting, namely for short- and medium-term water flow forecasts, with the exception of the Turgen, Prokhodnaya and Uzyn Kargaly River Basins.

A Modified SCS Curve Number Method for Temporally Varying Rainfall Excess Simulation

The SCS curve number (SCS-CN) method has gained widespread popularity for simulating rainfall excess in various rainfall events due to its simplicity and practicality. However, it possesses inherent structural issues that limit its performance in accurately simulating rainfall excess and infiltration over time. The objective of this study was to develop a modified CN method with temporally varying rainfall intensity (MCN-TVR) by combining a soil moisture accounting (SMA) based SCS-CN method with the SMA method in the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS). In the MCN-TVR, the SMA-based SCS-CN method is utilized to simulate the cumulative rainfall excess and infiltration, while the SMA method in the HEC-HMS serves as an infiltration control function. A key advantage of the MCN-TVR is that it eliminates the need for additional input parameters by inherently linking the parameters in the two SMA-based methods. Sixteen hypothetical 24 h SCS Type II rainfall events with different soil types and five real rainfall events for the Rush River Watershed in North Dakota were used to assess the performances of the MCN-TVR method and the SMA-based SCS-CN method. In the hypothetical simulations, the rainfall excess simulated by the SMA-based SCS-CN and MCN-TVR models was compared to that simulated by a Green–Ampt model. Discrepancies were observed between the rainfall excess simulated by the SMA-based SCS-CN and Green–Ampt models, especially for coarse soils under relatively light rainfall. However, the MCN-TVR model, incorporating an infiltration control function, demonstrated its improved performance closer to the Green–Ampt model. For all the hypothetical events, the Nash–Sutcliffe efficiency (NSE) coefficient of the rainfall excess simulated by the MCN-TVR method compared to the Green–Ampt model was greater than 0.99, while the root mean standard deviation ratio (RSR) was less than 0.03. In the real applications, the SMA-based SCS-CN model failed to provide acceptable simulation of the direct runoff for rainfall events with durations of less than the time of concentration. In contrast, the MCN-TVR model successfully simulated the direct runoff for all the events with NSE values ranging from 0.65 to 0.91 and RSR values from 0.31 to 0.56.

Noise Filtering in High-Resolution Satellite Images Using Composite Multiresolution Transforms

This contribution proposes a multiresolution analysis (MRA)-based composite technique for image restoration by noise filtering in satellite images. Multiresolution techniques provide a coarse–fine and scale-invariant decomposition of images for analysis and interpretation. MRA methods effectively handle the noise because of their multiscale feature. This study presents a scheme based on the combination of wavelet-, contourlet- and curvelet-based transforms as effective tool for noise filtering in satellite images. The proposed method is applied to the problem of restoring an image from noisy data and effects of denoising are compared. Several comparison experiments with state-of-the-art noise filtering schemes are conducted. The composite approach of curvelet and wavelet is found to be more effective than the others based on the set of evaluation measures like peak signal–noise ratio, mean-squared error, edge-enhancing index and mean–standard deviation ratio across edges. The results are illustrated using high-resolution satellite data, such as Quickbird and Worldview-2 images. Such high-resolution images are more likely to be noisy due to the short observation time over the target in contrast to images from low-resolution sensors.

Triangular norm-based cuts and possibility characteristics of triangular intuitionistic fuzzy numbers for decision making

Triangular intuitionistic fuzzy numbers (TIFNs) is one of the useful tools to manage the fuzziness and vagueness in expressing decision data and solving decision making problems. In this paper, triangular norm (t-norm) based cuts of TIFNs are developed to synthesize the membership and nonmembership functions in describing the cut sets, then the possibility characteristics of TIFNs, i.e., the possibility mean, the possibility variance, and the possibility mean-standard deviation ratio, are given. Thereby, on the ground of the possibility mean-standard deviation ratio, a ranking method of TIFNs is introduced. With these elements, an approach to multiple attributes decision making (MADM) is proposed and illustrated by a numerical example. It is shown that the approach to MADM comprehensively considers both the membership and nonmembership functions and can lead to objective and reasonable results.

The effects of adding real estate into mixed‐asset portfolios in South Africa

PurposeThe purpose of this paper is to examine the performance of asset classes in the South African investment market and assess the diversification benefits from adding listed property stock into domestic mixed‐asset portfolios.Design/methodology/approachThe data sets comprise of quarterly returns on property listed stock, all share, all bond and 90 day Treasury bill for the period of January, 1999 to December, 2009. Return‐risk performance of all the assets were compared using mean return, standard deviation, mean standard deviation ratio, coefficient of variation and correlation coefficient. To determine return enhancement and risk reduction benefits of property listed stock in mixed‐asset portfolios, 22 naïve portfolios (17 with property stock and five without) were constructed and, their return and risk levels, obtained using Markowitz's mean variance analysis, were compared.FindingsThe results showed that there was evidence of superior return and risk‐adjusted performance of real estate stock over other assets. Also, adding property stock into mixed‐asset portfolios was found to have produced enhanced and statistically significant risk‐adjusted returns but minimal and insignificant risk reduction benefits. These results however are conditional on the percentage allocation to real estate and the asset class replaced.Research limitations/implicationsThe study has implication for investors. They could consider the inclusion of listed property stock in their portfolios with the expectation of a significant risk‐adjusted return enhancement but marginal risk reduction.Originality/valueThe paper is one of the few attempts at assessing the diversification benefits of listed property stock, especially from the perspective of African emerging market.

Estimating mean-standard deviation ratios of financial data

This article treats the problem of linking the relation between excess return and risk of financial assets when the returns follow a factor structure. The authors propose three different estimators and their consistencies are established in cases when the number of assets in the cross-section (n) and the number of observations over time (T) are of comparable size. An empirical investigation is conducted on the Stockholm stock exchange market where the mean-standard deviation ratio is calculated for small- mid- and large cap segments, respectively.

Optimal risky bidding strategy for a generating company by self-organising hierarchical particle swarm optimisation

In this paper, an optimal risky bidding strategy for a generating company (GenCo) by self-organising hierarchical particle swarm optimisation with time-varying acceleration coefficients (SPSO–TVAC) is proposed. A significant risk index based on mean–standard deviation ratio (MSR) is maximised to provide the optimal bid prices and quantities. The Monte Carlo (MC) method is employed to simulate rivals’ behaviour in competitive environment. Non-convex operating cost functions of thermal generating units and minimum up/down time constraints are taken into account. The proposed bidding strategy is implemented in a multi-hourly trading in a uniform price spot market and compared to other particle swarm optimisation (PSO). Test results indicate that the proposed SPSO–TVAC approach can provide a higher MSR than the other PSO methods. It is potentially applicable to risk management of profit variation of GenCo in spot market.

Ambiguity Aversion and the Arbitrage Pricing Theory

We consider a linear factor APT model and assume that agents are ambiguity averse with respect to payoffs of arbitrage portfolios. In contrast to the standard result, pricing errors need not converge to zero in the limit as the number of assets goes to infinity. Even in the case of exact factor structure, pricing errors may be nonzero and would not be eliminated by ambiguity averse arbitragers. This is because ambiguity about factor loadings and/or expected returns remains even in pure factor portfolios. Moreover, when there is ambiguity about factor sensitivities, strict arbitrage is impossible and pricing errors need not be bounded at all. Replacing a strict no-arbitrage requirement with one which imposes bounds on mean-standard deviation ratios for portfolio returns implies a bound on the pricing error which depends on the maximum admissible mean-standard deviation ratio, ambiguity about the coefficients in the return generating process (RGP), and factor volatilities. We also consider a case when agents can learn the RGP from the data using the framework of Epstein and Schneider (2007, REStud.). We show that pricing errors induced by ambiguity about RGP are quantitatively significant after accounting for the effects of learning. The results have empirical implications for tests of APT, market efficiency (anomalies) and pricing of idiosyncratic risk.

On mean-standard deviation ratio problems and beyond via c-programming

In this short note we comment on the mean-standard deviation ratio problem analyzed recently by Chung (1992) and present an alternative framework for its treatment. It is shown that this problem is just one of the many interesting global optimization problems that can be analyzed elegantly and solved efficiently by c-programming techniques

Remarks on maximal meanstandard devition ratio in undiscounted mdps

In the steady state of an undiscounted Markov decision process, we consider the problem to find an optimal stationary probability distribution that maximizes the mean standard deviation ratio among all the stationary probability distributions. The problem injects considerations in MDPs from the relative point of view