A comparative evaluation of the estimators of the log Pearson type (LP) 3 distribution

Arora, Kishore; Singh, Vijay P.

doi:10.1016/0022-1694(89)90094-2

Cited by 47 publications

(22 citation statements)

References 13 publications

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“…Several researchers have successfully used the principle of maximum entropy to solve problems related to hydraulics and hydrology (Rao and Hsie, 1987;Chiu, 1987;Arora and Singh, 1989;Barbé et al, 1991;Cao and Knight, 1997;de Araújo and Chaudhry, 1998;Lien and Tsai, 2003).…”

Section: Methodsmentioning

confidence: 99%

Entropy‐based equation to assess hillslope sediment production

Araújo

2007

Earth Surf Processes Landf

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A new equation to assess hillslope sediment production, based on physical and probabilistic approaches, is presented. The equation, which allows the computation of the delivery ratio for every event, considers the physical variables of travel distance, stream power, settling velocity and gross erosion. The probability density function that arises from the new formulation is solved using the principle of maximum entropy. Based on data from five watersheds in both tropical and temperate zones, the new delivery parameter K v is calibrated and associated with vegetation cover and conservation practice. The proposed equation is rationally based in relation to parameter K v . The entropy-based equation was applied to assess sediment yield in two other experimental watersheds, showing good predictability for the set (mean absolute error of 20·8%). No systematic error was found in the analysed data. The entropy-based equation showed good predictability for long-term sets of data and for higherosivity events, but did not perform well for the low-erosivity ones.

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Section: Methodsmentioning

confidence: 99%

Entropy‐based equation to assess hillslope sediment production

Araújo

2007

Earth Surf Processes Landf

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“…The three parameter estimation methods are: 1. method of moments (MOM), 2. maximum-likelihood estimate (MLE), 3. method of maximum entropy (MME). These five distributions and three estimation methods have been discussed by various investigators (Kite, 1978;Kuczera, 1982;Arora and Singh, 1989).…”

Section: Selection Of Distributions and Parameter Estimation Methodsmentioning

confidence: 99%

“…The rainfall quantiles, in conjunction with a rainfall-runoff model are used to compute flood quantiles of a stream. The predicted quantiles at any site are subject to considerable variability (Arora and Singh, 1989). The major factors that affect the extent of variability are: 1) the sample size at an observation station, 2) the recurrence interval of an estimate, 3) the population distribution selected, and 4) the parameter estimation method used.…”

Section: Introductionmentioning

confidence: 99%

“…However, there is no general consensus on the performance of either a specific distribution or a parameter estimation method. For example, Arora and Singh (1989) concluded that the LPEAR3 with MOM performed poorly based on their Monte Carlo simulation results, while others found LPEAR3-MOM gave better estimates based on observed data (USWRC, 1967;Jam andSingh, 1987, Naghavi, et al, 1991). These presumably "contradictory" results are, in fact, not in conflict because one approach measures the predictive performance while the other measures the descriptive performance.…”

Section: Introductionmentioning

confidence: 99%

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COMPARATWE EVALUATION OF FREQUENCY DISTRIBUTIONS FOR LOUISIANA EXTREME RAINFALL¹

Naghavi¹,

Yu²,

Singh

1993

J American Water Resour Assoc

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A comparative study of five popular frequency distributions and three parameter estimation methods was conducted by using 92 Louisiana rainfall data sets. Computed results showed that the log‐Pearson type 3 (LPEAR3) distribution was the most appropriate probability distribution for the Louisiana rainfall data. Furthermore, the method of moments was found to be the best estimation method for the LPEAR3 distribution based on descriptive performance indices. A first‐order error analysis was performed on the parameters of the LPEAR3 distribution. Computed results showed that the predicted quantiles of the LPEAR3 distribution were most sensitive to the population mean and relatively insensitive to the coefficient of the skewness of the distribution.

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“…Equations (17) and (19), with sample estimators substituted for their population counterparts. We employed a Newton-Raphson method to solve Equation (22) with initial estimates obtained from a table of P(a) presented by Arora and Singh (1989). Estimates of the parameters obtained for each site, along with the coefficient of skewness as determined using Equation (16), are presented in Table 1.…”

Section: Ln(1-a)+amentioning

confidence: 99%

REGIONALIZATION OF LOW‐FLOW FREQUENCY ESTIMATES: AN ALABAMA CASE STUI)Y¹

Durrans

Tomić

1996

J American Water Resour Assoc

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Low-flow estimates, as determined by probabilistic modeling of observed data sequences, are commonly used to describe certain streamfiow characteristics. Unfortunately, however, reliable low-flow estimates can be difficult to come by, particularly for gaging sites with short record lengths. The shortness of records leads to uncertainties not only in the selection of a distribution for modeling purposes but also in the estimates of the parameters of a chosen model. In flood frequency analysis, the common approach to mitigation of some of these problems is through the regionalization of frequency behavior. The same general approach is applied here to the case of low-flow estimation, with the general intent of not only improving low-flow estimates but also illustrating the gains that might be attained in so doing. Data used for this study is that which has been systematically observed at 128 streamfiow gaging sites across the State of Alabama. Our conclusions are that the log Pearson Type 3 distribution is a suitable candidate for modeling of Alabama low-flows, and that the shape parameter of that distribution can be estimated on a regional basis. Low-flow estimates based on the regional estimator are compared with estimates based on the use of only at-site estimation techniques.(KEY TERMS: statistics; surface water hydrology; low-flow; regional analysis; parameter estimation; log Pearson Type 3.) WATER RESOURCES BULLETIN 26

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A comparative evaluation of the estimators of the log Pearson type (LP) 3 distribution

Cited by 47 publications

References 13 publications

Entropy‐based equation to assess hillslope sediment production

Entropy‐based equation to assess hillslope sediment production

COMPARATWE EVALUATION OF FREQUENCY DISTRIBUTIONS FOR LOUISIANA EXTREME RAINFALL¹

REGIONALIZATION OF LOW‐FLOW FREQUENCY ESTIMATES: AN ALABAMA CASE STUI)Y¹

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A comparative evaluation of the estimators of the log Pearson type (LP) 3 distribution

Cited by 47 publications

References 13 publications

Entropy‐based equation to assess hillslope sediment production

Entropy‐based equation to assess hillslope sediment production

COMPARATWE EVALUATION OF FREQUENCY DISTRIBUTIONS FOR LOUISIANA EXTREME RAINFALL1

REGIONALIZATION OF LOW‐FLOW FREQUENCY ESTIMATES: AN ALABAMA CASE STUI)Y1

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COMPARATWE EVALUATION OF FREQUENCY DISTRIBUTIONS FOR LOUISIANA EXTREME RAINFALL¹

REGIONALIZATION OF LOW‐FLOW FREQUENCY ESTIMATES: AN ALABAMA CASE STUI)Y¹