Function applicability to class conditional probability density of hourly rainfall
نویسندگان
چکیده
暴雨强度公式在水文、气象、工程设计等各领域都是非常关注的问题,而常用降雨概率分布函数的适用性欠缺,理论分布函数一直处于争鸣之中。从逐时降雨概率密度函数的适用性分析入手,有利于发现普适且恰当的理论密度函数。本文从我国暴雨洪涝灾害易发区中沿30°N选取4个经纬度长方形区域(雅安附近、鄂西南、江汉平原南部、杭州湾西),并在其南、北各选一对比分区(海南岛、郑州),对6个分区内降雨资料直接采用全样本,统计逐时降雨的三类概率密度经验函数,对照这些函数的特性,从理论上分析了众多分布函数的适用性,筛选适用函数并进行拟合试验,优选出理论密度函数。研究结果表明:三参广义伽玛函数拟合误差最小,而两参广义正态函数更恰当、被首推为理论密度函数;拟合参数寻优时的目标函数综合了乘性与加性误差模型,能使拟合曲线兼顾头尾;本研究有别于极值降雨概率分布中仅采用极少部分样本的方法,采用降雨概率密度方法替代传统的年极值法,使重现期计算更准确有效,能提高暴雨强度公式的科学性,拟合的高精度与函数的普适性有望解决降雨概率分布模型统一的问题。;Due to the deficiency of international-unified theoretical function for rainfall probability distribution, it is necessary analyze applicability, appropriateness and universality commonly used density function(PDF). On basis summarizing characteristics three kinds class conditional hourly rainfall, applicability 20 functions theoretically analyzed compared. The generalized Gamma distribution(GΓD), normal distribution(GND) Weibull distributions are selected as reference functions, genetic algorithm algorithms carry out fitting experiments. When deviation equivalent observation error, a condition distribution discriminant analysis. results show that performs worst, GΓD has smallest deviation, GND best. Therefore recommended fitting. In addition, can directly obtain optimal approximate solution through multi-objective parameter optimization, which simplify derivation calculation steps. multiplicative additive models both objective take beginning end curves, well absolute relative deviations into account.
منابع مشابه
Conditional Density Estimation with Class Probability Estimators
Many regression schemes deliver a point estimate only, but often it is useful or even essential to quantify the uncertainty inherent in a prediction. If a conditional density estimate is available, then prediction intervals can be derived from it. In this paper we compare three techniques for computing conditional density estimates using a class probability estimator, where this estimator is ap...
متن کاملProbability Distribution of Rainfall Depth at Hourly Time-Scale
Rainfall data at fine resolution and knowledge of its characteristics plays a major role in the efficient design and operation of agricultural, telecommunication, runoff and erosion control as well as water quality control systems. The paper is aimed to study the statistical distribution of hourly rainfall depth for 12 representative stations spread across Peninsular Malaysia. Hourly rainfall d...
متن کاملConditional Default Probability and Density
This paper is dedicated to our friend Marek, for his birthday. Two of us know Marek since more than 20 years, when we embarked in the adventure of Mathematics for Finance. Our paths diverged, but we always kept strong ties. Thank you, Marek, for all the fruitful discussions we have had. We hope you will find some interest in this paper and the modeling of credit risk we present, and we are look...
متن کاملNeural networks to estimate ML multi-class constrained conditional probability density functions
In this paper, a new algorithm, the Joint Network and Data Density Estimation (XKDDE), is proposed to estimate the 'a posteriori' probabilities of the targets with neural networks in multiple classes problem. It is based on the estimation of conditional dens@ functions for each class with some restrictions or constraints imposed by the classifier structure and the use B a y a rule to force the ...
متن کاملA new probability density function in earthquake occurrences
Although knowing the time of the occurrence of the earthquakes is vital and helpful, unfortunately it is still unpredictable. By the way there is an urgent need to find a method to foresee this catastrophic event. There are a lot of methods for forecasting the time of earthquake occurrence. Another method for predicting that is to know probability density function of time interval between earth...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Lake Sciences
سال: 2023
ISSN: ['1003-5427']
DOI: https://doi.org/10.18307/2023.0230