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島根大学農学部研究報告 Volume 8
published_at 1974-12-15
非定常マルコフモデルによる河川流量シミュレーション
Stochastic Simulation of River Flow by Nonstationary Markov-Chain Model
Tanaka Reijiro
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Descriptions
The time series of the average value of 5-days flow can be represented by a nonstationary Markov-chain model with randam components, month by month. The randam components ε_t may be transformed into χ_t (χ_t = ε_t + B + b) and it's distribution can be fitted most satisfactorily to a lognormal distribution. Here,
B ; a gradually increasing value with some step. The greater it is, the more normalized distribution of χ_t is gained.
b ; a parameter which approximates the shape of lognormal distribution to that of the observed one, by Iwai method.
Having sequentially generated the river flow by that model with Monte Carlo simulation technique for 100 years at two points, the model has been proved to be available for practical purposes.
B ; a gradually increasing value with some step. The greater it is, the more normalized distribution of χ_t is gained.
b ; a parameter which approximates the shape of lognormal distribution to that of the observed one, by Iwai method.
Having sequentially generated the river flow by that model with Monte Carlo simulation technique for 100 years at two points, the model has been proved to be available for practical purposes.
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