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Precise prediction models are essential for water resources management and planning because they provide a valuable support tool for decision-makers. Unfortunately, the random variability of hydrological variables is a serious constraint that can affect the final performance of any water action plan and hence, limits the interventions of decision makers. Actually, this can entail a significant risk when it comes to developing a water resources strategy, because the achievement of the objectives depends on the random behavior of such hydrological variables. The consequences could be very serious from a water resources management point of view, and the socio-economic and environmental impact would be even more so. The present chapter fits in this context; it presents some of our recent works on stochastic modeling for prediction of water resources in Algeria. This is done by focusing on BOX JENKINS, a particularly interesting type of models that can deal with random variability of hydrological variables. The principle of this type of model lies on the analysis of the observed time series to predict the future behavior of the generating mechanism. The objective here, is to look for the possibility of substituting these generating mechanisms by some mathematical models that behave in a similar way and then, use them to predict the future behavior of the concerned hydrological variables. These models are mostly required in water resources design and management because they provide the opportunity to foresee the risk, in part and therefore, offer the possibility to reduce its effects. In this way, considerable help is available to decision-makers in water resources so that they can develop their strategies with more safety. The data used here, are the annual and monthly stream flow time series observed over 25 years at four hydrometric stations, all located on operational dams in the north of Algeria. The obtained results are some ARMA, ARIMA and SARIMA models which are parsimonious, easy to use and whose predictions are satisfactory, since the corresponding error statistics are acceptable.

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Application of Time Series Models for Annual and Monthly Streamflow Forecasting in Northern Algeria

  • 章节

  • 发表日期：

    2025年

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  • Khadidja Boukharouba

摘要

Precise prediction models are essential for water resources management and planning because they provide a valuable support tool for decision-makers. Unfortunately, the random variability of hydrological variables is a serious constraint that can affect the final performance of any water action plan and hence, limits the interventions of decision makers. Actually, this can entail a significant risk when it comes to developing a water resources strategy, because the achievement of the objectives depends on the random behavior of such hydrological variables. The consequences could be very serious from a water resources management point of view, and the socio-economic and environmental impact would be even more so. The present chapter fits in this context; it presents some of our recent works on stochastic modeling for prediction of water resources in Algeria. This is done by focusing on BOX JENKINS, a particularly interesting type of models that can deal with random variability of hydrological variables. The principle of this type of model lies on the analysis of the observed time series to predict the future behavior of the generating mechanism. The objective here, is to look for the possibility of substituting these generating mechanisms by some mathematical models that behave in a similar way and then, use them to predict the future behavior of the concerned hydrological variables. These models are mostly required in water resources design and management because they provide the opportunity to foresee the risk, in part and therefore, offer the possibility to reduce its effects. In this way, considerable help is available to decision-makers in water resources so that they can develop their strategies with more safety. The data used here, are the annual and monthly stream flow time series observed over 25 years at four hydrometric stations, all located on operational dams in the north of Algeria. The obtained results are some ARMA, ARIMA and SARIMA models which are parsimonious, easy to use and whose predictions are satisfactory, since the corresponding error statistics are acceptable.