Agaj Tropikë, Budka Anna, Janicka Ewelina, Bytyqi Valbon
Department of Construction and Geoengineering, Poznań University of Life Sciences, Piątkowska 94, Poznań, 60-649, Poland.
Department of Land Improvement, Environmental Development and Spatial Management, Poznań University of Life Sciences, Piątkowska 94, Poznań, 60-649, Poland.
Sci Rep. 2024 Sep 28;14(1):22444. doi: 10.1038/s41598-024-73405-9.
It is vital to provide useful hydrological forecasts for urban and agricultural water management, hydropower generation, flood protection and management, drought mitigation and alleviation, and river basin planning and management, among other things. This paper introduces a simple and flexible hydrological time series forecasting framework. Predicting water levels is crucial, given the need for sustainable environmental management. The prognosis should be reasonable to persuade individuals to take proper precautions. While many methods have been developed to predict water levels, here the effectiveness of two approaches to predicting river water levels was assessed. For this purpose, nine years of data were used, which were divided into input data (2014-2021) and validation data (2022), on water levels in the Morava e Binçës river for the Vitia station, in the form of monthly time series records, to identify the best model among those used and to identify the information necessary for water resource management and hazard control. Models Autoregressive Integrated Moving Average(ARIMA) and Error Trend and Seasonality, or Exponential Smoothing (ETS), were examined using the R package to determine the most accurate. The results indicate the applicability of both models, as evidenced by the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE). The predictive analysis based on historical water levels allowed the identification of distinct periods characterized by high and low water levels between 2022 and 2024, which is important for the area in question due to the numerous flood events occurring here.
为城市和农业用水管理、水力发电、防洪与管理、干旱缓解与减轻以及流域规划与管理等提供有用的水文预报至关重要。本文介绍了一个简单灵活的水文时间序列预报框架。鉴于可持续环境管理的需求，预测水位至关重要。预测结果应合理，以便说服人们采取适当的预防措施。虽然已经开发了许多方法来预测水位，但本文评估了两种预测河流水位方法的有效性。为此，使用了九年的数据，这些数据以月度时间序列记录的形式，分为维蒂亚站莫拉瓦河和宾塞斯河的输入数据（2014 - 2021年）和验证数据（2022年），以确定所用模型中最佳的模型，并确定水资源管理和灾害控制所需的信息。使用R软件包检验了自回归积分滑动平均模型（ARIMA）和误差趋势与季节性模型，即指数平滑（ETS）模型，以确定最准确的模型。结果表明这两种模型均适用，均方根误差（RMSE）和平均绝对误差（MAE）证明了这一点。基于历史水位的预测分析能够识别出2022年至2024年期间水位高低不同的时期，由于该地区发生了众多洪水事件，这对该地区而言非常重要。
