Artificial Intelligence Based Ensemble Modeling for Multi-Station Prediction of Precipitation

Abstract

The aim of ensemble precipitation prediction in this paper was to achieve the best performance via artificial intelligence (AI) based modeling. In this way, ensemble AI based modeling was proposed for prediction of monthly precipitation with three different AI models (feed forward neural network-FFNN, adaptive neural fuzzy inference system-ANFIS and least square support vector machine-LSSVM) for the seven stations located in the Turkish Republic of Northern Cyprus (TRNC). Two scenarios were examined each having specific inputs set. The scenario 1 was developed for predicting each station’s precipitation through its own data at previous time steps while in scenario 2, the central station’s data were imposed into the models, in addition to each station’s data, as exogenous input. Afterwards, the ensemble modeling was generated to improve the performance of the precipitation predictions. To end this aim, two linear and one non-linear ensemble techniques were used and then the obtained outcomes were compared. In terms of efficiency measures, the averaging methods employing scenario 2 and non-linear ensemble method revealed higher prediction efficiency. Also, in terms of Skill score, non-linear neural ensemble method could enhance predicting efficiency up to 44% in the verification step.

Basic Statistical Estimation Outperforms Machine Learning in Monthly Prediction of Seasonal Climatic Parameters

“…For this reason, CNNs and ConvLSTMs are mainly applied to data sets with short time intervals of no more than a few minutes between data points, which are typically much larger than data sets with longer time intervals [29][30][31][32][33][34][35][36][37][38][39][40]. For single-output prediction, a wider range of ML tools and time frames have been used, from linear methods in [17,21,41,42], to ensemble methods in [43][44][45], to hybrid methods in [28,[46][47][48], to deep models in [49][50][51][52][53][54][55][56] covering time scales from minutes to years.…”

“…However, we found that many papers dealing with monthly prediction of climate parameters did not transform the input data to remove seasonality. Some papers accommodate seasonality by including data from month n − 12 to predict parameters at month n [13,17,19,20,24,25,27,28,44,49,51,58,60,62,67]. Month n's time stamp (defined as n mod 12) was used as a feature in [19,49], but is not common in the literature.…”

Enhancing the Encoding-Forecasting Model for Precipitation Nowcasting by Putting High Emphasis on the Latest Data of the Time Step

“…With the increase in the number of successful cases of application of deep learning in real life, such as in autonomous driving, healthcare, and smart cities [1][2][3][4][5][6][7][8][9], various attempts have been made to apply deep learning to weather-related fields using numerical models [10] to improve the performance of weather forecasting [11][12][13][14][15]. In the field of meteorology, nowcasting is a popular research topic in which deep learning techniques are being actively applied to the analysis of spatiotemporal data, such as radar and satellite data [16][17][18][19].…”

Hybrid Machine Learning Ensemble Techniques for Modeling Dissolved Oxygen Concentration

“…The outcomes indicated that the ensemble model has better prediction accuracy. This technique has been also applied in various fields of hydro-environmental engineering, such as precipitation [46], earth-fill dam seepage analysis [41], evapotranspiration [47], and River WQ [48]. One of the factors affecting the accuracy of the models is the model input determination, which depends on the identification of the AI-based models, and others include model configuration, prediction horizon, etc.…”