A novel combined model for prediction of daily precipitation data using instantaneous frequency feature and bidirectional long short time memory networks.

Levent Latifoğlu

doi:10.1007/s11356-022-18874-z

Abstract

Meteorological events constantly affect human life, especially the occurrence of excessive precipitation in a short time causes important events such as floods. However, in case of insufficient precipitation for a long time, drought occurs. In recent years, significant changes in precipitation regimes have been observed and these changes cause socio-economic and ecological problems. Therefore, it is of great importance to correctly predict and analyze the precipitation data. In this study, a reliable and accurate precipitation forecasting model is proposed. For this aim, three deep neural network models, long short-time memory networks (LSTM), gated recurrent unit (GRU), and bidirectional long short time memory networks (biLSTM), were applied for one ahead forecasting of daily precipitation data and compared the performances of these models. Moreover, to increase the far ahead forecasting performance of the biLSTM model, the instantaneous frequency (IF) feature was applied as the input parameter for the first time in the literature. Therefore, a novel model ensemble of IF and biLSTM was employed for the aim of one-six ahead forecasting of daily precipitation data. The performance of the proposed IF-biLSTM model was evaluated using mean absolute error (MAE), mean square error (MSE), correlation coefficient (R), and determination coefficient (R2) performance parameter and spider charts were used to assess the model performances. According to the numerical results, the biLSTM model outperformed compared with the LSTM and GRU models. After the good score achieved with biLSTM model, IF feature applied to biLSTM and IF-biLSTM model has the best forecasting performance for daily precipitation data with R2 value 0.9983, 0.9827, 0.9092, 0.8508, 0.7827, and 0.7646, respectively, for one-six ahead forecasting of daily precipitation data. It has been observed that the IF-biLSTM model has higher forecasting performance than the biLSTM model, especially in far ahead forecasting studies, and the IF feature improves the estimation performance.

Highlights

Water is the source of the life and the most important element for the survival of the life.Precipitation occurs when the moisture in the atmosphere condenses and returns to the earth in different conditions
The economical use of water resources is of vital importance in many areas such as the continuation of vitality, the need for agricultural irrigation, and electricity generation
In the first stage of the study, to evaluate the forecasting performance of Long Short Time Memory Networks (LSTM), BIDRECTIONAL LONG SHORT-TERM MEMORY NETWORKS (biLSTM) and GATED RECURRENT UNIT (GRU) models, one to three previous daily precipitation data was applied to models as input variables and one day ahead forecasting was performed

Summary

Introduction

Water is the source of the life and the most important element for the survival of the life.Precipitation occurs when the moisture in the atmosphere condenses and returns to the earth in different conditions. Hydrology is known as water science a kind of an applied science. It examines the temporal and spatial distribution of water on the earth, its physical, chemical and biological properties, and its mutual relations with the environment and living things (Wu et al 2012). Planning, projecting and operating water resources, which are of vital importance to human beings, are the leading hydrological studies (Price 2013). While benefiting from water resources, which are the leading hydrological studies, many parameters (precipitation, stream flow, infiltration, evaporation, vegetation, etc.) should be analyzed truly and their effects should be examined in order to use the water resources correctly and to project the water structures to be built correctly

Methods

Results

Conclusion