Determining the intensity of rainfall using the analysis of sound frequencies resulting from the impact of raindrops

Document Type : Research Paper

Authors

1 Department of Soil Science, Faculty of Agriculture, University of Tabriz,

2 Member of the academic staff of the Department of Soil Science, Faculty of Agriculture, Tabriz University, Iran

3 Member of the academic staff of the Department of Earth Sciences, Faculty of Natural Sciences, Tabriz University, Tabriz, Iran

Abstract

 
Knowing the intensity and duration of rainfall can be useful in many environmental analyses, including the estimation of rain erosivity and soil erosion. There are various devices to record the intensity and duration of rainfall, but purchasing and maintaining them are costly and often requires an operator to take care of them. The present research deals with the feasibility of using the analysis of sound signals caused by the collision of droplets with surfaces and objects in nature to determine the intensity and duration of rainfall. For this purpose, in the laboratory of the Department of Soil Science, Faculty of Agriculture, University of Tabriz, in 2022, rain simulators were designed to produce rains of different intensities, then, the sound signals caused by the impact of raindrops with the metal tray that was placed under the rain were recorded and transferred to the computer for processing. Then, the frequency size of audio files was extracted in MATLAB software. The results showed that with the increase in rainfall intensity, the audio amplitude and frequency size of the audio signals increased. Then, the frequency measurements were automatically placed in two clusters in SPSS software using the two-stage clustering method. Then the mean and standard deviation of each cluster were calculated and according to the correlation of each with each other and with the intensity of rainfall, and in order to avoid the multi-collinearity phenomenon, only the average of the second cluster was used as the input of gene expression programming and linear regression models. In order to test the accuracy and correctness of the results obtained from the models, the coefficient of determination (R2), root mean square error (RMSE), geometric mean of error ratio (GMER), geometric standard deviation of error ratio (GSDER) statistics were used. The values of R2, RMSE (mm/h), GMER(mm/h) and GSDER (mm/h) for the gene expression programming model in the training series data were 0.97, 1.85, 1.11 and 1.09 respectively and for the validation series data were 0.96, 2.05, 1.14 and 1.12 respectively. While the values of the above criteria in the regression model were 0.94, 2.74, 1.25 and 1.34 respectively for the training series data and 0.92, 2.91, 1.28 and 1.37 respectively for the validation series data. The results of the above statistics indicate that the gene expression programming model is relatively more accurate than the regression and overestimation model, and the estimated data of the regression model is relatively more spread than the gene expression programming model.

Keywords

Main Subjects

EXTENDED ABSTRACT

Introduction:

The application of sound data in many topics related to water and soil resources has not been used seriously yet. Especially in Iran, sound wave research in natural resources and environment sciences is considered as a new research. Therefore, it is necessary to conduct more and more diverse research in connection with the use of this method in various branches of comprehensive management of water and soil resources. Therefore, less time and money and more accurate and correct solutions can be obtained in related issues which increased the accuracy of predictions and modeling. In this research, a new and innovative method for estimating rainfall intensity based on audio data collection and audio frequency analysis is presented.

 

Materials and Methods:

In the laboratory of the Department of Soil Science, Faculty of Agriculture, University of Tabriz in 2022, 40 intensities of rainfall were created using designed rain simulators. The audio signals generated in different intensities of rainfall were recorded for 1 minute in 3 repetitions by REMAX model RP1 recorder in wav format and transferred to the computer for processing and the frequency size of audio files was extracted in MATLAB software. Then, the frequency measurements were automatically placed in two clusters in SPSS software using the two-stage clustering method. Then, the mean and standard deviation of each cluster were calculated and according to the correlation of each with each other and with rainfall intensity, and in order to avoid the phenomenon of multi-collinearity, only the mean of the second cluster was used as the input of the gene expression programming and linear regression models. To test the accuracy of the results obtained from the models, the coefficient of explanation (R2), root mean square error (RMSE), geometric mean error ratio (GMER) and geometric standard deviation of error ratio (GSDER) statistics were determined.

 

Results Discussion:

Different intensities of rain were obtained using equation 7, which is the minimum rainfall intensity of 8 mm/h and the maximum rainfall intensity is 145 mm/h (Table 1). The greater the intensity of the rainfall, the greater the kinetic energy and, as a result, its erosive power. The sound amplitude of any rainfall intensity depends on the kinetic energy of that percipitation, as the intensity of the rainfall increases, the sound amplitude will also increase accordingly. According to equation (3), rains that have a larger sound amplitude also have a larger frequency size. Based on two-stage clustering, the obtained frequency sizes for different rainfall intensities were automatically placed into two clusters and the average and standard deviation of each cluster were determined. Considering the correlation between the mean and standard deviation of each cluster with each other and with the intensity of rainfall and avoiding the phenomenon of collinearity, the mean of the second cluster was used as an input for gene expression programming and linear regression models. The values of R2, RMSE (mm/h), GMER(mm/h) and GSDER (mm/h) for the gene expression programming model in the training series data were 0.97, 1.85, 1.11 and 1.09 respectively and for the validation series data were 0.96, 2.05, 1.14 and 1.12 respectively. While the values of the above criteria in the regression model were 0.94, 2.74, 1.25 and 1.34 respectively for the training series data and 0.92, 2.91, 1.28 and 1.37 respectively for the validation series data. The results of the above statistics indicate that the gene expression programming model is relatively more accurate than the regression and overestimation model, and the estimated data of the regression model is relatively more spread than the gene expression programming model.

 

Conclusion:

 The kinetic energy of the rain is usually calculated according to the intensity of the rain, because the intensity of the rain is a function of the diameter of the raindrops, or actually a function of the mass of the raindrops and their final speed, and therefore it will be proportional to the kinetic energy of the rain. The greater the intensity of the rainfall, the greater the kinetic energy and, as a result, its erosive power. The sound amplitude of any rainfall intensity depends on the kinetic energy of that rainfall, as the intensity of the rainfall increases, the sound amplitude will increase accordingly, and as the intensity of the rainfall decreases, the sound amplitude will also decrease. Rainfalls that have a larger sound range also have a larger frequency range.

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Iranian Journal of Soil and Water Research
Volume 54, Issue 2
May 2023
Pages 319-335

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