p-ISSN : 1978-3345, e-ISSN(Online): 2460-8122 pp 59-65
The Performance of K-Nearest Neighbor (KNN) Approach for Estimating Extreme Rain Events
based on CCTV Images Camera Data
Sinta Berliana Sipayung1, Edy Maryadi2, Rahmat Sunarya3, Syahrul4, Lilik S Supriatin5, Adi Witono6, Soni Aulia Rahayu7, and Indah Susanti8
1,3,5,6,7 Research Center for Climate and Atmosphere - BRIN, Indonesia, 2,4 Research Center for Artificial Intelligence and Cyber Security - BRIN, Indonesia, 8 Repository Directorate, Multimedia and Science Publishing - BRIN,
Indonesia
Email: [email protected], [email protected], [email protected], [email protected], [email protected], [email protected], [email protected], [email protected]
Abstract—Hydrometeorological disasters such as flooding in urban areas are a big problem that must be managed. Indonesia as part of the maritime continent, has high rainfall variability, both temporally and spatially.
Unfortunately, the density of instruments for measuring rainfall is still low. To solve the problem, this research will try to utilize and modify Closed Circuit Television (CCTV) cameras which have a large number in terms of quantity as instruments for measuring rainfall. The purpose of this research is to obtain rainfall image information and data generated by CCTV cameras. The image data is converted to quantitative rainfall data. The method used is the K-NN algorithm and machine learning. The research location is located in a corner of the city of Bandung with a geographical position of 60 53”30.49'S and 107.035” 12.27' E. The results of this research show that the K-NN algorithm can be applied to estimate rainfall data from CCTV images with an accuracy of more than 98%. The level of accuracy generated between CCTV camera image data and AWS is 94%. The level of accuracy is high means that CCTV camera image data can represent or be converted into quantitative rainfall data.
Index Terms—Rainfall, Rain Gauge, CCTV Camera, Image Processing, Validation.
I. INTRODUCTION
Indonesia has high level of disaster vulnerability, both geologically (earthquakes and volcanic eruptions), and hydro-meteorologically. Those disasters are usually triggered by bad weather conditions (floods, landslides, hurricanes, and ocean waves). Based on statistics, hydrometeorological disasters always dominate the total number of all disasters in Indonesia. In several reports, the National Disaster Management Agency (BNPB, 2018) states that more than 90% of disaster events in Indonesia dominate by hydrometeorological disasters such as extreme rain, flood, and landslide. Therefore, the rainfall monitoring system becomes very important to anticipate the effects of extreme rain.
Climate change has led to changes of extreme weather patterns and increasing flood hazards that harm people and facilities [3] [4]. Hydrometeorological disasters are generally not localized in small areas, such as sub-district and even village scales. The rainfall is triggered by large- scale atmospheric phenomena, thus requiring a dense network of rain gauges and high resolution. In almost
every hydrometeorological disaster there are always victims, property, and psychological trauma.
Floods and landslides are determined by the direction of runoff water. The determination of network of rain gauges can be determined based on the purposes of rainfall measurement. If the purpose of rainfall measurement is to calculate the amount of run-off, then installation of rain gauges can be installed in places that are thought to be the source of runoff [20]. This means that the density of the rainfall measuring instrument is high and dense.
A rainfall monitoring system requires the support of rainfall gauges with good spatial and temporal resolution.
The current rainfall data are generally inadequate to meet the needs of the rainfall monitoring. Generally, the existing condition in Indonesia show that In-situ observation data represents for a city rainfall data.
Available data do not show variation within cities.
Increasing the number of rainfall gauges is not an easy matter because the price of the instrument is quite high.
Likewise, remote sensing data can provide coverage of a wide area, but the highest resolution currently available is around 5 km, whereas floods and landslides are usually on a spatial scale smaller than 5 km. Previously, according to WMO regulations, one rain gauge for small island can cover approximately 25 km2 (equivalent to one rain gauge for a location with a maximum radius of 2.8 km). In contrast to mountainous topographical areas, one rain gauge is sufficient to monitor an area of 100 – 250 km2, or the equivalent of one rain gauge applies to a location within a maximum radius of 5.6 – 8.9 km. In another case, if an area has a relatively flat topography, then one rain gauge is sufficient to represent an area of 600 – 900 km2 or the equivalent of one rain gauge applies to a location within a maximum radius of 13.8 – 16.9 km [25].
Bandung City has an area of around 167.3 km². The ratio of the number of AWS to the area of Bandung City is about 0.006. This ratio is lower than the WMO standard and does not meet the specific needs for the development of hydrometeorological disaster early warning systems and climate change mitigation. The gap between data limitations and data requirements has become a motivation to develop a method for measuring
rainfall with a low price, but high spatial resolution.
Closed Circuit Television (CCTV) cameras are technologies that have been widely used by the public.
This device can be found in almost every corner of a place or location such as streets, schools, corporate buildings, shopping buildings, homes, restaurants, and so on. The major people use CCTV to monitor security, but the objects captured by CCTV cameras are not only humans but also other objects, including rainfall. Closed Circuit Television cameras can show sunny or rainy conditions. This is the basis for making CCTV cameras as rain gauges instrument with low cost and high spatial resolution. Bandung City has more than 100 CCTV which are located in various places and public facilities.
If the existing CCTV network facilities in Bandung City can be used for monitoring rainfall, then the ratio of AWS numbers can be increased. It can provide more localized rainfall information with better resolution than the current one. The problem is how to convert CCTV image data or video into quantitative rainfall data.
Information technology and computer vision can be utilized for this purpose [22]. Computer vision is an advanced method, which is used to recognize certain objects or information in great depth based on digital images, videos, or other visual input. We utilize computer vision technology to convert CCTV images (qualitative data) into rainfall quantity data. Several documents show the method of estimating rainfall using computer vision based on CCTV images, but it is still limited to the concept level (patent No.
KR101461184B1). The concept only describes system specifications consisting of essential features, such as CCTV modules, information extraction modules, sensor modules, and service modules. In the description, there is no technical explanation related to algorithms and data processing techniques. It can be said that the concept is not applicable. In another reference, it is disclosed the use of machine learning with the Support Vector Machine (SVM) algorithm to generate information on rainfall categories (heavy, moderate, and light rainfall). The weakness of the invention is that rainfall information is only in the form of categories and not rainfall values but also a communication system for people with speech and/or hearing disabilities [17]. It is difficult to use for other meteorological applications. The purpose of this study is to convert CCTV image data into rainfall data for developing a hydrometeorological disaster early warning system with high resolution. Another method that can be used to run machine learning is K-Nearest Neighbour (KNN).
The research measures the performance of K-Nearest Neighbor (KNN) Approach to estimate the rainfall data.
II. METHODS
The data used in this study are CCTV image data and AWS data. The data period used is January until December 2020. There are 2 different research locations, but both are located on Jalan Dr. Djundjunan Bandung.
One research location is the office of the Center for
Atmospheric Science and Technology and the other location is Bandung Trade Centre (BTC). The existing data was divided into two parts, the first part is used as training data, and the second part is used as testing data.
The method in this research consists of several stages, starting with collecting CCTV camera image data and rain data from AWS. Then proceed with data processing, and modeling and ends with data analysis. The stages of this research method will be described in detail below.
A. CCTV and AWS data collection
CCTV and AWS data have been used to build a rainfall estimation model. Closed Circuit Television (CCTV) camera image data and AWS data used are in the same location, namely in the office of the Center for Atmospheric Science and Technology in Bandung, West Java (geographical position of 60 53”30.49'S and 107035” 12.27' E) as the first location. Another location for placing CCTV cameras is the Bandung Trade Center (BTC). The BTC (the second location) building is not far from the location where AWS and CCTV cameras were previously placed. The distance between the two locations is about 100 m. While AWS is only placed in the first location.
Closed Circuit Television data is moving image data that is analyzed to be converted into a single image and then analyzed its information contents. Rainfall data from AWS has been used as a reference in the classification process or CCTV data label. Closed Circuit Television is a system that uses a video camera to display and record images at a certain time and place where the device is installed. Closed Circuit Television uses closed signals, unlike television which uses broadcast signals.
Closed Circuit Television is generally used as a complementary security system and is widely used in various fields such as the military, airports, shops, offices, and factories. The CCTV system can be seen in Figure 1.
B. Data processing
The first stage in processing data is sorting the acquired image data from CCTV videos whose timing is adjusted to the availability of AWS data. The second stage is to convert multi-channel images (RGB, HSV, and so on) to grayscale images. The third stage is the extraction of CCTV data features such as brightness level and visibility. After the data is extracted, then do calculate the histogram based on CCTV image features.
Then do an analysis of the histogram based on similarity
Fig. 1. CCTV System
using the K-means cluster. And last one testing data will be labeled based on the label of its k nearest neighbor with rainfall data from AWS.
C. Modeling
A model is needed to build a rainfall estimation system from CCTV image data. The model is built by analyzing the relationship of image features with rainfall data from AWS based on the suitability of the time using machine learning. The level of similarity is calculated using the Euclidean distance between the image histogram to be estimated and all the histograms in the image histogram database. The Euclidean distance formula is as follows:
(𝑝, 𝑞) = √∑𝑛𝑖=1(𝑞𝑖− 𝑝𝑖)2 ……… (1) p,q = point in –n euclidean space
qi, pi = euclidean vector
n = n-dimensional space, where n = 256 The K-Nearest Neighbor (KNN) algorithm is a machine learning algorithm that determines classification by taking a number of K data that have proximity or similarity (Neighbor classification) as the basis for determining groups of new data. KNN is a non- parametric algorithm because in the process it does not make any assumptions about the distribution of the data.
In addition, the number of parameters in the KNN can increase along with the increase in data. Basically, KNN is a simple algorithm, but it is quite powerful in determining groups of new data. In KNN there is no learning until a test sample is available, and whenever there is new data, the group can be identified from the majority member of the most common among the K closest neighbors. Decision boundaries in determining groups can be explained using Voronoi diagrams.
Fig. 2. Voronoi diagram, which depicts the area closest to a certain point, where each line segment is the same distance between two
points of different classes.
The Voronoi diagram in figure 2 is easy to understand and provides clear group boundaries due to the small amount of data.
D. Data analysis
The analysis includes model analysis and data analysis. The model analysis is done by validating the model output. Several scenarios can be used, including train/test split, cross-validation, and other scenarios. The stages of modeling and validation are carried out iteratively to get a model with a high level of accuracy.
The level of accuracy that is used as a benchmark is 80%.
To achieve high accuracy, several classification methods
will be tried.
To determine the reliability of the model, validation, and correlation are needed. Correlation states the quantitative relationship between two variables (variables) or intervals measured on an ordinal scale [9].
The correlation in this study is the relationship between rain data from AWS and estimated rainfall data from CCTV images. Correlation is expressed in terms of coefficients. The magnitude of the correlation coefficient (r) indicates the strength/weakness of the linear relationship between the two variables. The magnitude of the correlation coefficient is -1 < r < 1. A positive r value indicates a directly proportional relationship between the two variables, but a negative r value indicates an inverse relationship. The correlation between two variables is weak if 0 < r < 0.5 and strongly correlated if 0.8 < r < 1 [11].
It is different from Tantular (2013) who states that the magnitude of the correlation coefficient is as follows:
a. If 0 < r < 0.2, then the relationship between the two variables is very small or very not close/very not strong
b. If 0.2 < r < 0.4, then the relationship between the two variables is small or not strong
c. If 0.4 < r < 0.7, then the relationship between the two variables is moderate or moderated
d. If 0.7 < r < 0.9, then the relationship between the two variables is strong
e. If 0.9 < r < 1, then the relationship between the two variables is very strong
III. RESULTS AND DISCUSSION
CCTV data that is processed is quite large, considering that image acquisition from the video is taken in a fairly short time frequency. In this case, a reliable device is needed, both for hardware and software. For hardware, HPC is used with a large capacity, namely 24 Tb of storage, 512 Gb of RAM, and 16 cores for the CPU.
While the software is developed using the Python programming language, with an anaconda as an integrated development environment and a Python repository. The programming libraries include OpenCV, pandas, and NumPy.
Overall, this system consists of cameras, rainfall estimation modules, cloud storage, application programming interfaces, and rainfall information services. The system can be illustrated in figure 3. The algorithm for running computer vision in estimating the rainfall value from CCTV images is K-Nearest Neighbor (K-NN).
Fig. 3. Rainfall estimation system based on camera image using Computer Vision
The program code that has been compiled includes:
• Preprocessing and fragmentation code
• labeling code
• modeling and estimating code
A. Video Preprocessing and Fragmentation
One of the important preprocessing is video fragmentation into image pieces. In this case, the fragmented image has a period of 10 seconds. The tool used is FFmpeg with scripting language bash. Each fragmented image is given a name:
'frame_channel_YYY-MM-dd__HH-mm-ss_HH-mm- ss_xxxx.png'. Where 'XXXX' is an incremental number.
In addition, each image is stored in a folder per day.
Some examples of the results of video fragmentation into images are shown in Figures 4, 5, and 6.
Fig. 4. Example of a CCTV frame/image that doesn't rain
Fig. 5. Example of a CCTV frame/image that rain
Fig. 6. Example of a CCTV frame/image that heavy rain
B. Mapping CCTV Frame Data with AWS Data The next preprocessing step is mapping CCTV frame data with AWS data. The function of this step is to map or correspond CCTV imagery with rainfall accumulation from AWS which corresponds to the time. The tools and libraries used are Python and pandas, NumPy and DateTime in the Python programming language. The result of this step is tabular data consisting of CCTV date, AWS date, CCTV image folder name, CCTV image file name, rainfall, and rainfall accumulation in 10 seconds.
The total data obtained reached 161811 (Figure 7).
Fig. 7. Tabulation of CCTV frame data mapping results with AWS data
Fig. 8. Quantification of CCTV frame data into rainfall data
Quantification of CCTV data frames into rainfall is carried out in the order shown in figure 8. Quantification of CCTV data frames into rainfall is carried out with 3 experiments as shown in table 1:
TABLE I
CCTV AND AWS DATA COLLECTION LOCATIONS
Reference data
Count of K
Location Experiment 1 January –
December 2020
1-10 Jl. dr. Djundjunan no 133 road, Bandung, West Java.
Experiment 2 October 2020 1-10 Bandung Trade Centre (the other place, still on Jl. dr Djundjunan Road) Experiment 3 October -
November 2020
1-10 Bandung Trade Centre (the other place, still on Jl. dr.
Djundjunan Road)
The experimental results are then evaluated by comparing the results of the estimated rainfall with the AWS rainfall data with the appropriate date, and calculating the accuracy of each experiment with the following formula:
𝐶𝑜𝑢𝑛𝑡 𝑜𝑓 𝑡𝑟𝑢𝑒 𝑒𝑠𝑡𝑖𝑚𝑎𝑡𝑖𝑜𝑛
𝐶𝑜𝑢𝑛𝑡 𝑜𝑓 𝑤ℎ𝑜𝑙𝑒 𝑠𝑎𝑚𝑝𝑙𝑒𝑠 …… (2) Figure 9 shows a composite of the estimated rainfall data with AWS data with an accuracy that exceeds 98%.
Although a very high accuracy has been obtained from the results of this experiment 1, to obtain the reliability of the model, another experiment is needed, which is carried out at a different location.
Fig. 9. Comparison of rainfall data between AWS with estimation from CCTV camera on experiment 1 (period January –
December 2020).
Read mapping of scene CCTV and AWS
(rainfall accumulation in 10 second)
Read scene data in grayscala
Feature extraction (histogram)
Split reference data and sample
K-NN Mapping
Citra reference scene and rainfall data accumulation
K-NN Testing
Based on Figure 10a shows the fitting of rainfall data between AWS and the model results from experiment 2 with locations in BTC for the October 2020 period.
Automatic Weather Station (AWS) data used in the mapping stage still uses AWS in Jl. dr. Djundjunan 133 Bandung. This is done because this AWS is the closest database to BTC and only that data is available. Even so, the AWS data in Jl. dr. Djundjunan 133 Bandung can still represent the rainfall for BTC because the distance is quite close. The results from experiment 2 show that the accuracy is not very good when compared to the accuracy in experiment 1, which is only around 84 to 86.3 percent (Figure 10b). However, the accuracy is still high. One of the difficulties encountered in using CCTV BTC data is that additional steps are required. In this case, there needs to be a manual renaming of image files because the naming system used by Dinas Komunikasi dan Informatika (Communication and Informatic Agency) Bandung City does not match the acquisition time.
Fig. 10. Distribution of AWS rainfall data and model results for CCTV BTC October 2020 period
Fig. 11. Comparison of AWS rainfall data and October, November, and December 2020 model results for CCTV BTC (experiment 3)
Figure 11 shows a high rainfall caused flooding at BTC Jln dr Junjunan on December 24, 2020, composite rainfall data estimated with AWS data with an accuracy that exceeds 98%. With a fairly large decrease in inaccuracy in experiment 2, experiment 3 was carried out using BTC CCTV data for the October-November 2020 period. The results from experiment 3 can be seen in Figure 11 with an accuracy rate of more than 94%.
In this research, to ensure that the performance of K- NN machine learning is appropriate, it is used to convert CCTV camera image data into quantitative rainfall data,
it was applied using CCTV BTC in the flood incident on December 24, 2020. The results show that there is a match between the rainfall between AWS and the estimated rainfall for CCTV BTC and K-NN. Likewise, high rainfall occurs in October and November 2020.
Furthermore, it will be applied to other locations, namely urban areas that are prone to flooding for hydrometeorological disaster mitigation, especially to support urban areas to support smart cities. Figure 12 is an image from CCTV camera that shows a flood accident in Jl. dr.Djundjunan Bandung on December 24, 2020.
Fig. 12. Image data from a CCTV camera that show a flood accident on December 24, 2020
If we compare the level of accuracy between estimated rainfall data from CCTV cameras and rainfall data from AWS, there is a difference. At the first location, where AWS and CCTV cameras are placed in the same location, the accuracy rate is at most 98%. Whereas in the second location, where AWS and CCTV cameras are not placed in the same location, the accuracy rate is smaller, which is 84 - 86.3%. Still, at the second location, the accuracy rate shows 94% when extreme rain occurs on December 24, 2020. The value of the different accuracy levels between estimated rainfall data from CCTV cameras and rain data from AWS is the latest finding in this study.
This can be because firstly, CCTV cameras at 2 different locations have different specifications so the quality of recording rainfall images is also different. secondly, there may be differences in the level of accuracy due to the position of the rain gauge, in this case, AWS, is not standard. Asdak (2002) states that the accuracy of rainfall measurements can be caused by the position of the rain measuring instrument (AWS). Third, the difference in the level of accuracy can also be caused by the variability of rainfall in the 2 locations. The magnitude of the spatial variability of rainfall is determined by the size and direction of rain movement [14]. Rainfall decreases with distance from the point of the rain gauge or with an increase in the area of the location [2][8]. This quantity is called the area reduction factor.
In the event of rain on December 24, 2020, where there was heavy rain (rain intensity 0.31 mm/sec), the accuracy rate between the CCTV camera's estimated rainfall data and AWS rain data also shows a large value (94%), but is smaller when compared to the accuracy rate at locations where AWS and CCTV cameras at the same location. This is due to the recording of rain images by CCTV cameras during heavy rain, the image quality is better than when it is light or moderate rain.
If it is correlated between the estimated rainfall data from CCTV cameras and rain data from AWS, the resulting correlation coefficient is only around 0.1.
Figure 13 presents the distribution of rainfall data between CCTV camera estimates and AWS data. Based
on Figure 13, it can be shown that the rainfall data estimated by CCTV cameras and AWS data show a pattern that is directly proportional (shown by the line in Figure 13), although the correlation coefficient showing the relationship between the two is not strong [24]. This can be caused when AWS no longer shows rain occurring or stopping, but CCTV cameras still record it as rain images.
Fig. 13. Distribution of rainfall data from estimated CCTV cameras and AWS
IV. CONCLUSION
Based on several experiments that have been carried out in this research, it can be seen that the longer the period of the training data, the better the resulting model.
This is because the 'representation' of population data is getting higher. Accuracy above 75 percent is obtained with the amount of reference data >= 2 days. The results of testing at the BTC location (second location) for October 2020 using a model based on training data at the LAPAN office (first location) for 1 year, shows an accuracy of above 84% which can be increased by increasing the training data period so that it can achieve an accuracy of more than 94%. If the model is applied to CCTV images in other locations, it may show different accuracy, it can be higher or lower. In addition, the system that is built requires uniformity of CCTV formats and image naming that makes it easier to run the model and is supported by adequate hardware capacity. The results of this research indicate that the K-NN algorithm can be applied to estimate rainfall data from CCTV images with an accuracy rate for CCTV cameras and AWS that are placed in the same location is 98%, while for CCTV cameras and AWS that are not placed in the same location the accuracy rate is 84 - 86.3% and for cases of extreme rainfall on December 24, 2020 (CCTV cameras and AWS that are not placed in the same location), the level of accuracy generated between CCTV camera image data and AWS is 94%.This level of accuracy is high, meaning that CCTV camera image data can represent or be converted into quantitative rainfall data.
ACKNOWLEDGMENT
The authors’ thanks go to the Bandung City Communication and Informatics Office for agreeing to provide CCTV image data for the camera used in this research.
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