# Analysis Model Evaluation based on IoT Data and Machine Learning Algorithm for Prediction of Acer Mono Sap Liquid Water

• Lee, Han Sung (School of Computer Eng., Youngsan University) ;
• Jung, Se Hoon (School of Creative Convergence, Andong National University)
• Accepted : 2020.09.10
• Published : 2020.10.31

#### Abstract

It has been increasingly difficult to predict the amounts of Acer mono sap to be collected due to droughts and cold waves caused by recent climate changes with few studies conducted on the prediction of its collection volume. This study thus set out to propose a Big Data prediction system based on meteorological information for the collection of Acer mono sap. The proposed system would analyze collected data and provide managers with a statistical chart of prediction values regarding climate factors to affect the amounts of Acer mono sap to be collected, thus enabling efficient work. It was designed based on Hadoop for data collection, treatment and analysis. The study also analyzed and proposed an optimal prediction model for climate conditions to influence the volume of Acer mono sap to be collected by applying a multiple regression analysis model based on Hadoop and Mahout.

# 1. INTRODUCTION

The sap industry is on the constant rise worldwide. In South Korea, mountains account for 75% of its land and make a repository of resources. Businesses using these mountain resources are increasing exponentially every year[1-3]. Acer mono max is one of the important tree species to collect quality sap from and has long been the source of high value added income for farmers in mountain villages. Old books record that the Hwarang members drank acer mono sap in Silla. As acer mono sap has been developed in various forms including health drinks and enjoyed by common people in recent years, it is widely collected in mountain villages of Gangwon, Jeolla, and Gyeongsang provinces where the acer mono max. is usually found. Since the acer mono sap collection business is mostly led by individual farmers, there is no professional management system involved in most cases[4-5]. One of the reasons for the absence of professional management system is that most of acer mono trees are distributed in rough mountain areas. Access to acer mono trees is very limited since they are distributed in areas of high altitude and low density rather than natural colonies. In researches on the estimated time and volume of acer mono sap collection, it seems impossible to make accurate predictions due to the geographical environment, which also explains why there are few researches to analyze the management and efficiency of acer mono tree collection. Previous studies on acer mono sap focused on the analysis of its components and on its distribution, and there is a shortage of research on the vegetation and location environment of an area where acer mono max. grows naturally or productivity according to the location conditions[6-7]. And the importance of effective utilization of meteorological information is growing in the management of agricultural products due to droughts and cold waves following climate changes. The Meteorological Office is thus conducting research to make use of meteorological information for major crops by the producing area and Big Data in the meteorological and agricultural fields for the prediction of agricultural products' yields[8-9], but its research targets certain open field crops such as the mandarin, potato, pepper, sweet potato, and perilla leaf with Acer mono found in mountainous areas left out. The academic circles are conducting few researches on the prediction of its sap collection volume, either. The present study thus proposed an analysis system for Acer mono data based on meteorological information and Hadoop for its collection. The proposed system includes the Big Data of meteorological information provided by the Meteorological Office including temperature, humidity, precipitation, and hours of daylight and also the Mahout system based on Hadoop usually used in Big Data analysis systems. The study also analyzed and proposed an optimal prediction model for the collection of Acer mono sap based on climate conditions to affect its collection volume by applying the Mahout multiple regression analysis model.

# 2. RELATED WORK

## 2.1 SmartFarm Analysis Model

Previous studies discussed the followings: [10] proposed to promote the reliability of a farming journal by automatically saving the data of produce conditions and control environments and entering the multimedia data of produce. The farming journal was materialized in a physical layer, which was comprised of soil sensors and internal and external sensors in the cultivation field, a middle layer, which covered the journal's database, video, sensor, and server management, and an application layer, which provided users with GUI. The farming journal was designed to record general works and disease and pest forecasts and check the data inserted in video, voice, text or image. [11] proposed a management and monitoring system for a growth environment to increase a crop yield. The growth monitoring system would check the crop conditions via the sensors and control the environment artificially. Related environment sensors would be necessary for EC, pH, temperature, humidity, intensity of illumination, and CO2. Most of the sensor nodes were organized in a wired fashion, and the system was organized in the RS485 method. When it was organized in a wireless fashion, the Zigbeebased USN technology was applied. The control system covered crop cultivation, environment, nutrient solution and source of light. Data collected from sensor and sink nodes would be sent to the sever of a local gate to monitor the current conditions. Independent gateways were set for sensor and energy monitoring control. [12] analyzed problems with the management of Acer mono sap collection and proposed a business management system for it. The study proposed a module to manage Acer mono trees and Acer mono sap collectors and assess the collection areas by introducing a database and GIS system and a practical Acer mono tree business management system with a built-in user convenience interface to promote easy manipulation. The proposed system consisted of a sap collection management model, a cost-profit analysis model for sap production, and an evaluation model for sap collection areas. The sap collection management model managed information needed for the management of Acer mono trees and collectors. The cost-profit analysis model for Acer mono sap production analyzed the cost needed to produce sap and the profit generated from it. The evaluation model for Acer mono sap collection areas divided the areas into the upper, middle, and lower grade according to the sap production and management conditions. [13] proposed a U-IT-based farm management system to manage mountain and forest products. The proposed system established a watering facility for the growth of forest products. A total sensing system with radar sensors measured temperature, humidity, and wind directions. A database was built to analyze the growth environment based on the information gathered from the monitoring system connected to all of the sensors and the management system.

## 2.2 Big Data Analysis and Element

The study [14-18] was involved with deducing a meaning in a word cloud by analyzing one million datasets with R-studio whereas, in the study, an attempt was made to analyze the hacking attempts of 140 million per day for a period of 40 days (4.2 billion attempts) against Korea Hydro & Nuclear Power Co., Ltd. with TensorFlow for the purpose of identifying vulnerabilities. Meanwhile, in the study[15], the hacking attempts (data) against Vietnam Bank were analyzed with Hadoop to find their significance and a new tree was proposed. Fig. 1 shows efficient solitary senior citizens care and application[14].

Fig. 1. An Efficient Solitary Senior Citizens Care and Application.

Regression analysis is a statistical method to explain causal relations in nature or in society with explanatory variables to influence and response variables to be influenced[19-21]. A regression model expresses response variables with the function of explanatory variables, and an estimated regression model is used to predict the values of response variables with those of explanatory variables. Binomial types expressed in Boolean values are used for response variables in regression analysis. When there are three values of response variables or more, multinomial and continuous types are used. Regression analysis, in general, is on the premise of linear relations between independent and dependent variables. There are interactive effects in such linear relations just like the increasing values of independent variables will lead to the certain increase or decrease of dependent variables between weight and height, for instance. Eq. (1) shows a linear functional formula to present relations between correlated independent and dependent variables. Multiple regression analysis has the same basic concept as simple linear regression analysis, but it uses two independent variables or more. Predictive abilities can be increased by using many different independent variables. This model was used to match linear relations between Y Group of quantitative dependent variables and X Group of independent variables.

$Y=\beta_{0}+\beta_{1} X_{1}+\beta_{2} X_{2}+\ldots+\beta_{n} X_{n}+\epsilon$       (1)

PCA is a technique of unsupervised learning to reduce information loss of multi-dimensional input vectors through analysis and to return them to lower-dimensional vectors. It is one of the multivariate data processing techniques presented in a couple of principal component values. When there is a vector of n dimension, eigenvector is obtained through average vector and variance-covariance matrix from the application of Eq. (2) and (3). Then eigenvector is arranged according to the size of the corresponding proper value to add a new matrix. The new matrix is applied as a transformation matrix to convert Vector x into Vector y as seen in Eq. (4). Then new variables in Row y have noncorrelation and are arranged in the order of monotone decreasing variance to reduce the dimensions with the big principal components of high variance value.

$m_{x}=\frac{1}{M} \sum_{k=1}^{M} x_{k}$       (2)

$C_{x}=\frac{1}{M} \sum_{k=1}^{M} x_{k} x_{k}^{T}-m_{k} m_{k}^{T}$       (3)

$y=n \operatorname{Matrix}\left(x-m_{x}\right)$       (4)

# 3. DESIGN OF PROPOSED SYSTEM

## 3.1 Structure Diagram of Proposed System

Fig. 2 shows the entire block diagram of the Big Data system proposed for Acer mono sap in the study[22]. The system consists of a data collector, data storage, Big Data analyzer to analyze meteorological information, and UI to provide managers with analysis results. A data collector would collect meteorological information including temperature and humidity and information on the previous collection volumes of Acer mono sap. It would also collect the meteorological information of the Meteorological Office for prior analysis. A data storage would save the meteorological information collected from the sensors and the data of the collection volumes of Acer mono sap in the large-capacity storage Hadoop via Sqoop. A new table would be made based on the combination of the table saving the meteorological information from the Meteorological Office and the one saving the collection volume of Acer mono sap by the date. Based on this combined table, an analyzer would propose an optimal analysis model with the Mahoutbased multiple regression analysis algorithm.

Fig. 2. Flow Chart of Acer Mono Sap Data Analysis System.

## 3.2 Design of Data Model for Liquid Water Analysis

Fig. 3 shows a data storage structure to save the meteorological information of collected Acer mono sap and the data of its yield by the date. First, MySQL would select data to be counted. Second, the data saved at MySQL would be accumulated in HDFS at Hadoop via Sqoop. Flume would be used to collect the saved data effectively, and Kafka for buffering and transaction processing would be used for the stable collection of data. Third, large-capacity files would be loaded on Hadoop upon collection, and real-time data would be loaded on Hbase or Redis via Kafka and Storm. In this case, real-time event analysis would be carried out via Storm, and the data would be loaded on Hbase or Redis according to analysis results. Fourth, the data loaded on Hadoop would undergo a series of works including refinement, alteration, integration, separation, and search with Hive. A data mart would be created based on the normalization of data in a standardized structure. Sqoop would be used to provide processed and analyzed data to the outside. The processing and search process helps to increase data quality, tending to be long and complex. As the process is organized in the workflow of Oozie, it can help to lower complexity and promote automation. And fifth, the data loaded on Hadoop via Mahout would be used to speed up data analysis and predict the collection volume of Acer mono sap based on its categorization and analysis.

Fig. 3. Structure of Data Storage in Acer Mono Sap Data Analysis System.

## 3.3 Collection and Load of Acer Mono Sap Liquid Water Data

The Big Data used in the study included the meteorological data of Gwangyang City provided by the Meteorological Office during the period of 1999 ∼2016(November∼February) and the collection volume data of Acer mono sap in Gwangyang City in the Korea Forest Service's survey on the production of forest products. The data collected in this way included precipitation, amount of snowfall, temperature and humidity at the time of collection. A hypothesis was set that these variables would have effects on the collection volume of Acer mono sap. The detailed information of the collected data was as follows: the meteorological data of the Meteorological Office covered average monthly temperature, precipitation, amount of solar radiation, relative humidity, and amount of snowfall. Numbers that could affect data analysis such as missing values and outliers would be removed from the collected data, which also went through a pre-treatment process to present the meteorological data of the Meteorological Office in statistics by the month. Table 1 offers explanations about the independent variables to be used in data analysis after the pre-treatment process.

Table 1. Independent Variables

Fig. 4 shows a refinement and loading architecture to load large amounts of meteorological information and collection volumes of Acer mono. The architecture reads large files with the source component of Flume and loads them on certain paths in HDFS with Sink. It is important to set data format, path, and partition values carefully when loading files on HDFS since the forms of loaded data have huge impacts on search and analysis works.

Fig. 4. Architecture of Data Collection and Load for Acer Mono Sap Liquid Water Prediction.

## 3.4 Search of Acer Mono Sap Liquid Water Data

Data search is the stage involving processing and understanding the loaded data. The search process of Big Data requires considerable amounts of time and resources. At the Big Data treatment and search process, one should standardize unstructured data in large amounts with an exquisite post-treatment work to ensure the immediacy of data and conduct enough exploratory analysis based on the understanding of the work domain. Fig. 5 shows the Hive structure to search and process the data sets of meteorological information and Acer mono sap collection volumes. Hive QL would be used to retrieve, combine, separate, alter, and refine the meteorological factors and collection volume data of Acer mono sap and organize an Acer mono DW, which would in turn perform secondary and tertiary search and high-end analysis to create an Acer mono analysis mart. The collected and loaded data would be loaded in the external part of Hive, which is also used to refine it, move it to the managed area, and create a mart by the topic area. The process of treating, searching and analyzing Big Data based on Hive, Peak, and Spark repeats itself in complex leading and trailing relations. Apache Oozie is used to process repetitive and complicated post-treatment jobs.

Fig. 5. Structure of Hive in Proposed Data Analysis Model.

Fig. 6 shows the Oozie architecture to be used in the Big Data system of Acer mono. The workflow made by a client in Oozie would be transmitted to the Oozie server with the meta-information of related workflows managed separately by RDBMS. The coordinator in the Oozie server would schedule the workflows registered in Oozie. Here, the engine would interpret the information of action and control nodes based on the workflows and implement related tasks at the Hadoop cluster. Oozie would be also used to define and process post-treatment works. A variety of Hive QL would be used to move the loaded data to the external, management, and mart area in the order. Scheduling would take place according to the promised time based on the workflow of Oozie.

Fig. 6. Structure of Oozie in Proposed Data Analysis Model.

# 4. IMPLEMENTATION OF ANALYSIS MODEL AND SIMULATION

The proposed system was subjected to implemented and experiment in the following environments: the main-processor was intel i7-4790 3.6Ghz, and main-memory was DDR3 12 Gbyte ram, and GPU was NVidia Geforce GTX 1070, and secondary memory unit was SSD 256 Gbyte. Python as the language of implement and Python 3.6, Spark 2.2, HDFS 2.7 as the tool of development. The present study built an analysis model based on the application of a multiple regression analysis algorithm to analyze relations between the learning data of collected meteorological data and the collection volume of Acer mono sap. There are two or more independent variables used in multiple regression analysis. A regression model was targeted with each independent variable in a linear relation with a dependent one. Table 2 shows the multiple regression analysis results. Various models were created as several independent variables and collection volumes were analyzed in the 1:N approach. Total 21 models were created with the ones whose coefficient of determination was under 0.4 removed. There were 12 analysis models whose coefficient of determination was 0.4 or higher. Table 2 shows the optimal analysis models in the top four places. In all the analysis models, important independent variables included average February temperature(x4 ) for the year, accumulated precipitation(x5 ) of November in the previous year, accumulated precipitation(x6) of December in the previous year, accumulated precipitation(x7 ) of January for the year, accumulated precipitation(x8) of February for the year, average relative humidity(x15) of January for the year, and average relative humidity(x16) of February for the year. A couple of independent variables made small contributions to the prediction model including accumulated amount of solar radiation(x12) of February for the year, average relative humidity(x13) of November in the previous year, and average relative humidity(x14) of December in the previous year. Model 4 was the analysis model measured based on these, recording the prediction accuracy of 98.25%.

Table 2. Data Analysis Result of Acer Mono Sap Liquid Water

# 5. CONCLUSION

The present study proposed a Big Data system based on meteorological information for Acer mono sap. The proposed system used Hadoop to collect, load, search, and analyze data. Of the meteorological information provided by the Meteorological Office, the independent variables influencing the collection volume of Acer mono sap were applied to the analysis model including average temperature, precipitation, amount of snowfall, relative humidity, and amount of solar radiation. The study also checked the analysis models for accuracy to select an optimal prediction model for the collection volume of Acer mono sap. The highest accuracy rate was 98.25%, but there were problems with predicting daily or monthly yields since the forest products data provided by the Korea Forest Service offered only the information about the total yields for the last year by the area and the data provided by the Meteorological Office offered only the data of the areas with an observation plane.

Follow-up study will build a system capable of predicting hourly yields as well as daily yields according to meteorological changes based on the farmers accurate meteorological information and real-time measurements of exudation amounts collected from the sensors of Acer mono farmers.

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