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IEEE Paper Template in A4 (V1) ISSN 2319 – 1953 International Journal of Scientific Research in Computer Science Applications and Management Studies Interactive web-based data generation software applicable for river engineering Girish S. Katkar#1, Dinesh A. Lingote*2, Ritesh Vijay@3 1 Head, Dept. of Computer Science, Taywade College, Koradi, Nagpur, M.S., India 2 Sr. Scientist, CSIR-National Environmental Engineering Research Institute, Nehru Marg, Nagpur, India 3 Principal Scientist and Head, CSIR-National Environmental Engineering Research Institute, Nehru Marg, Nagpur, India [email protected],[email protected] (corresponding author),[email protected] Abstract— It's an era of automated system, many researching RSC (Residual Sodium Carbonate), SAR (Sodium adsorption organizations are working on automated system to ease burden ratio), Sodium, Sulfate, TC (Total Coliform), Fluoride, Lead, of conventional systems. Among all, if data generation system for Copper and Zinc, PH (Potential of Hydrogen), turbidity, water large geographical area like: River modelling, Biodiversity and conductivity, total alkalinity, hardness, arsenic, BOD air quality monitoring is considered then conventional method (Biochemical Oxygen Demand), Calcium, chloride, chlorine, used for data generation realizes burdensome, time consuming, COD (Chemical Oxygen Demand), DO (Dissolved Oxygen) never ending process and sometime fails even. Consequently, it is have been created and its water quality values have been very essential to develop auto data generation techniques which depicted on or closed to SPs [3]. will generate large data to support conventional method (form a team of researcher, carry-out field work, collect sample, test it with the instruments and generate data). This research paper introduces a web-based software developed for the Kanhan river to generate river water quality data for its modelling. Software uses different data generation techniques like: data extraction, data estimation, data generation using public-partnership and data generation using scientific software respectively. This research paper also demonstrates its public portal utilized for the public health management. Keywords— data generation, Extraction, Data Estimation, River Engineering, Information System, Modelling, public health I. INTRODUCTION River is very important for living organism, over exploitation and urbanization has ecologically stressed the Indian rivers. Specifically, If Kanhan River is considered, then this river also stressed environmentally. Kanhan River originates from Satpura hills situated in Chhindwara districts of Madhya Pradesh, river progresses to Nagpur district of Fig. 1 Considered path for the Kanhan River Maharashtra and lastly confluences the Wainganga River [1]. To develop this software for Kanhan River, around 300Kms Web-pages are published using tomcat web server [4] and of Kanhan River stretch from Amla Dam, Madhya Pradesh to geo-information is published using Geoserver 2.11 [5]. Gosekhurd Dam, Maharashtra, India is considered along with Software uses different techniques for information generation the water sources confluences the river. Software records as shown in Fig. 3. and discussed in detail as follows. several water quality parameters and disseminates using its public and private portal. Nearby villages and Nagpur (Maharashtra, India) city uses Kanhan river water for drinking and domestic usage; Research team is kin in conserving Kanhan River, hence developed this software to address river issues in the public domain. II. SOFTWARE METHODOLOGY In view to generation information for Kanhan river, SPs (Study points) are imposed on the river stretch at 100-meter distance (Fig. 2). Bing Map is used as a base map [2], which is overlaid with different information layers. Several information layers like: ESP (Exchangeable Fig. 2 Study points imposed on the river path Sodium Carbonate), FC (Faecal Coliform), Hydrogen Carbonate, Iron, Magnesium, Nitrate, Phosphate, Potassium, IJSRCSAMS Volume 7, Issue 6 (November 2018) www.ijsrcsams.com ISSN 2319 – 1953 International Journal of Scientific Research in Computer Science Applications and Management Studies Data Generation Data Extraction Public Partnership Scientific and modelling software Bulk Data upload Data Estimation Data Share and update Fig. 5 login screen. Fig. 3 Information generation techniques A. Data generation (Conventional method). Authenticated users from different organization can register into the software (Fig. 4). Registration request of the user reaches to system administrator and after approval from administrator research-team user gets registered into the software. Using this simple procedure software forms a community of the researchers. Research-team user can login into the software (Fig. 5) and can generate main data. Team can carry out field work and collect water quality data to upload into the system. To generate data from all possible modes, research-team user is facilitated with utilities like: data share, data upload, data download (Fig. 6), point by point Fig. 6 Data Download data upload and Bulk data upload (using excel file). Apart from basic data generation, research team can also generate data using data extraction and data estimation techniques B. Public-partnership. provided by the software which is discussed associated section. If recent trend is studied, then data generation using public- partnership (It is also called as citizen assisted systems) is observed more popular and effective technique. Most of the physicochemical parameters can be recorded in the presence of water. Guest-user of the software can record presence or non-presence of the water on or closed to the SPs. Although such data has low significance in the system, but such feedback is very useful in validating the data. Software maintains different Boolean flags to identify the modes of the data generation. Among these, if guestuser flag is set to TRUE, it means data is generated by the guest-user and public- partnership mode is used for the data generation. When guest- user generates a data, software automatically sets guestuser flag to TRUE and by-default sets approved flag to FALSE. Data generated by guest-user won't be reflected to research- team till approved flag remains FALSE. Administrative user of the software can observe such data and can set approved flag to TRUE on ensuring its validity. Fig. 4 User registration. IJSRCSAMS Volume 7, Issue 6 (November 2018) www.ijsrcsams.com ISSN 2319 – 1953 International Journal of Scientific Research in Computer Science Applications and Management Studies causes hurdle as R&D needs data in complete form. Therefore, such missing data essentially need to be best estimated. C. Bulk data upload. Software uses two methods for data estimation Mean and Maximum Likelihood Estimation (MLE) method respectively. Depending upon nature of data user can opt method for the data estimation. As indicated in the Fig. 9, user can click on "Data generation" link and then can click on "Estimate data" link provided in the appeared page. As shown in Fig. 10 software is featured to estimate data for single SPs or multiple SPs. software considers non-zero values in the given range of Fig. 7, Bulk data Upload SPs, calculates mean and returns estimated value (or values) This software supports data generation using scientific to single or multiple SPs, if user selects Mean option for the software; most of the scientific and modelling software data estimation. Similarly, software calculates MLE for every generates data in excel file format or CSV (Comma Separated no-zero value available in the given range and returns value Value) file format. Considering this, software facilitates quick (or values) to single or multiple SPs for which MLE data upload utility (Fig. 7). However, bulk-data upload calculated is high. While estimating and returning values to requires authentication from administrator; if data is generated the missing SPs, software prioritizes existing non-zero-data using bulk data upload utility, then such data will be reflected and leave them unchanged while estimation. in the software and will be available for the research-team, if administrator-user approves it. D. Data share and update. Developed software ensures all-time availability of the data. Generated data is geo-mapped, thus offers great visualization, look & feel of the data. Using such features research-team user can validate and confirm the data. Recorded physicochemical parameters are depicted as information layer (Fig. 8). This ready platform offers great navigation features for the research-team and encourages them to generate more data. Provided shareware environment also offers collective visualizations, which enables researcher to study data and its connectivity. Subsequently, observing data and their inter- relationship next phase of data can also be generated. Fig. 9 Data estimation Software has seamless feature to generate diverse information, which can be used for executing many Research and Development (R&D) projects associated with the rivers [6]. Fig. 10 Mean and MLE options for data estimation (MEAN) Fig. 8 Data shared among the users and update (MLE) P( ) = . E. Data Estimation. River goes through valleys and dense forest, thus data Where n is total number of samples, = 3.14, x is sample value collection at every SPs won't be possible. Apparently, for MLE need to be calculated generates missing data at essential SPs. Such missing entries IJSRCSAMS Volume 7, Issue 6 (November 2018)
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