THE IMPROVEMENT OF GIS BASED DECISION SUPPORT SYSTEM FOR PEST CONTROL IN ENVIRONMENTAL HEALTH PROTECTION Technical Field

This invention is about control of vectors and pests harmful to the environment. The invention uses a GIS (Geographical Information System) based decision support system for environmental health protection and control vector/non-vector pests. Previous Technique

Pest control has become an important aspect of modern world. Pests are considered to be the source of numerous diseases. It is not possible to lead a healthy life on an environment infested with pests.The pests in question make habitats and our environments inhabitable. Especially, flies and pests rapidly reproduce in summer.

Manpower and chemicals are used to terminate pests. For controlling pests, chemicals are sprayed to the habitats of pests by manually or by using some tools. The goal of this disinfection process is to terminate pests. However, it is not possible to accurately and quickly obtain retrospective information about pest control activities using this method. Besides, there are no details about the conditions of pest control. Criteria related to habitats and biological cycles of vectors and pests, such as temperature, humidity, height, rainfall etc., are not taken into consideration during pest control. Making assessments about the number of workers used for pest control, used pesticide formulation, the effective dosage of the active agents and formulations used on certain pests and success rates is not possible.

The United States of America patent document with application number US2007/0143088, which is the state of the art, mentions a system and method for foreseeing conditions of insect/pest bites. The invention in question offers a system regarding current and future bug bite conditions of a certain geographical region. The system's user login foresees most realistic future conditions by learning from user experiences and using artificial intelligence.

Brief Description of Invention

The goal of this invention is to ensure the use of digital data for vector and pest (insect) control. Another goal of this invention is to ensure that vector and pest (insect) control methods are retrospectively recorded with a variety of details.

Another goal of this invention is to offer digital platform where the public can have active role in the process of controlling vectors and pests (insects).

Another goal of this invention is to create new vector and pest (insect) control methods based on data of the past practices.

Another goal of this invention is to create a digital, learnable and fruitful platform which will be used for determining the best control method for related vectors and pests (insects).

Another goal of this invention is to ensure the persons access to the system (1) via mobile applications and obtain respective information.

Another goal of this invention is to ensure certain functions performable with computer mouse to be also performance with audio commands.

Detailed Explanation of Fifures and Numbers

The "GIS (Geographical Information System) Based Decision Support System for Vector and Pest (Insect) Control in Environmental Health Protection" made for achieving the goals of this invention has beeb illustrated on the enclosed figures and these figures are:

Figure- 1 : General view of system architecture components

Figure-2: General View of System Architecture

Figure-3: General View of Team Tracking System

Figure-4: General Flow of Web Menu

Figure-5: General View of Vector Tracking System

Figure-6: General View of Habitat Tracking System

Figure-7: General Flow of Mobile Media

Figure-8: General View of Decision Support System Inferences

Figure-9: General View of Decision Support System

Figure- 10 : General View of Mobile Public Information Module

Figure- 11 : General View of Command Line and Audio Control Module

Figure-12: General View of Artificial Intelligence Based Work Development Module Figure-13: General View of Neural Web Based Decision Support Module

The parts illustrated on the figures are numbered one by one and the numbers correspond to the items given below:

1. GIS based decision support biotekcbs for pest control in environmental health protection 2. Surrounding human population 3. Applicators

4. Analyst

5. Chief in charge

6. Field personnel

7. Authorized Unit

8. Public

100. Team Tracking System

200. Web menu

300. Vector Tracking System

400. Habitat Tracking System

500. Mobile Application System

600. Decision Support System

700. Habitat Related Inferences

800. Vector and Pest Related Inferences

900. Pesticide Related Inferences

1000. Application Related Inferences

1100. Complaint Related Inferences

1200. Field Personnel Related Inferences

1300. Inferences Related Tools Used

1400. Weather Forecast Related Inferences

1500. Inferences Related Pesticide Used

1600. Mobile Public Information Module

1700. Command Line and Audio Control Module

1800. Artificial Intelligence Based Work Development Module

1900. Neural Web Based Decision Support Module

2000. Smart Watch Communication Module

2200. Desktop Application Module

2300. Multi-Live Connection and Video Conference Module

Detailed Explanation of Invention

The GIS based decision support biotekcbs for pest control in environmental health protection (1) includes the surrounding human population (2), applicators (3), analyst (4), chief in charge of application (5), field personnel (6), authorized unit (7), team tracking system (100), web menu (200), Vector tracking system (300), habitat tracking system (400) and mobile application system (500).

The GIS based decision support biotekcbs for pest control in environmental health protection (1) has two main sides. One of these two is the surrounding human population (2) which includes the applicators (3) and users. Surrounding human population (2) has two categories affected by the application; authorized unit (7) and public (8). Applicators (3) are divided into three categories; analyst (4), chief in charge of application (5) and field personnel (6). The Analyst (4) is responsible for reports (4.1) and data management (4.2). The reports (4.1) can be issued online or on any end device. It is possible to issue region-based, team-based and location-based reports (4.1). The reports (4.1) are related to the habitats, vectors, application, traps and pesticide reports. The chief in charge of application (5) is responsible for identifications, assignments and tracking. Defining region and team on digital map (5.1), pest identifications (5.2), vector identifications (5.3), pesticide identifications (5.4), application identifications (5.5), defining application duties (5.6), assessment of applications made (5.7), region and team tracking (5.8) are the duties of chief in charge of application (5). The field personnel (6) are in charge of formations and actions. Forming a habitat (6.1), forming vector (6.2), discoveries (6.3), traps (6.4) and applying (6.5) are the responsibilities of the field personnel (6).

The authorized unit (7) is responsible for the report/analysis (7.1) and monitoring the region (7.2). The public (8) can be defined as restricted user. The public (8) can file applications (8.1) and follow up the application (8.2).

The GIS based decision support biotekcbs for pest control in environmental health protection (1) has a server (A) and an end unit. The end unit can be a computer connecting to the system over web (200). Mobile devices are the other devices which can be used as end units.

The team tracking system (100) ensures collecting information about the teams involved in pest control and the environment they work in. The members of team tracking system (100) use tablet computers as end units. Tablets are able to use GPS to give location data to the system. The team tracking system (100) has the following steps; receiving location data from mobile media (100.1), having live video connection (100.2) and receiving application details from the server (100.3). Vector tracking system (300) provides information about the pests and habitats of pests. The vector tracking system (300) has the following steps; vector type selection (301); visual display of results on the digital map and receiving location data (300.1) and receiving data on vector formation time and applications made (300.2). Habitat tracking system (400) is the system providing data about the habitat. Web (200) connection to the system is possible and data required can be obtained from the server. The habitat tracking system (400) has the following steps; habitat type selection (401); visual display of results on the digital map and receiving location data (300.1) and receiving data on habitat formation time and applications made (400.1).

The GIS based decision support biotekcbs for pest control in environmental health protection (1) offers real-time connection to the system by using end units. These connections are able to upload image, location and such other information to the system. The mobile media of GIS based decision support biotekcbs for vector and pest (insect) control in environmental health protection (1) follows up the following steps within mobile environment; making a live connection request (500.1), entering data through web (200.1), giving feedback (500.2), realization of the application (500.3), choosing the application (500.4). The application selected can be applying pesticide (500.7), setting a trap (500.6) and making a discovery (500.5). The application type selection (500.4) can be one of these three applications. After determining the habitat (500.8), the location can be selected from the digital map (500.9). The step of choosing location on the digital map (500.9) follows the discovery (500.5) step. Once the location is selected from the digital map (500.9), the type of habitat is selected (500.10). The characteristics (500.11) of the type of habitat selected (500.10) are entered. A control will determine whether there are vectors or not (500.12). If there are vectors, vector type and details are entered (500.13) and the data are recorded on the database of the server (500.14). If there are no vectors, the record will be on the database of the server (500.14). In addition to these steps, there are habitat list control (500.15) and habitat selection (500.16) steps. After selecting the habitat (500.16), the step of entering characteristics (500.1 1) comes. The step of entering characteristics (500^1 1) is followed by the control to determine whether the habitat is active or not (500.17). If the habitat is not active, there will be no process (500.18). If the habitat is active, the type and life stage are controlled (500.19). Video and/or image of the habitat can be added to the system at this point ( 00.20). The decision support system (600) make connections with the information on the database of server (A) to make inferences and these inferences are provided to the user in writing and/or visually. The decision support system (600) has the following steps:

- Web user enters data (601) to the system (1),

- Receiving location, time, image, video, habitat type, vector, vector life cycle/stage details from mobile devices (602),

- Receiving humidity, wind, rainfall, temperature, pressure etc. atmospheric details of the location of the habitat from meteorology stations (603),

-Biotekcbs (1) creating feedback based on such information and returning the same to the web user (604),-Decision support system (600) analysing the information periodically and saving the results on database (605),-Results of decision support system (600) monitored by the authorized user as text or visually on mobile mediate or web, or rerunning the decision support system with the new parameters obtained by the system user to get new results (606).

The system (1) is able to give certain results/inferences by using the data input. These inferences enable the person using the system (1) at that moment to have insight and facilitate making the best decision regarding the action to be selected.

The habitat related inferences (700) out of these inferences are as follows: future-related formation inferences based on associating time and location details available on the database for habitats recorded and analyzing previous data; inferences about the vector activity of habitats to be active in the future, habitats to be created in the future and vectors to be active on these habitats based on analysis of vectors and pests (insects) on the database which are associated with the habitats recorded on the system and determined to be active periodically on that habitat; inferences about the habitats to occur and related complaints based on the complaints recorded on the database with location details and association with the habitats in that area or nearby; inferences about the habitats to be created and applications to be used based on relationship between the database location information and applications recorded, habitats at or nearby that location.

The inferences about vectors and pests (800) are as follows: obtaining approximate location and time data about future vector formations by analyzing location and time data related to the ID available on the database for vector and pests recorded; obtaining approximate data about the vector diseases caused by vectors and the level of plague (endemic, epidemic, pandemic) to be caused by analyzing vector records and related data sets on the database through vector-disease data processed to the system; inferences about the future complaints based on Vector and pest activity and distribution, formation by analyzing the complaints saved on the system and vector-pest information in these complaints; inferences about future applications based on applications associated with vectors and pests, vector and pest formations by analyzing the application records. Besides, approximate resistances of vectors and pests to active agents and insecticides and activity in the future are obtained by analyzing the results of resistance and biological tests made in the laboratory and on field colonies. The inferences related to the pesticide (900) have access to the information about pesticide used by analyzing the records of applications made and make estimates related to the future based on time and location details on the records as well as estimations based on pesticide details obtained by analyzing the records of previous applications and dosages on the records and pesticide efficiency estimations obtained by analyzing resistance information and application- vector and pest formation, datasets.

The inferences related to the applications (1000) make estimations related to the future for applications recorded by associating and analyzing the meteorology, time and location details on the database.

Inferences related to the complaints (1100) make future estimations related to the applications, and vector and pest habitat formation, for the complaints recorded by analyzing the time and location details on the database. Inferences about the field personnel (1200) estimate future location- time based on time and location details available on the database and by analyzing GPS data of the personnel involved in the applications records as well as making estimations for the future based on efficiency data of the applications made by the field personnel and by analyzing the related datasets.

The inferences about the tools/vehicles used (1300) estimate future location-time based on the time and location information on the database and analyzing the GPS information recorded as well as estimating future fuel consumption by analyzing the GPS data record and related datasets and estimating future maintenance works by analyzing GPS information recorded and related datasets. Inferences related to the pesticide used (1500) include graphical and numeric determination of daily- weekly-monthly-annually uses of chemicals and biological pesticides used for pest control and stock follow-up.

Inferences related to weather forecast (1400) analyze information received from meteorology stations to have estimations based on 12-24 hour, instant weather forecasts and previous data as well as estimating future vector and pest population density and distribution by associating meteorology datasets with habitat data and taking into consideration habitat- meteorology relationship parameters; mathematical estimations of future biological cycles by associating meteorology datasets and vector data and taking into consideration vector and pest biological cycle-meteorology relationship parameters; estimates related to the success of application to be used by associating meteorology datasets with application data and vector formation datasets; estimations related to biological efficiency and resistance formation of the pesticide to be used based on meteorology data; inferences related to work performance of field personnel based on meteorology data and inferences related to the impact of meteorology data on vector control efficiency of the vehicles and equipments used on the field. The System (1) has a trap application. The trap application determines the density and distribution of vector and pest population on the location where the trap is set. The density and distribution determined facilitates deciding the method (Residual, ULV, FOG, Mist Blower etc.), time and duration of pest control. Besides, it is an inevitable tool of determining active agents and products (insecticides) to which vectors and pests are sensitive and resistant. Thus, it is possible to choose an active agent and insecticide effective on vectors and pests. For this purpose, the vectors and pests caught on traps are taken to laboratories. The live vectors and pests in the trap are categorized based on species and colonies are formed. If the colonies on fields and laboratory resist to a certain active agent during active agent and formulation trials, the active agency in question will not be used. As for the biological efficiency test of formulations (insecticides), products offering an efficiency rate of 95 % and more on laboratory colonies and 80 % and more on field colonies will be included in the used applications. Formulations failing to meet the criteria required by local authorities will not be used. Mobile public module (1600) ensures instantaneous informing of the people. The public (8) may not access the entire informatio in the system but only to the portion made accessible to the public. Besides, the public (8) may enter information into the system (1). This information is in quality of records not having an absoluteness. The definiteness/absoluteness of the information entered by the public (8) may not be finalized by the system (1) or system administrator. The public (8) may make complaints (A), vector (B) and Habitat (C) processes via the cbs system (1). These are inform complaint (A.l) for complaint (A), complaint monitoring/follow up (A.2), inform vector (B.l) for vector (B), vector follow up (B.2), inform habitat (C.l) for habitat (C) and habitat follow up (C.2).

The command line and audio control module (1700) enables to perform the acts of the computer mouse (for instance clicking on a key) using sound/voice. The sound package is received with a microphone and processes as to enable the performance of the command. While the user is in the web system (200), the command line of the system means the adjacent audio control option. The command list permitting audio control includes certain commands with several characteristics as to be performed through the command line of the system. These characteristics consist of two main qualities. The first characteristic is that the command to be used is of a type which may be functioned without any parameter under the command line of the system. The second characteristic is that the command is of a type which does not include any branching with any other command included in the command line list. The audio functioning of these commands then ensure to transfer the results of the system commands to the user both on biotekcbs and in audio. The command audio option (1700A) becomes active when the user is in cbs web site (200). Thus, the system has a quality to process the audio expressions. The audio command of the user is transferred to the audio processing module (1700B) as in the form of sound data. The processes cbs command line command process (1700C) turns the sound data to a command and translates the same to a form understandable by the computer. Thus, cbs web site (200) displays the process to be performed. Artificial Intelligence Work Development Module (1800) may make certain inferences by using system (1) rules and system (1) data. Thus, the field personnel (6) may make work and duty definitions for any work performed. The work definitions and physical conditions may be used among the information within the database. The artificial intelligence rule tables (1800A) may use the works definitions and physical condition information within the database. The artificial intelligence algorithm (1800B) interacts with the database. When a duty formation request arrives from the field personnel (6), the artificial intelligence algorithm (1800B) starts interaction with the database and rule tables (1800A) and finds out the corresponding results with such duty. Neural web based decision support module (1900) uses the annually recorded data for training the artificial neural web. When the trained web is activated, any recorded dataset is used for obtaining certain foreseen information. The neural web module (1900) produces results related to the application coordinates thanks to the feeding of the existing information. The system (1) keeps in the database the habitat records, vector formations, complaint information together with respective coordinate data, environment tempature, ph and similar physical sizes. The applications performed are also taken under record in the same manner. The system (1) receives the habitat, vector, complaint and physical size information in the neural web (1900) and, in turn, corresponds the same with the performed application data and realizes the required training on the neural web. The trained neural web (1900) performs a much more effective inferencing about the application to be made instead of analysing and monitoring hundreds of data. The records kept in the database are used as inputs and are used for the training of the neural web (1900 A). Thus, application records (1900B) are produced as outputs in the database. When new information is entered in the trained neural web (1900C), the neural web (1900) uses this information and produces the coordinate information on which the application will be made (1900D).

The Smart Watch Communication Module (2000) is used to increase and ease the field operations of the field personnel (6) and biotekcbs (1). The smary watch is integrated to the mobile software (500) thanks to both its portable size and touch and audio control features. The field personnel (6) may manage the duties, complaints, message, live connection request etc. communications arriving from the system with said smart watch module (2000). The field personnel (6) may make audio controls, habitat and vector selections.

Desktop Application Module (2200) is module which reaches the system (1) and functions offline. The user may access to the system data (1) without internet connection thanks to the desktop application (2200) and may receive reports, use artificial intelligence and artificial neural web modules in the same manner. The application performs synchronizations with the server through certain intervals and receives the data into the local database. It may respond to any module, save instantaneous communication modules, in the web application when no internet connection exists. Multi-live Connection and Video Conference Module (2300) may make sessions with 1-6 users simultaneously and may provide uninterrupted video display even in weak network webs. The module (2300) ensures the administrator to log in upon the request of the field personnel (6) for necessary situations (when the field personnel may not find an appropriate solution) and invite such session other users deemed favourable as to maintain an online opinion exchange. The administrator may manage the teams through a single session when the teams are required to perform collective duties on the field and may make audio communication with any of the participating client units.