ES Model development Dr. Ahmed Elfaig The ES attempts to predict results from available information, data and knowledge The model should be able to infer how some given action e.g. reduction or increase in the number of ( Cars, buses, motorbikes or distance between the noise sources and the receivers results in decrease of noise level. ES should make intelligent prediction by using input data about given situation to infer further consequences or out comes based on the existing knowledge it has. Real time data

ES Model development The ES predicts noise level based on the given parameters and the real time data. The system has three main parameters: 1.Dynamic parameters Are the most important parameters from modeling point of view 2. Constraints Constraints in the model re also important parameters. It can be defined as pre-requisite that must be met before an action is carried out e.g.

ES Model development 0 ≤ cars (unit) 0 ≤ buses (unit) 0 ≤ motors (unit) 0 ≤ distance (m) 3. Static Parameters Static parameters are fixed data such as: a.Road speed approximately 50km/hour b.Wind effects should be under control (should be isolated by wind screen)

ES Development Phases In developing ES several steps and processes were involved as shown in Figure 1. 1.Identification Phase: includes problem identification, problem characterization, Development facilities (Clips, hardware ….etc) 2.Conceptualization Phase 3.Formulation phase 4.Implementation phase 5.Testing phase 6.System outcome phase

ES Development Phases

The testing phase aims at showing, validating and verifying the model and software of ES functions. It shows the overall structure of the system and its knowledge (verification shows no bugs or technical errors) Traces syntax errors that may prevent the rules from firing and fixing such errors

Goals of Verification Make sure there are no: Bug Technical errors Removing errors Incompleteness Ambiguity Inconsistency in system function

Knowledge Acquisition Knowledge acquisition : Is processes involve collecting, eliciting, organizing, analyzing and interpreting the knowledge that human experts use when solving particular problem Knowledge acquisition involve includes knowledge refinement, validation and verification.

Importance of Knowledge acquisition Importance of knowledge come from the fact that : The power utility of any system depends on underlying knowledge quality The clients acceptance of the system depends on the validity of the knowledge it has.

Type of knowledge Declarative knowledge: which is used to describe the problem characteristics and concepts Heuristic knowledge: Knowledge used to make judgement or strategic rule of thumb.

VALIDATION Comparison of research output (knowledge) with the heuristic of expert in the field Comparison of the research output with known results

TYPE OF VALIDITY Content validity Criterion validity Objective validity Subjective validity

Content Validity Results of the system or research test against experts The system models test against other models

Criterion validity Level of expertise provided by the research or a system

OBJECTIVE VALIDITY Actual system Performance Actual outcome

SUBJECTIVE VALIDITY Research results or system performance compare to experts.

VALIDATION PROCESSES Known results: for example WHO Blind performance test: Compare the results against human experts Face validation: Qualitative procedure to test the results Subjective evaluation: Evaluation of the results through consultation with experts

Validation: Assessments Results STDMeanParameters considered Variable: 1.Completeness 2.Importance Output: 1. Important results Performance: 1.Right results 2. Complete results Explanation; 1.Why certain variables are needed

Field Testing % complianceNumerical differences Research or system output Observed Results