This document introduces a project to build a flood rainfall disaster prediction website. It will provide flood warnings and rainfall analysis to help with disaster response and relief efforts. The website will ingest and analyze flood and rainfall data to train machine learning models. The models will then be used to serve real-time predictions and visualizations through a user interface. This will help alert the public of potential floods and allow preparations to minimize loss.

1. WELCOME TO OUR PROJECT
COURSE TITLE : SYSTEM ANALYSIS AND DESIGN
COURSE CODE :CSE-326
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2. PROJECT ON FLOOD RAINFALL DISASTER
PREDICTION WEBSITE
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3. INDEX
Topics Slide Number
Problem statement 5
Approach 6
Workflow diagram 7
Core Contents 8
Features 9
Intelligence 10
Background study 11
Limitation 12
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4. PROBLEM STATEMENT
DISASTER RELIEF AND RESPONSE
FLOOD PREDICTION
RAINFALL ANALYSIS
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5. APPROACH
 Disaster response is the second phase of the disaster management cycle. It consists of A
number of elements, for example, warning, evacuation, search and rescue, providing
immediate assistance, assessing damage, continuing assistance and the immediate
restoration.
 So among all, we will work upon warning system for floods. In this, we will provided A user
interface to the common public to check the level of water flow in rivers in future and we
will provide A mechanism of notification if there is any possibility of flood due to any river
in nearby future (12 months).
 Along with that users can also see the historical trends of rivers flow and can visualize the
rainfall patterns also in their sub-division (area). In Bangladesh. South Bengal in Bangladesh
is A very flood prone area. There are one or two large cyclones in the region each year, and
these cyclones produce tidal waves. Many people suffer as A result. This is why we are
interested in working with floods and cyclones.
 So with that much information beforehand and knowing the chances of the flood in any
region we can prepare ourselves and alert the local public so that loss would be minimum.
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6. WORKFLOW CHART
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7. CORE COMPONENTS
Data ingestion – ingest data related to flood and rainfall.
Data analyzer – data preparation - analysis, exploration, cleaning, feature
extraction, etc.
Ml training – machine learning model training, fine tuning and evaluation.
Model store – trained model weights are persisted in file system.
Model serving – serving flask jinja calls with trained models.
User interface – ui provides real-time graphs and data analysis.
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8. FEATURES
The government to predict floods and rainfall analysis in various vulnerable regions
of the country beforehand so that safety measures can be taken.
Effective real-time flood forecasting models could be useful for early warning and
disaster prevention.
Flood forecasting can also make use of forecasts of precipitation in an attempt to
extend the lead-time available.
Rainfall analysis can help in anticipation of crop yield and gross production value in
the region.
Forecasting flow rates and water levels for periods ranging from a few hours to
days ahead.
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9. INTELLIGENCE
Model can be deployed in flood prone areas for advance warning.
Can be used for early decision making of disaster relief responses.
Can be used for various industrial companies whose products are affected by
rainfall patterns.
Can be used in agriculture industry for proper planning beforehand.
Can be used in predicting suitable climatic condition of areas for transportation of
goods and services for companies.
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10. BACKGROUND STUDY
Basically, most of the team members of the team belong to the south
part of our country. We all know that part is very disaster-prone.
We are witnessing the plight of the people of the southern region.
So we have adopted this modern approach.
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11. LIMITATION
Sometimes ,prediction could wrong.
People can face strong networking issue.
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12. Thank You
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13. Any Question ?
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