Project Title: Rainfall Trends Analysis

Objective:

To analyze historical and real-time rainfall data to detect patterns, anomalies, and long-term trends—supporting climate change research, agricultural planning, and flood risk management.

Key Components:

Data Collection:

Sources include:

Meteorological agencies

Satellite rainfall estimates

Weather stations

Hydrological databases

Captures data such as daily/monthly precipitation, geolocation, and seasonal patterns.

Data Preprocessing:

Cleans and interpolates missing or inconsistent records.

Normalizes data to account for different units or sources.

Aggregates data to appropriate temporal (e.g., monthly, yearly) and spatial resolutions.

Exploratory Data Analysis (EDA):

Identifies seasonal cycles (e.g., monsoons).

Detects extreme events and periods of drought or heavy rainfall.

Uses histograms, time-series plots, and spatial heatmaps.

Trend Analysis:

Applies statistical methods (e.g., Mann-Kendall test, linear regression) to identify increasing or decreasing rainfall trends.

Assesses climate variability over decades.

Forecasting Models:

Uses ARIMA, Prophet, or LSTM to forecast future rainfall patterns.

Incorporates climate indices (e.g., ENSO, IOD) as predictors.

Spatial Analysis & Mapping:

Generates GIS maps showing regional variation in rainfall.

Highlights vulnerable zones for droughts or floods.

Applications & Impact:

Informs crop selection and irrigation planning.

Assists urban planners and disaster management teams.

Contributes to climate change adaptation strategies.

Outcomes:

Reveals long-term climate change indicators via rainfall patterns.

Enables data-driven planning for agriculture and water resource management.

Supports early warning systems for extreme weather events.

Promotes sustainable environmental policy and infrastructure design.