Flood Risk Insights & Recommendations
Turning historical datasets into actionable information and recommendations for farmers, LGUs & PDRRMO
Bataan Province, PH
Sun, 06 Sep 2026
Secure sign in
Data filters217 matching records
Reset Choose a municipality to select a barangay.
') center/cover">
Mission Statement
Enhance flood risk awareness, promote proactive decision-making, and help farmers, agricultural planners, and local authorities better prepare for future flooding disasters in Bataan.
AgriFlood turns historical datasets into insightful, actionable information for evidence-based preparedness.
217 Barangays Analyzed
30 Disaster Events (2019–2024)
11 Years of Rainfall Data (2015–2025)
Forecast Period: 2026–2028
Cumulative Agri Damage
₱817.1M
2019–2024 recorded agricultural losses
Total Affected People
1.25M
Recorded affected individuals, 2019–2024
Peak Rainfall Year
3,805mm
2020 — record annual total
Palay Area Change
6,162ha
19.4% change, 2015–2025; cause not assigned
Targeted Insights by Audience
🌾
For Farmers
Field-level flood preparedness
Key Insights from Data
01.Historically wettest months are July, August, September. Use this observed historical pattern as planning context and check current local advisories before changing farm activities.
02.Palay harvested area changed 19.4% from 2015 to 2025. The dataset does not identify flooding as the sole cause.
03.2 municipalities are in the current high-priority flood screening group. Check the specific barangay before applying preparedness actions.
04.Corn harvested area changed 83.6% across the displayed period. Consult field, soil, drainage, and market information before recommending crop or location changes.
05.Current forecast period: 2026–2028. A temporal flood trend is not published until repeated dated flood observations are available.
Quick Action: Check your barangay's flood risk in Flood Risk Analysis → then review comparable historical events in the Scenario Simulator →.
📊
For Agricultural Planners
DA–Region III & BSWM support
Planning Insights from Historical Data
01.PHP 817.1M in recorded agricultural damage appears in the historical reports. Use it to guide review, not to automatically reallocate production.
02.Flood model screening should be used together with source susceptibility categories, current field validation, and official advisories.
03.Latest precipitation context — 2025 recorded 3,302mm. Use this historical observation for planning, not as a future rainfall forecast.
04.Crop area, production, and yield are separate measures. Review all three before proposing crop expansion or diversification.
05.Forecast values for 2026–2028 are system-generated and remain separate from observations through 2025.
Quick Action: Download the Municipal Flood Screening CSV → and the Crop Forecast → for technical review.
🏛️
For Local Authorities
LGUs & PDRRMO–Bataan
Decision-Making Insights
01.67 of 217 barangays are marked flood-prone in the current dataset. Prioritize technical review according to municipality and barangay results.
02.Recorded infrastructure damage totals PHP 1003.3M across the displayed disaster period. Engineering assessment is required before selecting mitigation investments.
03.Typhoon Egay has the largest recorded affected-individual count in the current eligible history. Verify reporting definitions before using it for capacity planning.
04.Historical disaster model outputs support prioritization only. Model Performance shows the selected model, validation size, and errors for the current run.
05.Municipalities with no current prone flags are candidates for further field review, not automatically safe staging or farming zones.
Quick Action: Review historical event groups in the Scenario Simulator →, then generate the Barangay Risk Report → for PDRRMO coordination.
Historical Data Summary
Rainfall and Agricultural Damage by Year
Historical series shown together for context; this does not prove correlation or causation
Data comparison: 2020 had the highest recorded precipitation (3,805 mm), while 2024 had the highest recorded agricultural damage (PHP 220.52717622M). These are separate datasets.
Monthly Rainfall Risk Calendar
Observed 2015–2025 pattern — preparedness context, not a forecast
Insight: July has the highest historical average accumulated rainfall (504mm). Review planting and preparedness schedules against the full current dataset rather than treating this pattern as a forecast.
Historical Harvested Area Trend
Palay and Corn area changes; flooding is not identified as the sole cause
Data summary: Palay changed by 6,162 ha (19.4%) and Corn by 1,452 ha (83.6%) across the displayed period.
Recorded Damage Components by Year
Historical agriculture and infrastructure damage from supplied reports
Data summary: The highest displayed agriculture-plus-infrastructure total occurred in 2024 within the supplied historical records.
Proactive Preparedness Checklist
📅
Before Typhoon Season
Review barangay flood risk classification (Flood Risk Analysis)
Run scenario simulation for expected storm types
Coordinate with LGU for evacuation center assignments
Review planting schedules against the observed wet-month pattern: July, August, September
Secure crop insurance for high-risk farm plots
Stockpile emergency supplies in low-risk staging areas
🌀
During a Flood Event
Refer to ML risk map for highest-priority barangays
Prioritize LGU/PDRRMO assessment for the current screening leaders: City Of Balanga, Samal
Deploy response teams according to current barangay results and official incident reports
Collect real-time damage data for model re-training
Issue agriculture advisory — do not replant during active flood
Coordinate food assistance using recorded needs and validated historical risk flags
🔄
Post-Flood Recovery
Export damage report from Reports module for LGU filing
Update crop damage data in database for ML re-training
Identify new high-risk areas for risk map update
Issue climate-resilient farming advisories for displaced farmers
Coordinate with DA–Region III for calamity fund release
Review scenario simulator accuracy vs actual damage recorded
Municipal Flood Risk × Crop Planning Matrix
Current eligible flood screening; crop history is province-level and requires local field validation
Municipality Risk Level Flood-Prone Brgy Flood Exposure Crop Advisory Recommended Role
Dinalupihan Moderate 15/46 Lower observed exposure Candidate for further agricultural and hazard assessment Field validation recommended
City Of Balanga High Risk 14/24 Higher observed exposure Agricultural planning and barangay field review recommended Higher preparedness priority
Samal High Risk 9/14 Higher observed exposure Agricultural planning and barangay field review recommended Higher preparedness priority
Orani Moderate 8/23 Lower observed exposure Candidate for further agricultural and hazard assessment Field validation recommended
Hermosa Moderate 6/15 Lower observed exposure Candidate for further agricultural and hazard assessment Field validation recommended
Bagac Moderate 5/14 Lower observed exposure Candidate for further agricultural and hazard assessment Field validation recommended
Abucay Moderate 4/9 Lower observed exposure Candidate for further agricultural and hazard assessment Field validation recommended
Orion Low Risk 3/23 Lower observed exposure Candidate for further agricultural and hazard assessment Field validation recommended
Pilar Low Risk 2/19 Lower observed exposure Candidate for further agricultural and hazard assessment Field validation recommended
Mariveles Low Risk 1/13 Lower observed exposure Candidate for further agricultural and hazard assessment Field validation recommended
Limay Low Risk 0/12 Lower observed exposure Candidate for further agricultural and hazard assessment Field validation recommended
Morong Low Risk 0/5 Lower observed exposure Candidate for further agricultural and hazard assessment Field validation recommended
How Historical Data Becomes Actionable Insight
The AgriFlood data pipeline - from raw records to flood preparedness intelligence
🗄️
Step 01
Raw Data
Current validated Flood/Landslide, precipitation, disaster, crop, and reference-map datasets
🧹
Step 02
Data Cleaning
Missing value treatment, spatial alignment, normalization, feature engineering
🤖
Step 03
ML Training
Task-matched classifiers and regressors compared using validation metrics
📈
Step 04
Analytics
Flood and disaster risk classification, crop production forecasts, and rainfall patterns
💡
Step 05
Insights
Actionable reports for farmers, LGUs, PDRRMO, and agricultural planners
Recommendations for Flood Risk Preparedness
Data-Driven Decision Making
Based on current flood screening, 11 years of observed rainfall, historical disaster impacts, crop records, and the 2026–2028 forecast period — these recommendations support farmers, agricultural planners, and local authorities in preparedness planning.
🌾
Farmers
Field-level preparedness actions
🗓️
Review planting calendar
HIGH
Compare farm schedules with the observed wet-month pattern (July, August, September) and current local advisories.
🌱
Review flood-tolerant options
HIGH
For farms in City Of Balanga, Samal, consider crop-variety and drainage assessment with agricultural officers before changing practices.
🏔️
Verify lower-risk plots
MEDIUM
Use municipality screening to identify areas for field assessment. Check barangay risk, elevation, drainage, irrigation, and land suitability before changing farm locations.
📋
Review crop protection
MEDIUM
Farmers in higher-priority screening areas should review suitable insurance and preparedness options with authorized agricultural offices.
📡
Monitor local advisories
LOW
Use current local advisories together with the observed rainfall history and prepare drainage measures when advised.
📊
Agricultural Planners
DA–Region III strategic guidance
🗺️
Prioritize location field checks
HIGH
Use flood screening to plan barangay-level validation before changing subsidies, zoning, or agricultural programs.
🤖
Use validated ML screening in planning
HIGH
Use the selected model result to prioritize field review together with current local observations.
🌽
Review crop diversification options
MEDIUM
Use crop yield and area trends as planning inputs, then confirm soil, market, water, and barangay flood conditions before recommending expansion.
💧
Prioritize irrigation & drainage infrastructure
MEDIUM
The latest rainfall dataset records 3,302mm in 2025. Use this historical context together with current official advisories.
📑
Review actual and forecast separately
LOW
Observed records end at 2025; system-generated forecasts cover 2026–2028 and must remain clearly labeled.
🏛️
Local Authorities
LGUs & PDRRMO–Bataan
🚨
Prioritize preparedness review
HIGH
67 of 217 barangays are currently marked flood-prone. Prioritize assessment for City Of Balanga, Samal with LGU/PDRRMO validation.
💰
Use recorded losses for contingency planning
HIGH
The highest recorded event damage was PHP 922.3M. Treat this as historical evidence, not a guaranteed future loss estimate.
🏗️
Assess flood mitigation priorities
HIGH
Review recorded infrastructure damage together with engineering studies before selecting drainage, catch-basin, flood-wall, or other mitigation projects.
📦
Verify possible staging areas
MEDIUM
Use lower-priority screening municipalities as candidates only; confirm accessibility, elevation, capacity, and current hazard advisories before designation.
📲
Generate consistent risk reports
LOW
Use the Reports module to prepare dataset-backed Barangay Risk Reports, then have the responsible office verify the report before submission.
Recommendation Priority Key:
HIGH — Act before typhoon season (June). Directly reduces life, crop, and infrastructure losses.
MEDIUM — Strategic actions that improve long-term resilience and reduce recurring damage.
LOW — Monitoring and reporting improvements that strengthen evidence-based planning.
↓
PDF REPORT

Flood Risk Insights Report

Findings, recommendations, municipal results, and limitations.

Download PDF