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README.md

@@ -1,48 +1,60 @@
-# Flood Prediction Regression
-
-Flood Prediction Regression is a machine learning project aimed at predicting the probability of floods based on various environmental and infrastructural factors. By leveraging regression techniques, this project seeks to provide accurate flood probability predictions to aid in disaster preparedness and risk management.
-
-# Features Description:
-
-The following features are considered in this flood prediction model:
-
-MonsoonIntensity: Measures the intensity of the monsoon season, which directly impacts the likelihood of flooding.
-
-TopographyDrainage: Assesses the efficiency of drainage systems based on the topography of the area.
-
-RiverManagement: Evaluates the management practices of rivers, including dam operations and riverbank maintenance.
-
-Deforestation: Tracks the rate of deforestation, as reduced tree cover can lead to higher runoff and increased flood risk.
-
-Urbanization: Measures the extent of urban development, which often leads to reduced natural drainage and increased flood risk.
-
-ClimateChange: Accounts for changes in climate patterns that may contribute to unusual rainfall and flooding events.
-
-DamsQuality: Assesses the structural integrity and maintenance of dams, which play a crucial role in flood control.
-
-Siltation: Measures the accumulation of silt in water bodies, which can reduce water flow capacity and increase flood risk.
-
-AgriculturalPractices: Evaluates farming practices that may impact soil stability and water runoff.
-
-Encroachments: Tracks illegal constructions and encroachments on floodplains and natural water channels.
-
-IneffectiveDisasterPreparedness: Measures the inadequacy of disaster preparedness plans and response mechanisms.
-
-DrainageSystems: Assesses the capacity and effectiveness of urban and rural drainage systems.
-
-CoastalVulnerability: Evaluates the susceptibility of coastal areas to flooding due to sea-level rise and storm surges.
-
-Landslides: Monitors the occurrence of landslides, which can block rivers and cause flooding.
-
-Watersheds: Assesses the health and management of 
-watersheds, which are crucial for natural water flow regulation.
-
-DeterioratingInfrastructure: Measures the condition of infrastructure such as roads and bridges, which can affect flood response and recovery.
-
-PopulationScore: Considers the density and distribution of the population in flood-prone areas.
-
-WetlandLoss: Tracks the reduction in wetland areas, which naturally help in flood mitigation.
-
-InadequatePlanning: Evaluates the shortcomings in urban and regional planning that can exacerbate flood risks.
-
-PoliticalFactors: Considers political influences and policies that impact flood management and preparedness.
+---
+license: apache-2.0
+title: Flood Prediction
+sdk: gradio
+emoji: 🌍
+colorFrom: blue
+colorTo: blue
+pinned: true
+short_description: Flood Prediction using Machine learning
+---
+
+
+# Flood Prediction Regression
+
+Flood Prediction Regression is a machine learning project aimed at predicting the probability of floods based on various environmental and infrastructural factors. By leveraging regression techniques, this project seeks to provide accurate flood probability predictions to aid in disaster preparedness and risk management.
+
+# Features Description:
+
+The following features are considered in this flood prediction model:
+
+MonsoonIntensity: Measures the intensity of the monsoon season, which directly impacts the likelihood of flooding.
+
+TopographyDrainage: Assesses the efficiency of drainage systems based on the topography of the area.
+
+RiverManagement: Evaluates the management practices of rivers, including dam operations and riverbank maintenance.
+
+Deforestation: Tracks the rate of deforestation, as reduced tree cover can lead to higher runoff and increased flood risk.
+
+Urbanization: Measures the extent of urban development, which often leads to reduced natural drainage and increased flood risk.
+
+ClimateChange: Accounts for changes in climate patterns that may contribute to unusual rainfall and flooding events.
+
+DamsQuality: Assesses the structural integrity and maintenance of dams, which play a crucial role in flood control.
+
+Siltation: Measures the accumulation of silt in water bodies, which can reduce water flow capacity and increase flood risk.
+
+AgriculturalPractices: Evaluates farming practices that may impact soil stability and water runoff.
+
+Encroachments: Tracks illegal constructions and encroachments on floodplains and natural water channels.
+
+IneffectiveDisasterPreparedness: Measures the inadequacy of disaster preparedness plans and response mechanisms.
+
+DrainageSystems: Assesses the capacity and effectiveness of urban and rural drainage systems.
+
+CoastalVulnerability: Evaluates the susceptibility of coastal areas to flooding due to sea-level rise and storm surges.
+
+Landslides: Monitors the occurrence of landslides, which can block rivers and cause flooding.
+
+Watersheds: Assesses the health and management of 
+watersheds, which are crucial for natural water flow regulation.
+
+DeterioratingInfrastructure: Measures the condition of infrastructure such as roads and bridges, which can affect flood response and recovery.
+
+PopulationScore: Considers the density and distribution of the population in flood-prone areas.
+
+WetlandLoss: Tracks the reduction in wetland areas, which naturally help in flood mitigation.
+
+InadequatePlanning: Evaluates the shortcomings in urban and regional planning that can exacerbate flood risks.
+
+PoliticalFactors: Considers political influences and policies that impact flood management and preparedness.