Machine Learning Applications in Flood Predictions

The reliable streamflow prediction plays a vital part in flood risk assessment and mitigation. The substantial increase in population, global warming, and climate change effects are the main key issues that have significant impacts not only on global water resources but also in Pakistan. The frequen

Project Title

Machine Learning Applications in Flood Predictions

Project Area of Specialization

Artificial Intelligence

Project Summary

The reliable streamflow prediction plays a vital part in flood risk assessment and mitigation. The substantial increase in population, global warming, and climate change effects are the main key issues that have significant impacts not only on global water resources but also in Pakistan. The frequency of floods has increased in the last few years in the country, which emphasizes the importance of efficient practices needed to adopt for various aspects of water resource management such as reservoir development, water sustainability, and water supply. The traditional streamflow prediction techniques are tedious, time-consuming and demand a large amount of data which sometimes are not possible to collect in data scarce regions. In this work, we evaluate different Machine learning models to predict streamflow and validate its efficiency at the upper Indus basin (UIB), Pakistan.

Project Objectives

To analyze the efficiency of different machine learning models and their efficiency for streamflow prediction. Enhancing the precision rate of hydrological forecasting is very important for UIB and a reliable flood prediction along with the right lead time can provide future precautions of the forthcoming flood event. Although, complete safety is impossible by using these models, but it can provide timely and accurate predictions of flood crests, flood magnitude, and flood duration that save huge amounts of money and countless lives can be saved.

Project Implementation Method

Methodology: The methodology of this project will follow the below procedures:

• Data exploration (Temperature, Precipitation and Streamflow), cleansing and preparation
• Measure the monthly, seasonally  and yearly streamflow variation at different outlet in UIB
• Apply different machine learning models at Besham Qila and compare their results.

Benefits of the Project

Streamflow prediction is so important for Pakistan’s economy and day to day life is highly based on agriculture. Around 60% of the total population lives in rural areas and majority of their work related with agriculture. About 60% of Pakistan’s geographical area is used for agriculture. The Pakistan’s agriculture, which accounts for 18.5% of the gross domestic product (GDP), is largely affected from streamflow. Timely predict the streamflow which will save our economy and rural as well as urban life.

Technical Details of Final Deliverable

Outcomes: The outcome of this would help to analyze the efficiency of different machine learning models and their efficiency for streamflow prediction.

Enhancing the precision rate of hydrological forecasting is very important for UIB and a reliable flood prediction along with the right lead time can provide future precautions of the forthcoming flood event. 

Although, complete safety is impossible by using these models  but it can provide timely and accurate predictions of flood crests, flood magnitude, and flood duration that save huge amounts of money and countless lives can be saved.

Final Deliverable of the Project

Software System

Core Industry

IT

Other Industries

Agriculture , Food

Core Technology

Artificial Intelligence(AI)

Other Technologies

Others

Sustainable Development Goals

No Poverty, Zero Hunger

Required Resources

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