Riverine Flood Prediction and Early Warning in Mountainous Regions using Artificial Intelligence

TL;DR

This study demonstrates the effectiveness of advanced machine learning models, especially LSTM networks, in predicting river floods in mountainous regions using satellite data, aiding early warning systems and disaster management.

Contribution

It applies and compares multiple AI models for flood prediction in transboundary basins, highlighting LSTM's superior performance and addressing data challenges in mountainous flood forecasting.

Findings

LSTM achieved R2 of 0.96 and RMSE of 140.96 m3/sec.

Short-term forecasts up to five days are highly accurate.

Accuracy declines beyond four days, indicating need for more data.

Abstract

Flooding is the most devastating phenomenon occurring globally, particularly in mountainous regions, risk dramatically increases due to complex terrains and extreme climate changes. These situations are damaging livelihoods, agriculture, infrastructure, and human lives. This study uses the Kabul River between Pakistan and Afghanistan as a case study to reflect the complications of flood forecasting in transboundary basins. The challenges in obtaining upstream data impede the efficacy of flood control measures and early warning systems, a common global problem in similar basins. Utilizing satellite-based climatic data, this study applied numerous advanced machine-learning and deep learning models, such as Support Vector Machines (SVM), XGBoost, and Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRU) to predict daily and multi-step river flow. The LSTM network outperformed other models, achieving the highest R2 value of 0.96 and the lowest RMSE value of 140.96 m3/sec. The time series LSTM and GRU network models, utilized for short-term forecasts of up to five days, performed significantly. However, the accuracy declined beyond the fourth day, highlighting the need for longer-term historical datasets for reliable long-term flood predictions. The results of the study are directly aligned with Sustainable Development Goals 6, 11, 13, and 15, facilitating disaster and water management, timely evacuations, improved preparedness, and effective early warning.