COMPARATIVE ANALYSIS OF LONG SHORT-TERM MEMORY (LSTM) AND MULTI-LAYER PERCEPTRON (MLP) MODELS FOR RIVER RUNOFF PREDICTION IN THE HINDU KUSH HIMALAYAN REGION
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I.O.E. Pulchowk Campus
Abstract
Hydrological forecasting in the Hindu Kush Himalayas (HKH) presents special
challenges because of the complex interplay between climatic and environmental
factors. The quantitative predictive capabilities of two well-established models, Long
Short-Term Memory (LSTM) and Multi-Layer Perceptron (MLP), chosen for their
proven performance in previous studies, are meticulously compared in this thesis. The
analysis uses comprehensive data spanning 2001 to 2013, including discharge records
from the Department of Hydrology and Meteorology (DHM), precipitation data from
APHRODITE, temperature data from APHRODITE, and snow cover area information
from Google Earth Engine with MOD09A1 V6.1. The study employs rigorous
evaluation metrics, revealing nuanced insights into the hydrological processes.
Contrary to expectations, the MLP model exhibited slight superiority, showcasing a
nuanced understanding of the region's complexities. The quantitative assessment,
including RMSE (LSTM: 0.2396, MLP: 0.1733), MAE (LSTM: 0.1698, MLP: 0.0841),
R2 Score (LSTM: 0.9976, MLP: 0.9987), and NSE (LSTM: 0.9976, MLP: 0.9987),
emphasizes the indispensable role of robust predictive models, showcasing the
necessity of reliable models for enhancing accurate river runoff predictions crucial for
effective water resource management and flood preparedness in challenging terrains
like the HKH.
Description
Hydrological forecasting in the Hindu Kush Himalayas (HKH) presents special
challenges because of the complex interplay between climatic and environmental
factors. The quantitative predictive capabilities of two well-established models, Long
Short-Term Memory (LSTM) and Multi-Layer Perceptron (MLP), chosen for their
proven performance in previous studies, are meticulously compared in this thesis