Days-ahead water level forecasting using artificial neural networks for watersheds

Lemuel Clark Velasco; John Frail Bongat; Ched Castillon; Jezreil Laurente; Emily Tabanao; Lemuel Clark Velasco; John Frail Bongat; Ched Castillon; Jezreil Laurente; Emily Tabanao

doi:10.3934/mbe.2023035

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 1: 758-774. doi: 10.3934/mbe.2023035

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Days-ahead water level forecasting using artificial neural networks for watersheds

1.
Premier Research Institute of Science and Mathematics, Mindanao State University-Iligan Institute of Technology, Iligan City, 9200, The Philippines
2.
College of Computer Studies, Mindanao State University-Iligan Institute of Technology, Iligan City, 9200, The Philippines

Received: 09 September 2022 Revised: 09 October 2022 Accepted: 10 October 2022 Published: 14 October 2022

Watersheds of tropical countries having only dry and wet seasons exhibit contrasting water level behaviour compared to countries having four seasons. With the changing climate, the ability to forecast the water level in watersheds enables decision-makers to come up with sound resource management interventions. This study presents a strategy for days-ahead water level forecasting models using an Artificial Neural Network (ANN) for watersheds by conducting data preparation of water level data captured from a Water Level Monitoring Station (WLMS) and two Automatic Rain Gauge (ARG) sensors divided into the two major seasons in the Philippines being implemented into multiple ANN models with different combinations of training algorithms, activation functions, and a number of hidden neurons. The implemented ANN model for the rainy season which is RPROP-Leaky ReLU produced a MAPE and RMSE of 6.731 and 0.00918, respectively, while the implemented ANN model for the dry season which is SCG-Leaky ReLU produced a MAPE and RMSE of 7.871 and 0.01045, respectively. By conducting appropriate water level data correction, data transformation, and ANN model implementation, the results of error computation and assessment shows the promising performance of ANN in days-ahead water level forecasting of watersheds among tropical countries.
- artificial neural network,
- days-ahead water level forecasting,
- watersheds,
- water level forecasting,
- multilayer perceptron neural network
Citation: Lemuel Clark Velasco, John Frail Bongat, Ched Castillon, Jezreil Laurente, Emily Tabanao. Days-ahead water level forecasting using artificial neural networks for watersheds[J]. Mathematical Biosciences and Engineering, 2023, 20(1): 758-774. doi: 10.3934/mbe.2023035

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Abstract

Watersheds of tropical countries having only dry and wet seasons exhibit contrasting water level behaviour compared to countries having four seasons. With the changing climate, the ability to forecast the water level in watersheds enables decision-makers to come up with sound resource management interventions. This study presents a strategy for days-ahead water level forecasting models using an Artificial Neural Network (ANN) for watersheds by conducting data preparation of water level data captured from a Water Level Monitoring Station (WLMS) and two Automatic Rain Gauge (ARG) sensors divided into the two major seasons in the Philippines being implemented into multiple ANN models with different combinations of training algorithms, activation functions, and a number of hidden neurons. The implemented ANN model for the rainy season which is RPROP-Leaky ReLU produced a MAPE and RMSE of 6.731 and 0.00918, respectively, while the implemented ANN model for the dry season which is SCG-Leaky ReLU produced a MAPE and RMSE of 7.871 and 0.01045, respectively. By conducting appropriate water level data correction, data transformation, and ANN model implementation, the results of error computation and assessment shows the promising performance of ANN in days-ahead water level forecasting of watersheds among tropical countries.

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