Deep Machine Learning-Based Water Level Prediction Model for Colombo Flood Detention Area
Deep Machine Learning-Based Water Level Prediction Model for Colombo Flood Detention Area
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DOI：
10.3390/app13042194
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发表时间：
2023-02-01
影响因子：
2.7
通讯作者：
Rathnayake, Upaka
中科院分区：
文献类型：
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作者：
Herath, Madhawa;Jayathilaka, Tharaka;Rathnayake, Upaka
机器学习已经被证明是一种强大的最先进的技术，适用于许多非线性应用，包括环境变化和气候预测。湿地是水位预测中最具挑战性和最复杂的生态系统之一。湿地水位预测是至关重要的，因为湿地有自己的允许水位。超过这些水位会导致洪水和其他严重的环境破坏。另一方面，湿地的生物多样性受到水位突然波动的威胁。因此，对水位的早期预测有助于减轻大多数此类环境破坏。然而，由于各种限制，全球湿地水位的监测和预测一直受到限制。本研究首次应用深度机器学习技术（深度神经网络）来预测斯里兰卡首都城市湿地的水位。此外，在水位预测中首次研究了两种类型的关系：传统的水位与环境因子（包括温度、湿度、风速和蒸发）的关系，以及日水位之间的时间关系。开发了两种低负荷人工神经网络，并对前馈神经网络（FFNN）和长短期记忆神经网络（LSTM）两种神经网络之间的关系进行了分析，在无偏的基础上进行了比较。LSTM优于FFNN，并证实了时间关系在预测湿地水位方面比传统关系更稳健。此外，该研究还发现了湿地数据中嵌入的预测精度、数据量、人工神经网络类型和信息提取程度之间的有趣关系。与现有研究相比，LSTM神经网络（NN）的R-2为0.8786，均方误差（MSE）为0.0004，平均绝对误差（MAE）为0.0155。
Machine learning has already been proven as a powerful state-of-the-art technique for many non-linear applications, including environmental changes and climate predictions. Wetlands are among some of the most challenging and complex ecosystems for water level predictions. Wetland water level prediction is vital, as wetlands have their own permissible water levels. Exceeding these water levels can cause flooding and other severe environmental damage. On the other hand, the biodiversity of the wetlands is threatened by the sudden fluctuation of water levels. Hence, early prediction of water levels benefits in mitigating most of such environmental damage. However, monitoring and predicting the water levels in wetlands worldwide have been limited owing to various constraints. This study presents the first-ever application of deep machine-learning techniques (deep neural networks) to predict the water level in an urban wetland in Sri Lanka located in its capital. Moreover, for the first time in water level prediction, it investigates two types of relationships: the traditional relationship between water levels and environmental factors, including temperature, humidity, wind speed, and evaporation, and the temporal relationship between daily water levels. Two types of low load artificial neural networks (ANNs) were developed and employed to analyze two relationships which are feed forward neural networks (FFNN) and long short-term memory (LSTM) neural networks, to conduct the comparison on an unbiased common ground. The LSTM has outperformed FFNN and confirmed that the temporal relationship is much more robust in predicting wetland water levels than the traditional relationship. Further, the study identified interesting relationships between prediction accuracy, data volume, ANN type, and degree of information extraction embedded in wetland data. The LSTM neural networks (NN) has achieved substantial performance, including R-2 of 0.8786, mean squared error (MSE) of 0.0004, and mean absolute error (MAE) of 0.0155 compared to existing studies.