Projected Water Levels and Identified Future Floods: A Comparative Analysis for Mahaweli River, Sri Lanka
Projected Water Levels and Identified Future Floods: A Comparative Analysis for Mahaweli River, Sri Lanka
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DOI：
10.1109/access.2023.3238717
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发表时间：
2023
期刊：
影响因子：
3.9
通讯作者：
Namal Rathnayake;Upaka S. Rathnayake;Imiya M. Chathuranika;Tuan Linh Dang;Y. Hoshino
中科院分区：
文献类型：
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作者：
Namal Rathnayake;Upaka S. Rathnayake;Imiya M. Chathuranika;Tuan Linh Dang;Y. Hoshino
降雨-径流（R-R）关系对水文循环至关重要。复杂的水文模型可以准确地研究R-R关系，但是，它们需要大量的数据。因此，在水文、气象和地质数据有限的环境下，机器学习和软计算技术受到了人们的关注。这些模型的准确性取决于各种参数，包括输入和输出的质量以及所使用的算法。然而，确定一个完美的算法仍然具有挑战性。本研究发展一种以模糊逻辑为基础的演算法，称为Cascaded-ANFIS，以准确预测降雨量为基础的径流量。该模型与三种回归算法进行了比较：长短期记忆，Grated递归单元和递归神经网络。这些算法已被选中，因为他们在类似的研究中表现出色。这些模型在斯里兰卡最长的Mahaweli河上进行了测试。结果表明，级联ANFIS模型优于其他算法。对于级联ANFIS、LSTM、GRU、RNN、线性、岭和Lasso回归模型，每种算法预测的相关系数分别为0.9330、0.9120、0.9133、0.8915、0.6811、0.6811和0.6734。因此，这项研究得出结论，该算法比第二好的LSTM算法准确率高21%。此外，共享的社会经济路径（SSP 2 -4.5和SSP 5 -8.5情景）被用来生成未来情景，预测近期和中期的水位，并确定潜在的洪水事件。未来的预测结果表明，在两个SSP 2 -4.5和SSP 5 -8.5的情况下，洪水事件和量级减少。此外，SSP 5 -8.5情景显示每年5月至8月为干旱天气。研究成果可有效地用于水资源的管理和控制，减轻洪水灾害。
The Rainfall-Runoff (R-R) relationship is essential to the hydrological cycle. Sophisticated hydrological models can accurately investigate R-R relationships; however, they require many data. Therefore, machine learning and soft computing techniques have taken the attention in the environment of limited hydrological, meteorological, and geological data. The accuracy of such models depends on the various parameters, including the quality of inputs and outputs and the used algorithms. However, identifying a perfect algorithm is still challenging. This study develops a fuzzy logic-based algorithm called Cascaded-ANFIS to accurately predict runoff based on rainfall. The model was compared against three regression algorithms: Long Short-Term Memory, Grated Recurrent Unit, and Recurrent Neural Networks. These algorithms have been selected due to their outstanding performances in similar studies. The models were tested on the Mahaweli River, the longest in Sri Lanka. The results showcase that the Cascaded-ANFIS-based model outperforms the other algorithms. The correlation coefficient of each algorithm’s predictions was 0.9330, 0.9120, 0.9133, 0.8915, 0.6811, 0.6811, and 0.6734 for the Cascaded-ANFIS, LSTM, GRU, RNN, Linear, Ridge, and Lasso regression models respectively. Hence, this study concludes that the proposed algorithm is 21% more accurate than the second-best LSTM algorithm. In addition, Shared Socio-economic Pathways (SSP2-4.5 and SSP5-8.5 scenarios) were used to generate future rainfalls, forecast the near-future and mid-future water levels, and identify potential flood events. The future forecasting results indicate a decrease in flood events and magnitudes in both SSP2-4.5 and SSP5-8.5 scenarios. Furthermore, the SSP5-8.5 scenario shows drought weather from May to August yearly. The results of this study can effectively be used to manage and control water resources and mitigate flood damages.