Department of Civil Engineering, National Institute of Technology Srinagar, Hazratbal, Srinagar, Jammu & Kashmir, 190006, India.
Environ Sci Pollut Res Int. 2021 May;28(20):25265-25282. doi: 10.1007/s11356-021-12410-1. Epub 2021 Jan 16.
Flood prediction has gained prominence world over due to the calamitous socio-economic impacts this hazard has and the anticipated increase of its incidence in the near future. Artificial intelligence (AI) models have contributed significantly over the last few decades by providing improved accuracy and economical solutions to simulate physical flood processes. This study explores the potential of the AI computing paradigm to model the stream flow. Artificial neural network (ANN), fuzzy logic, and adaptive neuro-fuzzy inference system (ANFIS) algorithms are used to develop nine different flood prediction models using all the available training algorithms. The performance of the developed models is evaluated using multiple statistical performance evaluators. The predictability and robustness of the models are tested through the simulation of a major flood event in the study area. A total of 12 inputs were used in the development of the models. Five training algorithms were used to develop the ANN models (Bayesian regularization, Levenberg Marquardt, conjugate gradient, scaled conjugate gradient, and resilient backpropagation), two fuzzy inference systems to develop fuzzy models (Mamdani and Sugeno), and two training algorithms to develop the ANFIS models (hybrid and backpropagation). The ANFIS model developed using hybrid training algorithm gave the best performance metrics with Nash-Sutcliffe Model Efficiency (NSE) of 0.968, coefficient of correlation (R) of 97.066%, mean square error (MSE) of 0.00034, root mean square error (RMSE) of 0.018, mean absolute error (MAE) of 0.0073, and combined accuracy (CA) of 0.018, implying the potential of using the developed models for flood forecasting. The significance of this research lies in the fact that a combination of multiple inputs and AI algorithms has been used to develop the flood models. In summary, this research revealed the potential of AI algorithm-based models in predicting floods and also developed some useful techniques that can be used by the Flood Control Departments of various states/regions/countries for flood prognosis.
由于洪水灾害对社会经济造成的灾难性影响，以及预计在不久的将来其发生率会增加，洪水预测在全球范围内受到了重视。在过去的几十年中，人工智能 (AI) 模型通过提供改进的准确性和经济的解决方案来模拟物理洪水过程，为洪水预测做出了重大贡献。本研究探讨了 AI 计算范式在模拟河川流量方面的潜力。使用所有可用的训练算法，人工神经网络 (ANN)、模糊逻辑和自适应神经模糊推理系统 (ANFIS) 算法被用来开发九个不同的洪水预测模型。使用多个统计性能评估器评估所开发模型的性能。通过模拟研究区域内的一次重大洪水事件来测试模型的可预测性和稳健性。总共使用了 12 个输入来开发模型。使用五种训练算法来开发 ANN 模型（贝叶斯正则化、Levenberg Marquardt、共轭梯度、比例共轭梯度和弹性反向传播），两种模糊推理系统来开发模糊模型（Mamdani 和 Sugeno），以及两种训练算法来开发 ANFIS 模型（混合和反向传播）。使用混合训练算法开发的 ANFIS 模型具有最佳的性能指标，纳什-苏特克利夫模型效率 (NSE) 为 0.968，相关系数 (R) 为 97.066%，均方误差 (MSE) 为 0.00034，均方根误差 (RMSE) 为 0.018，平均绝对误差 (MAE) 为 0.0073，综合准确率 (CA) 为 0.018，表明了使用所开发模型进行洪水预测的潜力。这项研究的意义在于，已经使用多种输入和 AI 算法的组合来开发洪水模型。总之，这项研究揭示了基于 AI 算法的模型在洪水预测方面的潜力，并开发了一些有用的技术，可用于各国/地区/国家的防洪部门进行洪水预测。
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