This study aimed to develop novel flood forecasting models using artificial neural network (ANN) algorithms. The models were assessed and validated for the case study of an alluvial river in the Kashmir region of the Indian Himalayas‐River Jhelum. In September 2014, a major flood occurred in the Kashmir Valley with peak discharge exceeding 115,000 m³/s; brought about by the multifarious interaction among atmospheric disturbances. The present study targeted the development of ANN models for flood prognosis. Thereby five neural network models were developed: Bayesian regularisation neural network, the Levenberg–Marquardt neural network, conjugate gradient neural network, scaled conjugate gradient neural network, and resilient back‐propagation neural network. Levenberg–Marquardt neural network model, with the mean squared error of 0.002128 (lowest of all models) and coefficient of determination of 95.839% (highest of all models), proved to be the best model based on the statistical validation parameters. All the simulations were evaluated and curves graphed for actual versus predicted discharges for 20% of the data. These models may be used to predict floods, and take advance precautionary measures by Irrigation and Flood Control Department of the State, rather than waiting for flood hydrograph to shoot (norm as of now).

中文翻译：

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神经网络训练算法在冲积喜马拉雅河洪水预报模型中的比较分析

这项研究旨在使用人工神经网络（ANN）算法开发新颖的洪水预报模型。对模型进行了评估和验证，以用于印度喜马拉雅山-河西河的克什米尔地区的冲积河案例研究。2014年9月，克什米尔山谷发生了大水灾，洪峰流量超过115,000 m ³/ s; 由大气扰动之间的多种相互作用引起。本研究针对洪水预报的神经网络模型的发展。因此，开发了五个神经网络模型：贝叶斯正则化神经网络，Levenberg-Marquardt神经网络，共轭梯度神经网络，缩放共轭梯度神经网络和弹性反向传播神经网络。Levenberg-Marquardt神经网络模型的均方误差为0.002128（在所有模型中最低），测定系数为95.839％（在所有模型中最高），被证明是基于统计验证参数的最佳模型。对所有模拟进行了评估，并绘制了20％数据的实际流量与预计流量的曲线图。这些模型可以用来预测洪水，