Ehteram Mohammad, Panahi Fatemeh, Ahmed Ali Najah, Huang Yuk Feng, Kumar Pavitra, Elshafie Ahmed
Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran.
Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan, Iran.
Environ Sci Pollut Res Int. 2022 Feb;29(7):10675-10701. doi: 10.1007/s11356-021-16301-3. Epub 2021 Sep 15.
Evaporation is a crucial component to be established in agriculture management and water engineering. Evaporation prediction is thus an essential issue for modeling researchers. In this study, the multilayer perceptron (MLP) was used for predicting daily evaporation. MLP model is as one of the famous ANN models with multilayers for predicting different target variables. A new strategy was used to enhance the accuracy of the MLP model. Three multi-objective algorithms, namely, the multi-objective salp swarm algorithm (MOSSA), the multi-objective crow algorithm (MOCA), and the multi-objective particle swarm optimization (MOPSO), were respectively and separately coupled to the MLP model for determining the model parameters, the best input combination, and the best activation function. In this study, three stations in Malaysia, namely, the Muadzam Shah (MS), the Kuala Terengganu (KT), and the Kuantan (KU), were selected for the prediction of the respective daily evaporation. The spacing (SP) and maximum spread (MS) indices were used to evaluate the quality of generated Pareto front (PF) by the algorithms. The lower SP and higher MS showed better PF for the models. It was observed that the MOSSA had higher MS and lower SP than the other algorithms, at all stations. The root means square error (RMSE), mean absolute error (MAE), percent bias (PBIAS), and Nash Sutcliffe efficiency (NSE) quantifiers were used to compare the ability of the models with each other. The MLP-MOSSA had reduced RMSE compared to the MLP-MOCA, MLP-MOPSO, and MLP models by 18%, 25%, and 35%, respectively, at the MS station. The MAE of the MLP-MOSSA was 2.7%, 4.1%, and 26%, respectively lower than those of the MLP-MOCA, MLP-MOPSO, and MLP models at the KU station. The MLP-MOSSA showed lower MAE than the MLP-MOCA, MLP-MOPSO, and MLP models by 16%, 18%, and 19%, respectively, at the KT station. An uncertainty analysis was performed based on the input and parameter uncertainty. The results indicated that the MLP-MOSSA had the lowest uncertainty among the models. Also, the input uncertainty was lower than the parameter uncertainty. The general results indicated that the MLP-MOSSA had the high efficiency for predicting evaporation.
蒸发是农业管理和水利工程中需要确立的一个关键要素。因此，蒸发预测对于建模研究人员来说是一个至关重要的问题。在本研究中，多层感知器（MLP）被用于预测日蒸发量。MLP模型是著名的用于预测不同目标变量的多层人工神经网络（ANN）模型之一。采用了一种新策略来提高MLP模型的准确性。三种多目标算法，即多目标樽海鞘群算法（MOSSA）、多目标乌鸦算法（MOCA）和多目标粒子群优化算法（MOPSO），分别与MLP模型耦合，用于确定模型参数、最佳输入组合和最佳激活函数。在本研究中，选取了马来西亚的三个站点，即瓜拉丁加奴（MS）、瓜拉丁加奴（KT）和关丹（KU），来预测各自的日蒸发量。间距（SP）和最大散布（MS）指标用于评估算法生成的帕累托前沿（PF）的质量。对于模型而言，较低的SP和较高MS表明PF更好。结果发现，在所有站点，MOSSA的MS值更高，SP值更低。均方根误差（RMSE）、平均绝对误差（MAE）、偏差百分比（PBIAS）和纳什-萨特克利夫效率（NSE）量化指标用于相互比较各模型的能力。在MS站点，与MLP-MOCA、MLP-MOPSO和MLP模型相比，MLP-MOSSA的RMSE分别降低了18%、25%和35%。在KU站点，MLP-MOSSA的MAE分别比MLP-MOCA、MLP-MOPSO和MLP模型低2.7%、4.1%和26%。在KT站点，MLP-MOSSA的MAE分别比MLP-MOCA、MLP-MOPSO和MLP模型低16%、18%和19%。基于输入和参数不确定性进行了不确定性分析。结果表明，MLP-MOSSA在各模型中不确定性最低。此外，输入不确定性低于参数不确定性。总体结果表明，MLP-MOSSA在预测蒸发量方面具有较高效率。
