This study utilized artificial neural network (ANN) optimization techniques including biography-based optimization (BBO), earthworm optimization (EWA), shuffled complex evolution (SCE), and stochastic fractal search (SFS) to predict landslide susceptibility mapping. The ANN model was optimized using hybrid algorithms based on BBO-MLP, EWA-MLP, SCE-MLP, and SFS-MLP. A large dataset consisting of 3211 training and testing datasets from the eastern Azerbaijan province in west Iran was used to prepare the ANN network. The variables of the algorithms were optimized, including network parameters and weights, to create reliable maps of landslide susceptibility. The layers for preparing the landslide susceptibility map included 16 environmental, geographical, hydro-geomorphological, and climatic factors. The accuracy of the probabilistic models was evaluated using the area under the curve criterion within the context of predictive modeling for landslide susceptibility mapping. Numerous algorithms and swarm sizes were employed to assess the results. The area under the curve (AUC) was used to measure the accuracy of these algorithms. For the BBO-MLP and EWA-MLP models, AUC values were calculated for different population sizes in training databases. The optimal hybrid model for the two algorithms was determined to have a swarm size of 500. Similarly, the SCE-MLP and SCE-MLP models were assessed and determined to possess remarkable precision, as evidenced by AUC scores that varied between 0.9965 and 0.9997 for both training and testing. Notably, the SFS-MLP model exhibited the most exceptional accuracy overall, with an AUC value that surpassed all others. The SFS-MLP model had the highest overall accuracy with an AUC value of 0.9879 compared to SCE-ANN (AUC = 0.9887) and BBO & EWA-ANN (AUC = 0.9865, 0.9793). These algorithms proved effective in optimizing artificial neural networks and improving performance in landslide risk zoning.

本研究利用人工神经网络（ANN）优化技术，包括基于传记的优化（BBO）、蚯蚓优化（EWA）、混洗复杂进化（SCE）和随机分形搜索（SFS）来预测滑坡敏感性绘图。使用基于 BBO-MLP、EWA-MLP、SCE-MLP 和 SFS-MLP 的混合算法对 ANN 模型进行优化。使用来自伊朗西部阿塞拜疆东部省份的 3211 个训练和测试数据集组成的大型数据集来准备 ANN 网络。对算法的变量（包括网络参数和权重）进行了优化，以创建可靠的滑坡敏感性地图。制作滑坡敏感性图的图层包括 16 个环境、地理、水文地貌和气候因素。在滑坡敏感性测绘预测模型的背景下，使用曲线下面积标准来评估概率模型的准确性。采用多种算法和群体大小来评估结果。曲线下面积（AUC）用于衡量这些算法的准确性。对于 BBO-MLP 和 EWA-MLP 模型，计算了训练数据库中不同群体规模的 AUC 值。两种算法的最佳混合模型确定为群体大小为 500。同样，SCE-MLP 和 SCE-MLP 模型经过评估并确定具有出色的精度，AUC 分数在 0.9965 和 0.9997 之间变化就证明了这一点。培训和测试。值得注意的是，SFS-MLP 模型总体上表现出最出色的准确性，其 AUC 值超过了所有其他模型。与 SCE-ANN (AUC = 0.9887) 和 BBO & EWA-ANN (AUC = 0.9865, 0.9793) 相比，SFS-MLP 模型具有最高的总体精度，AUC 值为 0.9879。这些算法被证明可以有效优化人工神经网络并提高滑坡风险分区的性能。