Modeling and Uncertainty Analysis of Groundwater Level Using Six Evolutionary Optimization Algorithms Hybridized with ANFIS, SVM, and ANN

• URL: http://arxiv.org/abs/2006.16848v1
• Date: Sun, 28 Jun 2020 11:06:53 GMT
• Title: Modeling and Uncertainty Analysis of Groundwater Level Using Six Evolutionary Optimization Algorithms Hybridized with ANFIS, SVM, and ANN
• Abstract summary: Six meta-heuristic schemes are hybridized with artificial neural network (ANN), adaptive neuro-fuzzy interface system (ANFIS), and support vector machine (SVM) The performance of hybrid models with optimization algorithms was far better than that of classical ANN, ANFIS, and SVM models without hybridization.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the present study, six meta-heuristic schemes are hybridized with artificial neural network (ANN), adaptive neuro-fuzzy interface system (ANFIS), and support vector machine (SVM), to predict monthly groundwater level (GWL), evaluate uncertainty analysis of predictions and spatial variation analysis. The six schemes, including grasshopper optimization algorithm (GOA), cat swarm optimization (CSO), weed algorithm (WA), genetic algorithm (GA), krill algorithm (KA), and particle swarm optimization (PSO), were used to hybridize for improving the performance of ANN, SVM, and ANFIS models. Groundwater level (GWL) data of Ardebil plain (Iran) for a period of 144 months were selected to evaluate the hybrid models. The pre-processing technique of principal component analysis (PCA) was applied to reduce input combinations from monthly time series up to 12-month prediction intervals. The results showed that the ANFIS-GOA was superior to the other hybrid models for predicting GWL in the first piezometer and third piezometer in the testing stage. The performance of hybrid models with optimization algorithms was far better than that of classical ANN, ANFIS, and SVM models without hybridization. The percent of improvements in the ANFIS-GOA versus standalone ANFIS in piezometer 10 were 14.4%, 3%, 17.8%, and 181% for RMSE, MAE, NSE, and PBIAS in the training stage and 40.7%, 55%, 25%, and 132% in testing stage, respectively. The improvements for piezometer 6 in train step were 15%, 4%, 13%, and 208% and in the test step were 33%, 44.6%, 16.3%, and 173%, respectively, that clearly confirm the superiority of developed hybridization schemes in GWL modeling. Uncertainty analysis showed that ANFIS-GOA and SVM had, respectively, the best and worst performances among other models. In general, GOA enhanced the accuracy of the ANFIS, ANN, and SVM models.

Related papers

• Towards Robust Training in NNGPT AutoML Pipeline: A Loss-Optimizer Pairing Selection Study [48.83701310501069]
  This paper investigates whether a single recipe is sufficient for heterogeneous architecture pools, or whether the optimal pairing varies across structurally diverse models.<n>We conduct a systematic empirical study of all $3 times 6 = 18$ combinations of six classifiers paired with three loss functions: Cross-Entropy (CEL), Negative Log-Likelihood (NLL), and the recently introduced genetically evolved NGL loss across the base models presented in LEMUR heterogeneous architecture pool on six image classification datasets.<n>Our results confirm that no single pairing is universally optimal. Cross-Entropy with Adam or AdamW is the most robust choice across
  arXiv  Detail & Related papers  (2026-06-18T20:51:42Z)
• Physics-Constrained Neural Networks for Improved Short-Term Weather Forecasting: A Case Study over the South Pacific [0.7705076872059964]
  This study introduces enhancements to physics-constrained neural networks (PCNNs) that improve the accuracy and stability of hybrid short-term weather forecasting models.<n>An upgraded numerical solver, combined with a beta-plane approximation, and subgrid-scale viscosity, permits a fourfold increase in the integration time step to 1200 s.<n>A unified autoregressive hybrid block replaces the original chain of 24 specialised modules, eliminating overfitting to specific lead times.
  arXiv  Detail & Related papers  (2026-06-16T08:18:48Z)
• CSI-4CAST: A Hybrid Deep Learning Model for CSI Prediction with Comprehensive Robustness and Generalization Testing [44.045995554758385]
  This paper introduces CSI-4CAST, a hybrid deep learning architecture that integrates 4 key components, i.e., Convolutional neural network residuals, Adaptive correction layers, ShuffleNet blocks, and Transformers.<n>The dataset spans multiple channel models, a wide range of delay spreads and user velocities, and diverse noise types and intensity degrees.<n> Experimental results show that CSI-4CAST achieves superior prediction accuracy with substantially lower computational cost.
  arXiv  Detail & Related papers  (2025-10-14T21:19:52Z)
• Hybrid Quantum-Classical Model for Image Classification [0.0]
  This study presents a systematic comparison between hybrid quantum-classical neural networks and purely classical models across three benchmark datasets.<n>The hybrid models integrate parameterized quantum circuits with classical deep learning architectures, while the classical counterparts use conventional convolutional neural networks (CNNs)<n>Experiments were conducted over 50 training epochs for each dataset, with evaluations on validation accuracy, test accuracy, training time, computational resource usage, and adversarial robustness.
  arXiv  Detail & Related papers  (2025-09-14T09:55:00Z)
• Multi-Strategy Improved Snake Optimizer Accelerated CNN-LSTM-Attention-Adaboost for Trajectory Prediction [9.489729227685057]
  This paper proposes a hybrid CNN-LSTM-attention-adaboost neural network model incorporating a multi-strategy improved snake-herd optimization (SO) algorithm.<n>The results show that SO-adaboost outperforms traditional CLAs such as particle swarm, whale, and gray wolf in handling large-scale high-dimensional trajectory data.<n>In addition, introducing the full-strategy collaborative improvement SO algorithm improves the model's prediction accuracy by 39.89%.
  arXiv  Detail & Related papers  (2025-07-21T17:44:06Z)
• Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout [62.73150122809138]
  Federated Learning (FL) is a promising distributed machine learning approach that enables collaborative training of a global model using multiple edge devices.<n>We propose the FedDHAD FL framework, which comes with two novel methods: Dynamic Heterogeneous model aggregation (FedDH) and Adaptive Dropout (FedAD)<n>The combination of these two methods makes FedDHAD significantly outperform state-of-the-art solutions in terms of accuracy (up to 6.7% higher), efficiency (up to 2.02 times faster), and cost (up to 15.0% smaller)
  arXiv  Detail & Related papers  (2025-07-14T16:19:00Z)
• Benchmarking Foundation Models and Parameter-Efficient Fine-Tuning for Prognosis Prediction in Medical Imaging [40.35825564674249]
  This study introduces the first structured benchmark to assess the robustness and efficiency of transfer learning strategies for Foundation Models.<n>Four publicly available COVID-19 chest X-ray datasets were used, covering mortality, severity, and admission.<n>CNNs pretrained on ImageNet and FMs pretrained on general or biomedical datasets were adapted using full finetuning, linear probing, and parameter-efficient methods.
  arXiv  Detail & Related papers  (2025-06-23T09:16:04Z)
• S*: Test Time Scaling for Code Generation [55.11863577956177]
  We propose S*, the first hybrid test-time scaling framework for code generation. S* substantially improves the coverage and selection accuracy of generated code.
  arXiv  Detail & Related papers  (2025-02-20T09:18:53Z)
• Foxtsage vs. Adam: Revolution or Evolution in Optimization? [4.939986309170004]
  This study introduces Foxtsage, a novel hybrid optimisation approach that integrates the Hybrid FOX-TSA with Gradient Descent for training Multi-Layer Perceptron models. Experimental results demonstrate that Foxtsage achieves a 42.03% reduction in loss mean (Foxtsage: 9.508, Adam: 16.402) and a 42.19% improvement in loss standard deviation (Foxtsage: 20.86, Adam: 36.085)
  arXiv  Detail & Related papers  (2024-12-20T03:20:58Z)
• Protein sequence classification using natural language processing techniques [3.0846824529023396]
  This study employs natural language processing (NLP) techniques on a dataset comprising 75 target protein classes. We explored various machine learning and deep learning models, including K-Nearest Neighbors (KNN), Multinomial Na"ive Bayes, Logistic Regression, Multi-Layer Perceptron (MLP), Decision Tree, Random Forest, XGBoost, Voting and Stacking classifiers, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and transformer models.
  arXiv  Detail & Related papers  (2024-09-06T13:16:16Z)
• An Effective Networks Intrusion Detection Approach Based on Hybrid Harris Hawks and Multi-Layer Perceptron [47.81867479735455]
  This paper proposes an Intrusion Detection System (IDS) employing the Harris Hawks Optimization (HHO) to optimize Multilayer Perceptron learning. HHO-MLP aims to select optimal parameters in its learning process to minimize intrusion detection errors in networks. HHO-MLP showed superior performance by attaining top scores with accuracy rate of 93.17%, sensitivity level of 95.41%, and specificity percentage of 95.41%.
  arXiv  Detail & Related papers  (2024-02-21T06:25:50Z)
• Improved Forecasting Using a PSO-RDV Framework to Enhance Artificial Neural Network [0.0]
  This study used an improved method of forecasting utilizing the Random Descending Velocity Inertia Weight technique. The IW technique, inspired by the motions of a golf ball, modified the particles' velocities as they approached the solution point to a parabolically descending structure. Applying the modified ANN-PSO using RDV IW technique greatly improved the new HIV/AIDS forecasting model compared with the two models.
  arXiv  Detail & Related papers  (2024-01-10T01:15:33Z)
• Improving Urban Flood Prediction using LSTM-DeepLabv3+ and Bayesian Optimization with Spatiotemporal feature fusion [7.790241122137617]
  This study presented a CNN-RNN hybrid feature fusion modelling approach for urban flood prediction. It integrated the strengths of CNNs in processing spatial features and RNNs in analyzing different dimensions of time sequences.
  arXiv  Detail & Related papers  (2023-04-19T22:00:04Z)
• DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimization [51.517956081644186]
  We introduce a new graph-based diffusion framework, namely DIFUSCO. Our framework casts NPC problems as discrete 0, 1-vector optimization problems. For the MIS problem, DIFUSCO outperforms the previous state-of-the-art neural solver on the challenging SATLIB benchmark.
  arXiv  Detail & Related papers  (2023-02-16T11:13:36Z)
• Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuning [126.84770886628833]
  Existing finetuning methods either tune all parameters of the pretrained model (full finetuning) or only tune the last linear layer (linear probing) We propose a new parameter-efficient finetuning method termed as SSF, representing that researchers only need to Scale and Shift the deep Features extracted by a pre-trained model to catch up with the performance full finetuning.
  arXiv  Detail & Related papers  (2022-10-17T08:14:49Z)
• Multiple Time Series Fusion Based on LSTM An Application to CAP A Phase Classification Using EEG [56.155331323304]
  Deep learning based electroencephalogram channels' feature level fusion is carried out in this work. Channel selection, fusion, and classification procedures were optimized by two optimization algorithms.
  arXiv  Detail & Related papers  (2021-12-18T14:17:49Z)
• ANNETTE: Accurate Neural Network Execution Time Estimation with Stacked Models [56.21470608621633]
  We propose a time estimation framework to decouple the architectural search from the target hardware. The proposed methodology extracts a set of models from micro- kernel and multi-layer benchmarks and generates a stacked model for mapping and network execution time estimation. We compare estimation accuracy and fidelity of the generated mixed models, statistical models with the roofline model, and a refined roofline model for evaluation.
  arXiv  Detail & Related papers  (2021-05-07T11:39:05Z)
• Genetically Optimized Prediction of Remaining Useful Life [4.115847582689283]
  We implement LSTM and GRU models and compare the obtained results with a proposed genetically trained neural network. We hope to improve the consistency of the predictions by adding another layer of optimization using Genetic Algorithms. These models and the proposed architecture are tested on the NASA Turbofan Jet Engine dataset.
  arXiv  Detail & Related papers  (2021-02-17T16:09:23Z)
• Synthesizing multi-layer perceptron network with ant lion, biogeography-based dragonfly algorithm evolutionary strategy invasive weed and league champion optimization hybrid algorithms in predicting heating load in residential buildings [1.370633147306388]
  The significance of heating load (HL) accurate approximation is the primary motivation of this research. The proposed models are through multi-layer perceptron network (MLP) with ant lion optimization (ALO) Biogeography-based optimization (BBO) featured as the most capable optimization technique, followed by ALO (OS = 27) and ES (OS = 20)
  arXiv  Detail & Related papers  (2021-02-13T14:06:55Z)
• Towards a Competitive End-to-End Speech Recognition for CHiME-6 Dinner Party Transcription [73.66530509749305]
  In this paper, we argue that, even in difficult cases, some end-to-end approaches show performance close to the hybrid baseline. We experimentally compare and analyze CTC-Attention versus RNN-Transducer approaches along with RNN versus Transformer architectures. Our best end-to-end model based on RNN-Transducer, together with improved beam search, reaches quality by only 3.8% WER abs. worse than the LF-MMI TDNN-F CHiME-6 Challenge baseline.
  arXiv  Detail & Related papers  (2020-04-22T19:08:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.