Enhancement groundwater level prediction using hybrid ANN-HHO model: case study (Shabestar Plain in Iran)

Mirzania, Ehsan; Ghorbani, Mohammad Ali; Asadi, Ebrahim

doi:10.1007/s12517-023-11584-x

Cited by 7 publications

(1 citation statement)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ANN's parameters for medical diagnosis can optimize the HHO algorithm, greatly improving accuracy and prediction efficiency. On the other hand, an ANN itself learns a great deal about data just by going through this optimization process, making it more capable as well as faster and efficient at diagnosing ailments [14].…”

Section: Introductionmentioning

confidence: 99%

Use Harris Hawks Optimization (HHO) Algorithm based on Artificial Neural Network for liver disease diagnosis

Al-safi,

Alsalihi,

Munilla

2024

icisna

View full text Add to dashboard Cite

This research paper presents a new method for diagnosing liver disease using the Harris Hawks Optimization (HHO) algorithm in combination with an Artificial Neural Network (ANN). The HHO algorithm enhances the ANN's performance in liver disease classification by refining its parameters. Clinical, laboratory, and demographic data are collected from hepatitis patients and individuals without hepatic illness. The dataset is processed to handle missing values, outliers, and normalization. The HHO algorithm optimizes the weights and biases of the ANN, facilitating the identification of relevant features for accurate diagnosis. The trained ANN model is evaluated using various performance metrics, demonstrating its effectiveness in diagnosing liver disease. The HHO algorithm efficiently searches the entire search space, enhancing the ANN's ability to learn complex patterns and make accurate predictions. Evaluation metrics indicate that the optimized ANN model outperforms traditional machine learning methods, showcasing its potential as a reliable diagnostic tool. Interpretability metrics, such as feature importance and saliency maps, provide insights into the key elements of diagnosis. The proposed approach shows high diagnostic accuracy and interpretability, implying the potential for a stable decision-aid system in clinical practice. Early detection and timely intervention enabled by this method can lead to increased patient safety rates and optimized resource allocation.

show abstract

Section: Introductionmentioning

confidence: 99%

Use Harris Hawks Optimization (HHO) Algorithm based on Artificial Neural Network for liver disease diagnosis

Al-safi,

Alsalihi,

Munilla

2024

icisna

View full text Add to dashboard Cite

show abstract

Shannon entropy of performance metrics to choose the best novel hybrid algorithm to predict groundwater level (case study: Tabriz plain, Iran)

Saroughi,

Mirzania,

Achite

et al. 2024

Environ Monit Assess

Self Cite

View full text Add to dashboard Cite

Predicting groundwater level (GWL) fluctuations, which act as a reserve water reservoir, Particularly in arid and semi-arid climates, is vital in water resources management and planning. Within the scope of current research, a novel hybrid algorithm is proposed for estimating GWL values in the Tabriz plain of Iran by combining the artificial neural network (ANN) algorithm with newly developed nature-inspired Coot and Honey Badger metaheuristic optimization algorithms. Various combinations of meteorological data such as temperature, evaporation and precipitation, previous GWL values, and the month and year values of the data were used to evaluate the algorithm's success. Furthermore, shannon entropy of performance of models was assessed according to 44 different statistical indicators which is classified into two class: accuracy and error class. Hence, based on high value of Shannon entropy, the best statistical indicator was selected and the results of best model and selecting the best scenario were analyzed. Results indicated that value of Shannon entropy is higher for accuracy class than error class. Also, for accuracy and error class respectively, Akaikes Information Criterion (AIC) and Residual Sum of Squares (RSS) indexes with the highest entropy value which is equal to 12.72 and 7.3 are the best indicators of both classes and Legate-McCabe Efficiency (LME) and Normalized Root Mean Square Error-mean (NRMSE-Mean) indexes with the lowest entropy value which is equal to 3.7 and -8.3 are the worst indicators of both classes. According to the results of evaluation best indicator in the testing phase, AIC indicator value for HBA-ANN, COOT-ANN, and the stand-alone ANN models is equal to -344, -332.8, -175.8, respectively. Furthermore, it was revealed that the proposed metaheuristic algorithms significantly ameliorate the performance of the stand-alone ANN model and offered satisfactory GWL prediction results.Finally, it was concluded that the Honey Badger optimization algorithm showed superior results than the Coot optimization algorithm in GWL prediction.

show abstract

A new strategy for groundwater level prediction using a hybrid deep learning model under Ecological Water Replenishment

Jia,

Zhang,

Shi

et al. 2024

Environ Sci Pollut Res

View full text Add to dashboard Cite

Enhancement groundwater level prediction using hybrid ANN-HHO model: case study (Shabestar Plain in Iran)

Cited by 7 publications

References 65 publications

Use Harris Hawks Optimization (HHO) Algorithm based on Artificial Neural Network for liver disease diagnosis

Use Harris Hawks Optimization (HHO) Algorithm based on Artificial Neural Network for liver disease diagnosis

Shannon entropy of performance metrics to choose the best novel hybrid algorithm to predict groundwater level (case study: Tabriz plain, Iran)

A new strategy for groundwater level prediction using a hybrid deep learning model under Ecological Water Replenishment

Contact Info

Product

Resources

About