Improvement of landslide spatial modeling using machine learning methods and two Harris hawks and bat algorithms

The Harris hawk optimizer is a recent population-based metaheuristics algorithm that simulates the hunting behavior of hawks. This swarm-based optimizer performs the optimization procedure using a novel way of exploration and exploitation and the multiphases of search. In this review research, we focused on the applications and developments of the recent well-established robust optimizer Harris hawk optimizer (HHO) as one of the most popular swarm-based techniques of 2020. Moreover, several experiments were carried out to prove the powerfulness and effectivness of HHO compared with nine other state-of-art algorithms using Congress on Evolutionary Computation (CEC2005) and CEC2017. The literature review paper includes deep insight about possible future directions and possible ideas worth investigations regarding the new variants of the HHO algorithm and its widespread applications.

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“…Payani et al [288] used the HHO and Bat Algorithm with machine learning tools (ANFIS/SVR) in order to improve modeling of a landslide spatial.…”

Section: Geological Engineeringmentioning

confidence: 99%

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

et al. 2022

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“…The aftermath of most natural disasters, such as landslides often involves devastating damage. Landslides are considered a serious global issue as they always resulted in at least 17% of casualties and billions of dollars of financial losses worldwide [1]. Globally, the highest numbers of fatalities from landslides occur in the mountains of Asia and Central and South America, as well as on steep islands in the Caribbean and Southeast Asia [2].…”

Section: Introductionmentioning

confidence: 99%

Local variations of landslide factors in Pulau Pinang, Malaysia

Zulkafli

Majid

Rainis

2023

IOP Conf. Ser.: Earth Environ. Sci.

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A landslide is one of the most notorious natural disasters, resulting in massive losses and significant damages. Thus, this paper aims to analyze the spatial heterogeneity of the influencing factors which later inspect the relationship between the factors and landslide occurrences. A total of 988 landslides historical data and eight landslide factors were obtained from proper field validation and maps provided by those concerned in the government, including distance to roads, distance to rivers, distance to faults, slope angle, curvature, slope aspect, land use, and lithology. Geographically Weighted Logistic Regression (GWLR) is introduced in this paper to carry out the local analysis, resulting in the slope angle and the slope aspect playing the most significant role in influencing landslides. The Akaike’s information criterion (AICc) of GWLR is 824.51 which has a lower value than the global regression represented as 906.09 revealing that GWLR is the best model. Other evaluation criteria such as deviance, local percent deviance explained (pdev), and Bayesian information criterion (BIC) also validate the significance of the GWLR model. The GWLR results show the degree of spatial variation in the relationship between landslides and the influencing factors in the study area as the coefficient values of every factor are inconsistent, providing a reference for managers to formulate targeted decision-making measures. In the meantime, urgent action to sustain this natural disaster as suggested in the SDG 13 has to be taken earnestly to avoid bigger impacts on both society and the environment.

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“…Therefore, hybrid machine learning algorithms have recently generated promising results in landslide prediction modeling 23 , 83 , 84 . The base model prediction performance is improved by using a hybrid technique 10 .…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the hybrid models higher performance in predicting landslide revealed that the landslide modeling may be improved by factor optimization 85 . Many studies have demonstrated the value of a hybrid strategy in landslide situations all over the world 10 , 82 , 83 , 86 . Due to regional geological and geomorphological causes, slope unsteadiness, including landslides, is a common problem in Uttarkashi, Uttarakhand 87 – 89 .…”

Section: Introductionmentioning

confidence: 99%

“…Several well-known and well-tested machine learning algorithms, such as SVM, Naïve Bayes, Logistic Regression, Bayesian Network, BPNN, and Random forest were used in this study because they produce great results in the situation of landslide assessment 68 , 93 . The ability to forecast using various combinations of machine learning technologies is always improving 83 . To select the optimal model, we investigated five hybrid models namely XGBoost-Rough Set (XGBRS), Backpropagation Neural Network-Rough Set (BPNNRS), Bayesian Network-Rough Set (BNRS), Bagging-Rough Set (BRS), and Random Forest-Rough Set (RFRS) in the landslide prediction modeling of the Uttarkashi district, Uttarakhand, India.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid machine learning approach for landslide prediction, Uttarakhand, India

Kainthura

Sharma

2022

Sci Rep

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Natural disasters always have a damaging effect on our way of life. Landslides cause serious damage to both human and natural resources around the world. In this paper, the prediction accuracy of five hybrid models for landslide occurrence in the Uttarkashi, Uttarakhand (India) was evaluated and compared. In this approach, the Rough Set theory coupled with five different models namely Bayesian Network (HBNRS), Backpropagation Neural Network (HBPNNRS), Bagging (HBRS), XGBoost (HXGBRS), and Random Forest (HRFRS) were taken into account. The database for the models development was prepared using fifteen conditioning factors that had 373 landslide and 181 non-landslide locations that were then randomly divided into training and testing locations with a ratio of 75%:25%. The appropriateness and predictability of these conditioning factors were assessed using the multi-collinearity test and the least absolute shrinkage and selection operator approach. The accuracy, sensitivity, specificity, precision, and F-Measures, and the area under the curve (AUC)-receiver operating characteristics curve, were used to evaluate and compare the performance of the individual and hybrid created models. The findings indicate that the constructed hybrid model HXGBRS (AUC = 0.937, Precision = 0.946, F1-score = 0.926 and Accuracy = 89.92%) is the most accurate model for predicting landslides when compared to other models (HBPNNRS, HBNRS, HBRS, and HRFRS). Importantly, when the fusion is performed with the rough set method, the prediction capability of each model is improved. Simultaneously, the HXGBRS model proposed shows superior stability and can effectively avoid overfitting. After the core modules were developed, the user-friendly platform was designed as an integrated GIS environment using dynamic maps for effective landslide prediction in large prone areas. Users can predict the probability of landslide occurrence for selected region by changing the values of a conditioning factors. The created approach could be beneficial for predicting the impact of landslides on slopes and tracking landslides along national routes.

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Improvement of landslide spatial modeling using machine learning methods and two Harris hawks and bat algorithms

Cited by 13 publications

References 31 publications

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

Local variations of landslide factors in Pulau Pinang, Malaysia

Hybrid machine learning approach for landslide prediction, Uttarakhand, India

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