Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms—Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine—in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for modeling and validation processes. The performance of the models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naïve Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) models. Therefore, we recommend the use of the Logistic Model Tree model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk.

This study highlights the severity of arsenic contamination in the Ganga River basin (GRB), which encompasses significant geographic portions of India, Bangladesh, Nepal, and Tibet. The entire GRB experiences elevated levels of arsenic in the groundwater (up to 4730 µg/L), irrigation water (~1000 µg/L), and in food materials (up to 3947 µg/kg), all exceeding the World Health Organization’s standards for drinking water, the United Nations Food and Agricultural Organization’s standard for irrigation water (100 µg/L), and the Chinese Ministry of Health’s standard for food in South Asia (0.15 mg/kg), respectively. Several individuals demonstrated dermal, neurological, reproductive, cognitive, and cancerous effects; many children have been diagnosed with a range of arsenicosis symptoms, and numerous arsenic-induced deaths of youthful victims are reported in the GRB. Victims of arsenic exposure face critical social challenges in the form of social isolation and hatred by their respective communities. Reluctance to establish arsenic standards and unsustainable arsenic mitigation programs have aggravated the arsenic calamity in the GRB and put millions of lives in danger. This alarming situation resembles a ticking time bomb. We feel that after 29 years of arsenic research in the GRB, we have seen the tip of the iceberg with respect to the actual magnitude of the catastrophe; thus, a reduced arsenic standard for drinking water, testing all available drinking water sources, and sustainable and cost-effective arsenic mitigation programs that include the participation of the people are urgently needed.

Landslides have multidimensional effects on the socioeconomic as well as environmental conditions of the impacted areas. The aim of this study is the spatial prediction of landslide using hybrid machine learning models including bagging (BA), random subspace (RS) and rotation forest (RF) with alternating decision tree (ADTree) as base classifier in the northern part of the Pithoragarh district, Uttarakhand, Himalaya, India. To construct the database, ten conditioning factors and a total of 103 landslide locations with a ratio of 70/30 were used. The significant factors were determined by chi-square attribute evaluation (CSEA) technique. The validity of the hybrid models was assessed by true positive rate (TP Rate), false positive rate (FP Rate), recall (sensitivity), precision, F-measure and area under the receiver operatic characteristic curve (AUC). Results concluded that land cover was the most important factor while curvature had no effect on landslide occurrence in the study area and it was removed from the modelling process. Additionally, results indicated that although all ensemble models enhanced the power prediction of the ADTree classifier (AUC_training = 0.859; AUC_validation = 0.813); however, the RS ensemble model (AUC_training = 0.883; AUC_validation = 0.842) outperformed and outclassed the RF (AUC_training = 0.871; AUC_validation = 0.840), and the BA (AUC_training = 0.865; AUC_validation = 0.836) ensemble model. The obtained results would be helpful for recognizing the landslide prone areas in future to better manage and decrease the damage and negative impacts on the environment.

In the present study, Rotation Forest ensemble was integrated with different base classifiers to develop different hybrid models namely Rotation Forest based Support Vector Machines (RFSVM), Rotation Forest based Artificial Neural Networks (RFANN), Rotation Forest based Decision Trees (RFDT), and Rotation Forest based Naïve Bayes (RFNB) for landslide susceptibility modelling. The validity of these models was evaluated using statistical methods such as Root Mean Square Error (RMSE), Kappa index, accuracy, and the area under the success rate and predictive rate curves (AUC). Part of the landslide prone area of Pithoragarh district, Uttarakhand, Himalaya, India was selected as the study area. Results indicate that the RFDT is the best model showing the highest predictive capability (AUC = 0.741) in comparison to RFANN (AUC = 0.710), RFSVM (AUC = 0.701), and RFNB (AUC = 0.640) models. The present study would be helpful in the selection of best model for landslide susceptibility mapping.