Landslides are prevalent in the Western Ghats, and the incidences that happened in 2021 in the Koottickal area of the Kottayam district (Western Ghats) resulted in the loss of 10 lives. The objectives of this study are to assess the landslide susceptibility of the high-range local self-governments (LSGs) in the Kottayam district using the analytical hierarchy process (AHP) and fuzzy-AHP (F-AHP) models and to compare the performance of existing landslide susceptible maps. This area never witnessed any massive landslides of this dimension, which warrants the necessity of relooking into the existing landslide-susceptible models. For AHP and F-AHP modeling, ten conditioning factors were selected: slope, soil texture, land use/land cover (LULC), geomorphology, road buffer, lithology, and satellite image-derived indices such as the normalized difference road landslide index (NDRLI), the normalized difference water index (NDWI), the normalized burn ratio (NBR), and the soil-adjusted vegetation index (SAVI). The landslide-susceptible zones were categorized into three: low, moderate, and high. The validation of the maps created using the receiver operating characteristic (ROC) technique ascertained the performances of the AHP, F-AHP, and TISSA maps as excellent, with an area under the ROC curve (AUC) value above 0.80, and the NCESS map as acceptable, with an AUC value above 0.70. Though the difference is negligible, the map prepared using the TISSA model has better performance (AUC = 0.889) than the F-AHP (AUC = 0.872), AHP (AUC = 0.867), and NCESS (AUC = 0.789) models. The validation of maps employing other matrices such as accuracy, mean absolute error (MAE), and root mean square error (RMSE) also confirmed that the TISSA model (0.869, 0.226, and 0.122, respectively) has better performance, followed by the F-AHP (0.856, 0.243, and 0.147, respectively), AHP (0.855, 0.249, and 0.159, respectively), and NCESS (0.770, 0.309, and 0.177, respectively) models. The most landslide-inducing factors in this area that were identified through this study are slope, soil texture, LULC, geomorphology, and NDRLI. Koottickal, Poonjar-Thekkekara, Moonnilavu, Thalanad, and Koruthodu are the LSGs that are highly susceptible to landslides. The identification of landslide-susceptible areas using diversified techniques will aid decision-makers in identifying critical infrastructure at risk and alternate routes for emergency evacuation of people to safer terrain during an exigency.

Wildfires are one of the gravest and most momentous hazards affecting rich forest biomes worldwide; India is one of the hotspots due to its diverse forest types and human-induced reasons. This research aims to identify wildfire risk zones in two contrasting climate zones, the Wayanad Wildlife Sanctuary in the Western Ghats and the Kedarnath Wildlife Sanctuary in the Himalayas, using geospatial tools, analytical hierarchy process (AHP), and fuzzy-AHP models to assess the impacts of various conditioning factors and compare the efficacy of the two models. Both of the wildlife sanctuaries were severely battered by fires in the past, with more than 100 fire incidences considered for this modeling. This analysis found that both natural and anthropogenic factors are responsible for the fire occurrences in both of the two sanctuaries. The validation of the risk maps, utilizing the receiver operating characteristic (ROC) method, proved that both models have outstanding prediction accuracy for the training and validation datasets, with the F-AHP model having a slight edge over the other model. The results of other statistical validation matrices such as sensitivity, accuracy, and Kappa index also confirmed that F-AHP is better than the AHP model. According to the F-AHP model, about 22.49% of Kedarnath and 17.12% of Wayanad fall within the very-high risk zones. The created models will serve as a tool for implementing effective policies intended to reduce the impact of fires, even in other protected areas with similar forest types, terrain, and climatic conditions.

With population and income growth, the need for energy has increased in developing and emerging economies, which has inevitably led to an increase in carbon dioxide emissions (CO₂e). This paper investigates the impact of energy consumption on CO₂e influenced by population growth, energy consumption per capita, and income. In particular, this paper investigates whether or not an increase in energy consumption, energy intensity, energy consumption per capita, population growth, and income impacts CO₂e in China, India, and the USA. The study applied the non-linear Autoregressive distributed lag (NARDL) and machine learning techniques. We found a significant impact of energy consumption per capita on the CO₂ emissions in China, India, and USA. Furthermore, the results revealed that, when income increased, CO₂ emissions increased in India, but decreased in the USA. The results confirmed that population growth increases CO₂ emissions only in India. The results revealed that a decrease in energy intensity significantly improves the environmental quality in China and India. Finally, we forecasted the CO₂e trend from 2017 to 2025. The results revealed an upcoming increase in CO₂e levels in China and India. Conversely, the forecasted results demonstrated a downward trend of CO₂e emissions in the USA.

Increasing agricultural production is a major concern that aims to increase income, reduce hunger, and improve other measures of well-being. Recently, the prediction of soil-suitability has become a primary topic of rising concern among academics, policymakers, and socio-economic analysts to assess dynamics of the agricultural production. This work aims to use physico-chemical and remotely sensed phenological parameters to produce soil-suitability maps (SSM) based on Machine Learning (ML) Algorithms in a semi-arid and arid region. Towards this goal an inventory of 238 suitability points has been carried out in addition to14 physico-chemical and 4 phenological parameters that have been used as inputs of machine-learning approaches which are five MLA prediction, namely RF, XgbTree, ANN, KNN and SVM. The results showed that phenological parameters were found to be the most influential in soil-suitability prediction. The validation of the Receiver Operating Characteristics (ROC) curve approach indicates an area under the curve and an AUC of more than 0.82 for all models. The best results were obtained using the XgbTree with an AUC = 0.97 in comparison to other MLA. Our findings demonstrate an excellent ability for ML models to predict the soil-suitability using physico-chemical and phenological parameters. The approach developed to map the soil-suitability is a valuable tool for sustainable agricultural development, and it can play an effective role in ensuring food security and conducting a land agriculture assessment.

Positive and negative magnetic anomalies occupied the total aeromagnetic (TM) map of the Harrat ad Danun area, Saudi Arabia. Reduction to the pole (RTP) maps display the range of magnetic values (−312.4 to 209.4 nT) that vary in shape, size, and magnitude. These anomalies generally follow the NNW–SSE (Red Sea axis trend), NE–SW, and NNE–SSW trends. The NNW-SSE linear negative and positive magnetic anomalies could be brought on by buried faults, shear zones, or subsurface dikes. In the central part, the position of Au and Ba mineralization was connected to this trend. It is concluded that the principal structures are represented by the NNW–SSE, NE–SW, and NNE–SSW tendencies. Based on gridded RTP magnetic data, the 2-D power spectrum was computed and revealed the frequency of the near-surface and deep magnetic components. It is believed that the depths of the shallow and deep magnetic sources are typically 80 m and 570 m, respectively. Additional negative and positive magnetic anomalies with varied amplitudes and frequencies, trending in the NNW–SSE, ENE–WSW, and NE–SW directions, are seen when the high-pass and low-pass maps are closely examined. Many faults in various directions cut into these anomalies. The occurrence of negative linear magnetic anomalies (−36.6 nT to −137.3 nT) at this depth (80 m) is also confirmed by this map. The TDR filter and the Euler deconvolution method were used to identify the horizontal variations in magnetic susceptibility as well as the source position and depth of magnetic sources. The linear clustering rings are thought to be caused by contacts or faults with depths between 1 m to 474 m that are oriented WNW–ESE, NNE–SSW, and NNW–SSE. These faults or contacts are thought to be particularly prominent in the western, eastern, southern, northern, and central zones. The majority of felsic and mafic dikes are found to be connected to subsurface structures, showing that three structural trends—WNW–ESE, NNE–SSW, and NNW–SSE—affect the studied area. This demonstrates that important features and shear zones control the majority of Saudi Arabia’s gold deposits. A negative magnetic anomaly that is centered in the area, trending NNW–SSE and crossing the NNE–SSW fault, is connected to the plotted gold and barite mineralization in the study area. This may imply that these two tendencies are responsible for mineralization. This result raises the possibility of mineralization in the NNW negative magnetic feature located in the western part of the area. The occurrence of gold and barite was significantly impacted by the NNW–SSE and NNE–SSW structural lineaments.