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Recently, the spread of white spot disease in shrimps has a major impact on the aquaculture activity worldwide affecting the economy of the countries, especially South-East Asian countries like Vietnam. This deadly disease in shrimps is caused by the White Spot Syndrome Virus (WSSV). Researchers are trying to understand the spread and control of this disease by doing field and laboratory studies considering effect of environmental conditions on shrimps affected by WSSV. Generally, they have not considered spatial factors in their study. Therefore, in the present study, we have used spatial (distances to roads and factories) as well as physio-chemical factors of water: Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), Salinity, NO3, P3O4 and pH, for developing WSSV susceptibility maps of the area using Decision Tree (DT)-based Machine Learning (ML) models namely Random Tree (RT), Extra Tree (ET), and J48. Model's performance was evaluated using standard statistical measures including Area Under the Curve (AUC). The results indicated that ET model has the highest accuracy (AUC: 0.713) in predicting disease susceptibility in comparison to other two models (RT: 0.701 and J48: 0.641). The WSSV susceptibility maps developed by the ML technique, using DT (ET) method, will help decision makers in better planning and control of spatial spread of WSSV disease in shrimps. [ABSTRACT FROM AUTHOR]

This study undertakes a comparative analysis of four distinct deep learning models, i.e., Convolutional Neural Network (CNN), Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM), in the context of roadside landslide prediction, aiming to provide comprehensive insights into their strengths and weaknesses. A geospatial dataset from the Lai Châu province, Vietnam, with thirteen environmental factors (elevation, aspect, slope, curvature, topographic wetness index, stream power index, flow accumulation, geology, normalized difference vegetation index, maximum rainfall, average annual rainfall, and proximity to faults and rivers) and 284 road-along landslides was considered for analysis. Our modeling efforts yielded invaluable insights into the performance of these models during both training and validation phases. The DNN model emerged as the frontrunner in the training phase, boasting the highest area under the curve (AUC) of 0.94, accuracy of 87.47%, kappa of 0.748, and lowest RMSE of 0.125. However, during validation, the CNN model outshone others, exhibiting the highest AUC of 0.88 and overall accuracy of 80.00%. Despite variations in performance metrics across phases, CNN consistently demonstrated robust predictive prowess. The findings of this study underscore the significance of selecting appropriate machine learning models tailored to specific contexts and objectives. Moreover, they contribute valuable insights for decision-makers and researchers alike, ultimately aiming to enhance the safety and resilience of communities inhabiting landslide-prone areas. Moving forward, future research directions may explore ensemble methods, novel architectures, and interpretability techniques to further advance predictive accuracy and applicability in roadside landslide susceptibility modeling. • Different deep learning models were developed for effective landslide susceptibility mapping. • Database of landslides along the road located in Vietnam was used for modeling. • SHAP analysis was used for explaining the modeling process of deep learning models. • Results of this study showed the potential of deep learning models for landslide modeling. [ABSTRACT FROM AUTHOR]

Worldwide landslide occurrences imply the need for intelligence tools to identify the most susceptible areas toward adopting efficient mitigation strategies and reaction plans. In this study, we developed three spatially explicit ensemble predictive models for the prediction of landslide susceptibility in the Muong Nhe district of the Dien Bien Province, Vietnam. The Multiclass Alternating Decision Trees (MADT) method was used as the base classifier with the Dagging, MultiboostAB, and Random Subspace (RSS) as the ensemble learners. The location of past landslides was identified through field surveys and the interpretation of Google Earth images, aerial photographs, and historical archives of the Muong Nhe district. The landslide locations were liked to twelve landslide conditioning factors (slope, aspect, elevation, curvature, topographic wetness index (TWI), stream power index (SPI), geology, flow accumulation, normalized difference vegetation index (NDVI), and distance to rivers, roads, and faults) to investigate the spatial patterns of landslide susceptibility across the study area. The results showed that the RSS-MADT model achieved the highest performance in terms of predicting future landslides (AUC = 0.878), followed by DG-MADT (AUC = 0.857), MAB-MADT (AUC = 0.854), and the single MADT model (AUC = 0.828), respectively. Approximately 13% and 10% of the Muong Nhe district were identified as having moderate and severe (high/very high) susceptibility to landslide occurrences. These areas that extend along the rivers, primarily in the central parts of the Muong Nhe district, should be treated with the highest priority to mitigate the negative impacts of future landslides. • Developing three ensemble models for landslide susceptibility mapping. • Performance test of Dagging, MultiboostAB, and Random Subspace ensemble techniques. • Highest reliability of mapping coupling Alternating Decision Trees with Random Subspace. • Landslide prevention activities primarily needed on 13% of the land area. [ABSTRACT FROM AUTHOR]