Determination of rainfall thresholds for landslide prediction using an algorithm-based approach: Case study in the Darjeeling Himalayas, India

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. Landslides are one of the most devastating and commonly recurring natural hazards in the Indian Himalayas. They contribute to infrastructure damage, land loss and human casualties. Most of the landslides are primarily rainfall-induced and the relationship has been well very well-established, having been commonly defined using empirical-based models which use statistical approaches to determine the parameters of a power-law equation. One of the main drawbacks using the traditional empirical methods is that it fails to reduce the uncertainties associated with threshold calculation. The present study overcomes these limitations by identifying the precipitation condition responsible for landslide occurrence using an algorithm-based model. The methodology involves the use of an automated tool which determines cumulated event rainfall–rainfall duration thresholds at various exceedance probabilities and the associated uncertainties. The analysis has been carried out for the Kalimpong Region of the Darjeeling Himalayas using rainfall and landslide data for the period 2010–2016. The results signify that a rainfall event of 48 h with a cumulated event rainfall of 36.7 mm can cause landslides in the study area. Such a study is the first to be conducted for the Indian Himalayas and can be considered as a first step in determining more reliable thresholds which can be used as part of an operational early-warning system

Long lead time drought forecasting using lagged climate variables and a stacked long short-term memory model.

Drought forecasting with a long lead time is essential for early warning systems and risk management strategies. The use of machine learning algorithms has been proven to be beneficial in forecasting droughts. However, forecasting at long lead times remains a challenge due to the effects of climate change and the complexities involved in drought assessment. The rise of deep learning techniques can solve this issue, and the present work aims to use a stacked long short-term memory (LSTM) architecture to forecast a commonly used drought measure, namely, the Standard Precipitation Evaporation Index. The model was then applied to the New South Wales region of Australia, with hydrometeorological and climatic variables as predictors. The multivariate interpolated grid of the Climatic Research Unit was used to compute the index at monthly scales, with meteorological variables as predictors. The architecture was trained using data from the period of 1901-2000 and tested on data from the period of 2001-2018. The results were then forecasted at lead times ranging from 1 month to 12 months. The forecasted results were analysed in terms of drought characteristics, such as drought intensity, drought onset, spatial extent and number of drought months, to elucidate how these characteristics improve the understanding of drought forecasting. The drought intensity forecasting capability of the model used two statistical metrics, namely, the coefficient of determination (R2) and root-mean-square error. The variation in the number of drought months was examined using the threat score technique. The results of this study showed that the stacked LSTM model can forecast effectively at short-term and long-term lead times. Such findings will be essential for government agencies and can be further tested to understand the forecasting capability of the presented architecture at shorter temporal scales, which can range from days to weeks

Ascorbate-mediated enhancement of reactive oxygen species generation from polymorphonuclear leukocytes: modulatory effect of nitric oxide

Recent studies from our laboratory have demonstrated that ascorbate potentiated enzymatic synthesis of nitric oxide (NO) from polymorphonuclear leukocytes (PMNs). NO is known to modulate various function of PMNs such as chemotaxis, adherence, aggregation, and generation of reactive oxygen species (ROS). The role of ascorbate in the PMN phagocytosis, ROS generation, and apoptosis was thus evaluated in the present study. Ascorbate and its oxidized and cell-permeable analog, dehydroascorbate (DHA), did not affect the phagocytosis but enhanced ROS generation and apoptosis following treatment with Escherichia coli or arachidonic acid. A detailed investigation on the DHA-mediated response indicated that inhibitors of DHA uptake, reduced nicotinamide adenine dinucleotide phosphate oxidase, NO synthase, or ROS scavengers attenuated ROS generation. In DHA-treated cells, enhanced generation of peroxynitrite was also observed; thus, ascorbate-mediated ROS and reactive nitrogen species generation might mediate cytotoxicity toward the ingested microbes and subsequently, augmented PMN apoptosis. Results of the present study have helped in delineating the role of ascorbate in the modulation of NO-mediated ROS generation from PMNs

Characterization of nutraceuticals in bael powder prepared from fruits harvested at different developmental stages

724-730Bael [Aegle marmelos (L.) Correa], is well known in Indian traditional medical system for its multipurpose use in treatment of various diseases. Fresh ripe fruits are used in various types of shakes and sharbats but bael fruits are mainly used into its processed form like nectar or squash, jelly, candy and murabba. Bael powder is another form of product which has very high pharmaceutical value, long storability and is the pure concentrated form of fruit pulp. The aim of this study is to measure the nutraceutical values in bael powder (dry weight basis) prepared from fruit of CISH B-1 harvested at various stages of growth and development [180–335 days after fruit set (DAFS)] by using a simple HPLC technique and atomic absorption spectrophotometer (AAS). The antioxidants value (in terms of FRAP) ranges from 13.45 mmol/g at 180 DAFS (month of November) to 22.6 mmol/g at 335 DAFS (month of April). Maximum polyphenols content (5.99%) was observed at 305 and 335 DAFS (months of March and April). The antioxidants and polyphenols were enhanced significantly with the maturity of the fruits. Marmelosin and psoralen concentrations were highest at 215 DAFS and were found as 737 and 511 µg/g, respectively. Thereafter, both compounds declined significantly in mature fruit powder. Mineral contents in powder also varied with maturity stages. From this study, it may be concluded that powder prepared from immature fruits collected at early stages of development (November-January; 180–245 DAFS), possessed significantly higher amount of potassium, iron, marmelosin, psoralen and tannic acid, whereas, mature fruit powder (harvested during March-April; 305-335 DAFS) contains significantly higher content of zinc, copper, polyphenols and antioxidants

Instability in Rice Production in Gujarat: A Decomposition Analysis

Rice is the most important and extensively grown food crop in India and is the staple food for more than half of the world population. In India, Gujarat ranks 15th in terms of area and production and 9th in productivity (2011). The scope for expanding rice production lies in enhancing productivity. The growth rates of rice area, production and productivity during 1982-83 to 2011-12 were 0.41, 1.25 and 0.83 percent per annum respectively. The growth estimate from last 30 years data shows that negligible increase was recorded in area and production of rice. Presently the yield level of rice in the state is comparatively low from national average need to be increased substantially. The magnitude of instability in area and production of rice has been higher in all the selected districts compared to state. Variability in production has been at a higher rate compared to area and productivity variability in this crop. The area-yield co-variance had a stabilizing effect on reduction of instability in rice production It can be inferred that the wide fluctuation in production of rice crop have been due to the high variability in its productivity. The future development programmes should envisage on increase of yield for bringing stabilization in production of the crop. The area instability also needs to be reduced. This could be reduced by more investment on research for rice production technology in the state

SPECTRAL-SPATIAL CLASSIFICATION OF HYPERSPECTRAL REMOTE SENSING IMAGES USING VARIATIONAL AUTOENCODER AND CONVOLUTION NEURAL NETWORK

In this paper, we propose a spectral-spatial feature extraction framework based on deep learning (DL) for hyperspectral image (HSI) classification. In this framework, the variational autoencoder (VAE) is used for extraction of spectral features from two widely used hyperspectral datasets- Kennedy Space Centre, Florida and University of Pavia, Italy. Additionally, a convolutional neural network (CNN) is utilized to obtain spatial features. The spatial and spectral feature vectors are then stacked together to form a joint feature vector. Finally, the joint feature vector is trained using multinomial logistic regression (softmax regression) for prediction of class labels. The classification performance analysis is done through generation of the confusion matrix. The confusion matrix is then used to calculate Cohen’s Kappa (Κ) to get a quantitative measure of classification performance. The results show that the K value is higher than 0.99 for both HSI datasets

Pathways and challenges of the application of artificial intelligence to geohazards modelling

© 2020 International Association for Gondwana Research The application of artificial intelligence (AI) and machine learning in geohazard modelling has been rapidly growing in recent years, a trend that is observed in several research and application areas thanks to recent advances in AI. As a result, the increasing dependence on data driven studies has made its practical applications towards geohazards (landslides, debris flows, earthquakes, droughts, floods, glacier studies) an interesting prospect. These aforementioned geohazards were responsible for roughly 80% of the economic loss in the past two decades caused by all natural hazards. The present study analyses the various domains of geohazards which have benefited from classical machine learning approaches and highlights the future course of direction in this field. The emergence of deep learning has fulfilled several gaps in: i) classification; ii) seasonal forecasting as well as forecasting at longer lead times; iii) temporal based change detection. Apart from the usual challenges of dataset availability, climate change and anthropogenic activities, this review paper emphasizes that the future studies should focus on consecutive events along with integration of physical models. The recent catastrophe in Japan and Australia makes a compelling argument to focus towards consecutive events. The availability of higher temporal resolution and multi-hazard dataset will prove to be essential, but the key would be to integrate it with physical models which would improve our understanding of the mechanism involved both in single and consecutive hazard scenario. Geohazards would eventually be a data problem, like geosciences, and therefore it is essential to develop models that would be capable of handling large voluminous data. The future works should also revolve towards interpretable models with the hope of providing a reasonable explanation of the results, thereby achieving the ultimate goal of Explainable AI

Short-term spatio-temporal drought forecasting using random forests model at New South Wales, Australia

© 2020 by the authors. Droughts can cause significant damage to agriculture and water resources, leading to severe economic losses and loss of life. One of the most important aspect is to develop effective tools to forecast drought events that could be helpful in mitigation strategies. The understanding of droughts has become more challenging because of the effect of climate change, urbanization and water management; therefore, the present study aims to forecast droughts by determining an appropriate index and analyzing its changes, using climate variables. The work was conducted in three different phases, first being the determination of Standard Precipitation Evaporation Index (SPEI), using global climatic dataset of Climate Research Unit (CRU) from 1901-2018. The indices are calculated at different monthly intervals which could depict short-term or long-term changes, and the index value represents different drought classes, ranging from extremely dry to extremely wet. However, the present study was focused only on forecasting at short-term scales for New SouthWales (NSW) region of Australia and was conducted at two different time scales, one month and three months. The second phase involved dividing the data into three sample sizes, training (1901-2010), testing (2011-2015) and validation (2016-2018). Finally, a machine learning approach, Random Forest (RF), was used to train and test the data, using various climatic variables, e.g., rainfall, potential evapotranspiration, cloud cover, vapor pressure and temperature (maximum, minimum and mean). The final phase was to analyze the performance of the model based on statistical metrics and drought classes. Regarding this, the performance of the testing period was conducted by using statistical metrics, Coefficient of Determination (R2) and Root-Mean-Square-Error (RMSE) method. The performance of the model showed a considerably higher value of R2 for both the time scales. However, statistical metrics analyzes the variation between the predicted and observed index values, and it does not consider the drought classes. Therefore, the variation in predicted and observed SPEI values were analyzed based on different drought classes, which were validated by using the Receiver Operating Characteristic (ROC)-based Area under the Curve (AUC) approach. The results reveal that the classification of drought classes during the validation period had an AUC of 0.82 for SPEI 1 case and 0.84 for SPEI 3 case. The study depicts that the Random Forest model can perform both regression and classification analysis for drought studies in NSW. The work also suggests that the performance of any model for drought forecasting should not be limited only through statistical metrics, but also by examining the variation in terms of drought characteristics

Explainable artificial intelligence for sarcasm detection in dialogues

Sarcasm detection in dialogues has been gaining popularity among natural language processing (NLP) researchers with the increased use of conversational threads on social media. Capturing the knowledge of the domain of discourse, context propagation during the course of dialogue, and situational context and tone of the speaker are some important features to train the machine learning models for detecting sarcasm in real time. As situational comedies vibrantly represent human mannerism and behaviour in everyday real-life situations, this research demonstrates the use of an ensemble supervised learning algorithm to detect sarcasm in the benchmark dialogue dataset, MUStARD. The punch-line utterance and its associated context are taken as features to train the eXtreme Gradient Boosting (XGBoost) method. The primary goal is to predict sarcasm in each utterance of the speaker using the chronological nature of a scene. Further, it is vital to prevent model bias and help decision makers understand how to use the models in the right way. Therefore, as a twin goal of this research, we make the learning model used for conversational sarcasm detection interpretable. This is done using two post hoc interpretability approaches, Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive exPlanations (SHAP), to generate explanations for the output of a trained classifier. The classification results clearly depict the importance of capturing the intersentence context to detect sarcasm in conversational threads. The interpretability methods show the words (features) that influence the decision of the model the most and help the user understand how the model is making the decision for detecting sarcasm in dialogues