Hybrid time series models with exogenous variable for improved yield forecasting of major Rabi crops in India.

Accurate and in-time prediction of crop yield plays a crucial role in the planning, management, and decision-making processes within the agricultural sector. In this investigation, utilizing area under irrigation (%) as an exogenous variable, we have made an exertion to assess the suitability of different hybrid models such as ARIMAX (Autoregressive Integrated Moving Average with eXogenous Regressor)-TDNN (Time-Delay Neural Network), ARIMAX-NLSVR (Non-Linear Support Vector Regression), ARIMAX-WNN (Wavelet Neural Network), ARIMAX-CNN (Convolutional Neural Network), ARIMAX-RNN (Recurrent Neural Network) and ARIMAX-LSTM (Long Short Term Memory) as compared to their individual counterparts for yield forecasting of major Rabi crops in India. The accuracy of the ARIMA model has also been considered as a benchmark. Empirical outcomes reveal that the ARIMAX-LSTM hybrid modeling combination outperforms all other time series models in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE) values. For these models, an average improvement of RMSE and MAPE values has been observed to be 10.41% and 12.28%, respectively over all other competing models and 15.83% and 18.42%, respectively over the benchmark ARIMA model. The incorporation of the area under irrigation (%) as an exogenous variable in the ARIMAX framework and the inbuilt capability of the LSTM model to process complex non-linear patterns have been observed to significantly enhance the accuracy of forecasting. The performance supremacy of other hybrid models over their individual counterparts has also been evident. The results also suggest avoiding any performance generalization of individual models for their hybrid structures.

Vegetation diversity pattern during spring season in relation to topographic and edaphic variables in sub-tropical zone.

BACKGROUND: The present study was conducted to explore the diversity pattern of spring vegetation under the influence of topographic and edaphic variables in sub-tropical zone, District Malakand. In the present vegetation study, 252 species of 80 families were recorded in the study area. It included 39 species of trees, 43 species of shrubs, 167 species of herbs and 3 climber species. As a whole, 12 communities were established on the basis of topographic and edaphic characteristics in 12 different stations. RESULTS: The results of the present study revealed that all diversity indices (species diversity, evenness index, species richness index, maturity index) during spring showed that the communities in plains lying at lower altitudes had higher diversity while the communities formed at high altitudes had lower diversity. The results of the similarity index showed that there was low similarity (below 50%) amongst the communities in different stations. CONCLUSIONS: It can be concluded that variations in topographic and edaphic factors affect species diversity and communities pattern.

Households' vulnerability assessment: empirical evidence from cyclone-prone area of Bangladesh.

Despite Bangladesh being vulnerable to cyclones, there is a dearth of research on cyclone vulnerability assessment. Assessing a household's vulnerability is considered a crucial step in avoiding the adverse effects of catastrophe risks. This research was conducted in the cyclone-prone district of Barguna, Bangladesh. This study's purpose is to evaluate this region's vulnerability. A questionnaire survey was conducted using a convenience sample technique. A door-to-door survey of 388 households in two Unions of Patharghata Upazila, Barguna district, was conducted. Forty-three indicators were selected to assess cyclone vulnerability. The results were quantified using an index-based methodology with a standardized scoring method. Where applicable, descriptive statistics have been obtained. In terms of vulnerability indicators, we also utilized the chi-square test to compare Kalmegha and Patharghata Union. When appropriate, the non-parametric Mann-Whitney U test was employed to evaluate the relationship between the Vulnerability Index Score (VIS) and the union. According to the results, the environmental vulnerability (0.53 ± 0.17) and the composite vulnerability index (0.50 ± 0.08) were significantly greater in Kalmegha Union than in Patharghata Union. They faced inequity in government assistance (71%) and humanitarian aid (45%) from national and international organizations. However, 83% of them underwent evacuation practices. 39% were satisfied with the WASH conditions at the cyclone shelter, whereas around half were dissatisfied with the status of the medical facilities. Most of them (96%) rely only on surface water for drinking. National and international organizations should have a comprehensive plan for disaster risk reduction that encompasses all individuals, regardless of race, geography, or ethnicity.

Sanitary municipal landfill site selection by integration of GIS and multi-criteria techniques for environmental sustainability in Safita area, Tartous governorate, Syria.

Urban waste disposal is a problem that poses a major challenge to city planners as a result of rapid population growth and urbanization. Finding suitable sites for solid waste is one of the most important solutions developed globally to manage this problem. In this regard, a set of physical, socio-economic and technological criteria must be considered to tackle the problem. Safita area (Tartous governorate) witnessed a rapid population growth during the decade of the war in Syria due to the onrush of internal refugees, which resulted in several environmental problems, including random waste dumps. After perusing the previous literature and considering expert opinions, a map of the spatial suitability of sustainable waste sites in the Safita area was developed by integrating the multi-criteria decision- making methodology (analytic hierarchy process) with the geographic information system. Thirteen criteria, including elevation, slope, permeability, distance to faults, distance to settlement, land use/land cover, distance to drainage, distance to water supplies, distance to lakes, distance to road, distance from tourist centers, distance from archaeological centers, and distance from religious centers, were used to achieve the goal of this study. The layer maps for these criteria were developed based on various data sources, including conventional and remote sensing data. Potential landfill sites were identified and divided into five categories: unsuitable (83.28%), less suitable (8.49%), moderately suitable (4.49%), highly suitable (2.57%), and very highly suitable (0.72%). The results of this study provide reliable spatial outputs that will help in suggesting new landfill sites that maintain environmental and socio-economic sustainability in the post-war phase. Moreover, the application of the methodology of this study can be generalized to the rest of the regions in Syria within the framework of the integrated management of the problem of random landfills.

Evaluation of Vulnerability Status of the Infection Risk to COVID-19 Using Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA): A Case Study of Addis Ababa City, Ethiopia.

COVID-19 is a disease caused by a new coronavirus called SARS-CoV-2 and is an accidental global public health threat. Because of this, WHO declared the COVID-19 outbreak a pandemic. The pandemic is spreading unprecedently in Addis Ababa, which results in extraordinary logistical and management challenges in response to the novel coronavirus in the city. Thus, management strategies and resource allocation need to be vulnerability-oriented. Though various studies have been carried out on COVID-19, only a few studies have been conducted on vulnerability from a geospatial/location-based perspective but at a wider spatial resolution. This puts the results of those studies under question while their findings are projected to the finer spatial resolution. To overcome such problems, the integration of Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA) has been developed as a framework to evaluate and map the susceptibility status of the infection risk to COVID-19. To achieve the objective of the study, data like land use, population density, and distance from roads, hospitals, bus stations, the bank, markets, COVID-19 cases, health care units, and government offices are used. The weighted overlay method was used; to evaluate and map the susceptibility status of the infection risk to COVID-19. The result revealed that out of the total study area, 32.62% (169.91 km2) falls under the low vulnerable category (1), and the area covering 40.9% (213.04 km2) under the moderate vulnerable class (2) for infection risk of COVID-19. The highly vulnerable category (3) covers an area of 25.31% (132.85 km2), and the remaining 1.17% (6.12 km2) is under an extremely high vulnerable class (4). Thus, these priority areas could address pandemic control mechanisms like disinfection regularly. Health sector professionals, local authorities, the scientific community, and the general public will benefit from the study as a tool to better understand pandemic transmission centers and identify areas where more protective measures and response actions are needed at a finer spatial resolution.