This study applies machine learning methods such as Decision Tree (CART) and Random Forest to classify drought intensity based on meteorological data. The goal of the study was to evaluate the effectiveness of these methods for drought classification and their use in water resource management and agriculture. The methodology involved using two machine learning models that analyzed temperature and humidity indicators, as well as wind speed indicators. The models were trained and tested on real meteorological data to assess their accuracy and identify key factors affecting predictions. Results showed that the Random Forest model achieved the highest accuracy of 94.4% when analyzing temperature and humidity indicators, while the Decision Tree (CART) achieved an accuracy of 93.2%. When analyzing wind speed indicators, the models’ accuracies were 91.3% and 93.0%, respectively. Feature importance revealed that atmospheric pressure, temperature at 2 m, and wind speed are key factors influencing drought intensity. One of the study’s limitations was the insufficient amount of data for high drought levels (classes 4 and 5), indicating the need for further data collection. The innovation of this study lies in the integration of various meteorological parameters to build drought classification models, achieving high prediction accuracy. Unlike previous studies, our approach demonstrates that using a wide range of meteorological data can significantly improve drought classification accuracy. Significant findings include the necessity to expand the dataset and integrate additional climatic parameters to improve models and enhance their reliability.

The Cisadane Watershed is in a critical state, which has expanded residential areas upstream of Cisadane. Changes in land use and cover can impact a region’s hydrological characteristics. The Soil and Water Assessment Tool (SWAT) is a hydrological model that can simulate the hydrological characteristics of the watershed affected by land use. This study aims to evaluate the impact of land use change on the hydrological characteristics of the Cisadane watershed using SWAT under different land use scenarios. The models were calibrated and validated, and the results showed satisfactory agreement between observed and simulated streamflow. The main river channel is based on the results of the watershed delineation process, with the watershed boundary consisting of 85 sub-watersheds. The hydrological characteristics showed that the maximum flow rate (Q max) was 12.30 m³/s, and the minimum flow rate (Q min) was 5.50 m³/s. The study area’s distribution of future land use scenarios includes business as usual (BAU), protecting paddy fields (PPF), and protecting forest areas (PFA). The BAU scenario had the worst effect on hydrological responses due to the decreasing forests and paddy fields. The PFA scenario yielded the most favourable hydrological response, achieving a notable reduction from the baseline BAU in surface flow, lateral flow, and groundwater by 2%, 7%, and 2%, respectively. This was attributed to enhanced water infiltration, alongside increases in water yield and evapotranspiration of 3% and 15%, respectively. l Therefore, it is vital to maintain green vegetation and conserve land to support sustainable water availability.

This study conducts a systematic literature review to analyze the integration of artificial intelligence (AI) within business excellence frameworks. An analysis of the findings in the reviewed articles yielded five major themes: AI technologies and intelligent systems; impact of AI on business operations, strategies, and models; AI-driven decision-making in infrastructure and policy contexts; new forms of innovation and competitiveness; and the impact of AI on organizational performance and value creation in infrastructure projects. The findings provide a comprehensive understanding of how AI can be integrated into organizational excellence emerged frameworks to address challenges in infrastructure governance, and sustainable development. Key questions addressed include: how AI affects consumer behavior and marketing strategies. What AI’s capabilities for businesses, especially marketing and digital strategies? How can organizations address the drivers and barriers to help make better use of AI in these business operations? Should organizations even do anything with these insights? These questions and more will be tackled throughout this discussion. This paper attempts to derive a comprehensive conceptual framework from several fields of human resources, operational excellence, and digital transformation, that can help guide organizations and policymakers in embedding AI into infrastructure and development initiatives. This framework will help practitioners navigate the complexities of AI integration, ensuring profitability and sustainable growth in a highly competitive landscape. By bridging the gap between AI technologies and development-related policy initiatives, this research contributes to the advancement of infrastructure governance, public management, and sustainable development.

The role of technology in stimulating economic growth needs to be reexamined considering current heightened economic conditions of Asian developing Economies. This study conducts a comparative analysis of technology proxied by R&D expenditures alongside macroeconomic variables crucial for economic growth. Monthly time-series data from 1990 to 2019 were analyzed using a vector error correction model (VECM), revealing a significant impact of technology on the economic growth of India, Pakistan, and the Philippines. However, in the cases of Indonesia, Malaysia, Thailand, and Bangladesh, macroeconomic indicators were found more crucial to their economic growth. Results of Granger causality underlined the relationship of R&D expenditures and macroeconomic variables with GDP growth rates. Sensitivity analyses endorsed robustness of the results which highlighted the significance and originality of this study in economic growth aligned with sustainable development goals (SDGs) for developing countries.

This study examines the crucial role of digital marketing in promoting sustainable tourism in the villages of Bali. It adopts a mixed methods approach, using qualitative and quantitative data collection and analysis. The qualitative data were obtained from semi-structured interviews with management teams who have experience in implementing digital marketing strategies for village tourism. The interviewees were selected using a purposive sampling technique. The quantitative data were gathered from questionnaires distributed to domestic tourists who visited the villages. The questionnaires measured the tourists’ perceptions of digital marketing as a tool for village tourism marketing. The study found that digital marketing plays a vital role in promoting tourism villages, as most tourists learned about the villages through online media. The study also identified five dimensions of digital marketing, namely website media, social media, search engines, email marketing, and online advertising, which have potential effects on the sustainability of tourism villages. The study conducted statistical tests to examine the effects of 20 indicators of digital marketing on village tourism marketing. The results showed that 16 indicators had a significant positive effect, while four indicators had no effect. These findings suggest that digital marketing is an effective way to market tourism villages and enhance their sustainability.

The primary objective of this paper is to explore the impact of household policies in both Saudi Arabia and Nigeria towards achieving efficient and sustainable economic growth in the 21st century. Fundamentally, the objective of the study was sparked by the basic factors of comparison the importance of culture in international relations, challenges related to terrorism which impede adequate implementations of economic policies, trade facilitation and logistics to enhance economic growth and cross-border movement of goods and services. Systematic literature review (SLR) and content analysis (CA) were used as methodological approaches of the paper. The articles explored for review were accessed using visualization of similarities (VOS) by exploring different database such as: journals, core collection of Web of Science (WOS), peer review sources and library sources. The findings demonstrated that Saudi Arabia and Nigeria have different policies regarding households in achieving sustainable economic growth. On one hand, in Saudi Arabia, the focus is on the economic burden associated with chronic non-communicable diseases (NCDs) and the out-of-pocket spending among individuals diagnosed with these diseases. In addition, the study found that households with older and more educated members, an employed head of household, higher socioeconomic status, health insurance coverage, and urban residency had significantly higher out-of-pocket expenditure in achieving sustainable economic development. On the other hand, Nigeria’s policy is centered around trade liberalization and its impact on household welfare as an integral part of sustainable economic development. The policies implemented in Saudi Arabia and Nigeria have implications for the well-being of their citizens. In Saudi Arabia, the household policies have significantly impacted the quality of life (QoL) of households, particularly those with low income, large size, male-led, urban, and with elderly heads. In Nigeria, trade liberalization policies have mixed welfare implications for households in the aspects of real income, they also induce unemployment in key sectors, such as agriculture and industry. To mitigate negative effects, it is suggested that Saudi Arabia should effectively address chronic non-communicable diseases (NCDs) among the households while Nigeria should efficiently pursue trade liberalization on a sectorial basis, focusing on sectors that do not severely undermine household welfare.