Forest fires are among the most major causes of global ecosystem degradation. The integration of spatial information from various sources using statistical analyses in the GIS environment is an original tool in managing the spread of forest fires, which is one of the most significant natural hazards in the western region of Syria. Moreover, the western region of Syria is characterized by a significant lack of data to assess forest fire susceptibility as one of the most significant consequences of the current war. This study aimed to conduct a performance comparison of frequency ratio (FR) and analytic hierarchy process (AHP) techniques in delineating the spatial distribution of forest fire susceptibility in the Al-Draikich region, located in the western region of Syria. An inventory map of historical forest fire events was produced by spatially digitizing 32 fire incidents during the summers of 2019, 2020, and 2021. The forest fire events were divided into a training dataset with 70% (22 events) and a test dataset with 30% (10 events). Subsequently, FR and AHP techniques were used to associate the training data set with the 13 driving factors: slope, aspect, curvature, elevation, Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Topographic Wetness Index (TWI), rainfall, temperature, wind speed, TWI, and distance to settlements, rivers and roads. The accuracy of the maps resulting from the modeling process was checked using the validation dataset and receiver operating characteristics (ROC) curves with the area under the curve (AUC). The FR method with AUC = 0.864 achieved the highest value compared to the AHP method with AUC = 0.838. The outcomes of this assessment provide constructive spatial insights for adopting forest management strategies in the study area, especially in light of the consequences of the current war.

Renewable energy sources are the most necessitated natural energy to reduce fossil fuels globally. Fossil fuel is the most valuable and limited resource on the planet, but on the other hand, renewable energy creates less pollution. Solar energy is the most effective renewable resource for daily use. Solar power plants are necessary for domestic and daily use. Remote sensing and geographic information technology (GIS) were used for this study to delineate the possible site selection of solar power plants in Kolkata and the surrounding area in West Bengal, India. The analytical hierarchy process (AHP) and the multi-criteria decision-making process (MCDA) were used for each weight calculation and ArcGIS v10.8 was applied for weighted overlay analysis (WOA) for delineation of the result. The site suitability map was developed using a pairwise comparison matrix and the weights were calculated for each criterion. The suitability map was divided into five categories, from not suitable to very highly suitable. A total of 474.21 km² (10.69%) of the area was classified as very highly suitable whereas 249.54 km² (5.62%) area was classified as not suitable because of the water area and east Kolkata wetland. A total of 1438.15 km² (32.43%) of the area was classified as highly suitable for a solar power plant. The Kolkata megacity and water body locations were identified as moderate to not suitable sites. Very high and high-potential sites were identified 2 to 5 km from the central business district (CBD) location, which is Dharmotala. Renewable energy source is needed in the megacity of Kolkata. If solar power plants are contracted then the demand for fossil fuel will be reduced one day, and that will help the environment as well as the society in terms of sustainable development. This study result is helpful for administrators, urban planners, developers, and other stakeholders for the implementation and development of a new solar power plant in the study area.

Global fossil fuel reserves are declining due to differential uses, especially for power generation. Everybody can help to do their bit for the environment by using solar energy. Geographically, Bangladesh is a potential zone for harnessing solar energy. In March 2021, the renewable generation capacity in Bangladesh amounted to 722.592 MW, including 67.6% from solar, 31.84% from hydro, and 0.55% from other energy sources, including wind, biogas, and biomass, where 488.662 MW of power originated from over 6 million installed solar power systems. Concurrently, over 42% of rural people still suffer from a lack of electricity, where solar energy can play a vital role. This paper highlights the present status of various forms of solar energy progress in Bangladesh, such as solar parks, solar rooftops, solar irrigation, solar charging stations, solar home systems, solar-powered telecoms, solar street lights, and solar drinking water, which can be viable alternative sources of energy. This review will help decision-makers and investors realize Bangladesh’s up-to-date solar energy scenario and plan better for the development of a sustainable society.

Assessing water quality is crucial for improving global water resource management, particularly in arid regions. This study aims to assess and monitor the status of groundwater quality based on hydrochemical parameters and by using artificial intelligence (AI) approaches. The irrigation water quality index (IWQI) is predicted by using support vector machine (SVM) and k-nearest neighbors (KNN) classifiers in Matlab’s classification learner toolbox. The classifiers are fed with the following hydrochemical input parameters: sodium adsorption ratio (SAR), electrical conductivity (EC), bicarbonate level (HCO₃), chloride concentration (Cl), and sodium concentration (Na). The proposed methods were used to assess the quality of groundwater extracted from the desertic region of Adrar in Algeria. The collected groundwater samples showed that 9.64% of samples were of very good quality, 12.05% were of good quality, 21.08% were satisfactory, and 57.23% were considered unsuitable for irrigation. The IWQI prediction accuracies of the classifiers with the standardized, normalized, and raw data were 100%, 100%, and 90%, respectively. The cubic SVM with the normalized data develops the highest prediction accuracy for training and testing samples (94.2% and 100%, respectively). The findings of this work showed that the multiple regression model and machine learning could effectively assess water quality in desert zones for sustainable water management.

Plant diversity is lower in arid regions around the world. However, semi-arid regions have very high species richness and are notable in terms of vegetation structure and plant diversity. The major goal of this study was to assess the composition and diversity of woody species with respect to edaphic properties in semi-arid lowlands of Kasur, Punjab Pakistan. Comprehensive field surveys were conducted to study the botanical diversity of woody vegetation in Kasur district (Punjab) during 2020–2022. Based on geography and vegetation richness, 120 sampling sites were selected, with each site comprising 05 transects of 500 m² randomly. Therefore, from each sampling site, 25 quadrats were taken for exploring the biological assortment of woody vegetation. Soil samples were collected at a depth of 9 to 12 cm and placed in a polythene bag. The soil samples were tested for soil pH, EC, OM, macronutrients (N, P, and K), and cation exchange capacity (CEC). A total of 86 woody species belonging to 61 genera and 26 families were documented from diverse habitats of semi-arid lowland of Kasur, Punjab, Pakistan. The leading family was Leguminosae having 13 genera and 21 species, followed by Moraceae, including four genera and nine species, and Bignoniaceae with seven genera and 09 species. Overall, five woody plant communities were renamed by means of the maximum indictor-valued plant species, such as those listed below; 1. EDM: Eucalyptus-Dalbergia-Morus community; 2. PBM: Populus-Bombax-Morus community; 3. ZTZ: Ziziphus-Tamarix- Ziziphus community; 4. PAP: Prosopis-Acacia-Prosopis community, and 5. BCR: Broussonetia-Conocarpus-Ricinus community. The CCA ordination makes it apparent that appropriate edaphic aspects, such as pH, organic matter, N, P, K, soil texture (silt, sand, and clay), and cation exchange capacity (CEC), have a significant influence on how woody species are distributed. The current effort provides a fundamental layout to realize the influences of environmental variables on the arrangement, variety, and relations of woody vegetation, which is useful to improve the conservation and management events for the ecological restoration of degraded habitation in the studied district.

Hydrological reaction to climate change anticipates water cycle alterations. To ensure long-term water availability and accessibility, it is essential to develop sustainable water management strategies and better hydrological models that can simulate peak flow. These efforts will aid in water resource planning, management, and climate change mitigation. This study develops and compares Sacramento, Australian Water Balance Model (AWBM), TANK, and SIMHYD conceptual models to simulate daily streamflow at Rajegaon station of the Pranhita subbasin in the Godavari basin of India. The study uses daily Indian Meteorological Department (IMD) gridded rainfall and temperature datasets. For 1987–2019, 70% of the models were calibrated and 30% validated. Pearson correlation (CC), Nash Sutcliffe efficiency (NSE), Root mean square error (RMSE), and coefficient of determination (CD) between the observed and simulated streamflow to evaluate model efficacy. The best conceptual (Sacramento) model selected to forecast future streamflow for the SSP126, SSP245, SSP370, and SSP585 scenarios for the near (2021–2040), middle (2041–2070), and far future (2071–2100) using EC-Earth3 data was resampled and bias-corrected using distribution mapping. In the far future, the SSP585 scenario had the most significant relative rainfall change (55.02%) and absolute rise in the annual mean temperature (3.29 °C). In the middle and far future, the 95th percentile of monthly streamflow in the wettest July is anticipated to rise 40.09% to 127.06% and 73.90% to 215.13%. SSP370 and SSP585 scenarios predicted the largest streamflow increases in all three time periods. In the near, middle, and far future, the SSP585 scenario projects yearly relative streamflow changes of 72.49%, 93.80%, and 150.76%. Overall, the findings emphasize the importance of considering the potential impacts of future scenarios on water resources to develop effective and sustainable water management practices.