High disease burden and healthcare resource usage in patients with acute porphyria-A population-based analysis.

BACKGROUND AND AIMS: Acute porphyria is a chronic recurrent disease with late diagnosis, heterogeneous clinical presentations and potentially devastating complications. The study aimed at providing real-world evidence on the natural course of acute porphyria, patient characteristics, disease burden, and healthcare utilization before diagnosis. METHODS: This observational study used anonymized claims data covering 8 365 867 persons from German statutory health insurance, spanning 6 years (2015-2020). Patients with at least one diagnosis of acute porphyria during the index period (2019-2020) were classified into three groups by attack frequency. These findings were compared with two age- and sex-adjusted reference groups: the general population and fibromyalgia patients. Prevalence over the index period was calculated for all porphyria patients and those with active acute porphyria. RESULTS: We revealed a prevalence of 79.8 per 1 000 000 for acute porphyria, with 12.9 per 1 000 000 being active cases. Acute porphyria patients, particularly with frequent attacks, demonstrated a higher comorbidity burden compared to the general population. Within the year before the recorded diagnosis, patients with acute porphyria required a median of 23.0 physician visits, significantly higher than the general population's 16.0. Additionally, 33.8% were hospitalized at least once during this period, a notably higher proportion than the general population (19.3%). CONCLUSIONS: This study's findings, collected before the introduction of givosiran, as the first approved preventive therapy for acute porphyria in Europe, highlight the need for healthcare strategies and policies tailored to the complex needs of acute porphyria patients. The significant healthcare demands, heightened comorbidity burden, and increased healthcare system utilization emphasize the urgency of developing a comprehensive support infrastructure for these patients. Also, these acute porphyria real-world findings provide additional insights on disease characteristics in Germany.

Coastal Flood risk assessment using ensemble multi-criteria decision-making with machine learning approaches.

Coastal areas are at a higher risk of flooding, and novel changes in the climate are induced to raise the sea level. Flood acceleration and frequency have increased recently because of unplanned infrastructural conveniences and anthropogenic activities. Therefore, the assessment of flood susceptibility mapping is considered the most significant flood management model. In this paper, flood susceptibility identification is performed by applying the innovative Multi-criteria decision-making model (MCDM) called Analytical Hierarchy Process (AHP) by ensembles with Support vector machine (AHP-SVM) and Decision Tree (AHP-DT). This model combines two Representation concentration pathway (RCP) scenarios such as RCP 2.6 & RCP 8.5. The factors influencing the coastal flooding in Bandar Abbas, Iran, identified through Flood susceptibility mapping. Multi-criteria decision-making (MCDM) has been applied to evaluate the Coastal flood conditioning factors, and ensemble machine learning (ML) approaches are employed for Coastal risk factor (CRF) prediction and classification. The statistical variances are measured through Friedman and Wilcoxon signed rank tests and statistical metrics such as Accuracy, sensitivity, and specificity. Among the models, AHP-DT obtained an improved AUC value of ROC as 0.95. After applying the ML models, the northern and western park of Raidak Basin River recognises very low and low flood susceptibility because of their topographic characteristics. The eastern part of the middle section fell very high and high CFSM. Observed from this result analysis, the people living nearer to the coastline are distributed by the low to medium exposure in the region of the west and middle of the considered study area. The results of this study can help decision-makers take necessary risk reduction approaches in the high-risk flooding zones of the coastal system.

Selection of data analytic techniques by using fuzzy AHP TOPSIS from a healthcare perspective.

The healthcare industry has been put to test the need to manage enormous amounts of data provided by various sources, which are renowned for providing enormous quantities of heterogeneous information. The data are collected and analyzed with different Data Analytic (DA) and machine learning algorithm approaches. Researchers, scientists, and industrialists must manage or select the best approach associated with DA in healthcare. This scientific study is based on decision analysis between the DA factors and alternatives. The information affects the whole system in a rational manner. This information is very important in healthcare sector for appropriate prediction and analysis. The evaluation discusses its benefits and presents an analytic framework. The Fuzzy Analytic Hierarchy Process (Fuzzy AHP) approach is used to address the weight of the factors. The Fuzzy Techniques for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) address the rank of the data analytic alternatives used in healthcare sector. The models used in the article briefly discuss the challenges of DA and approaches to address those challenges. The assorted factors of DA are capture, cleaning, storage, security, stewardship, reporting, visualization, updating, sharing, and querying. The DA alternatives include descriptive, diagnostic, predictive, prescriptive, discovery, regression, cohort and inferential analyses. The most influential factors of the DA and the most suitable approach for the DA are evaluated. The 'cleaning' factor has the highest weight, and 'updating' is achieved at least by the Fuzzy-AHP approach. The regression approach of data analysis had the highest rank, and the diagnostic analysis had the lowest rank. Decision analyses are necessary for data scientists and medical providers to predict diseases appropriately in the healthcare domain. This analysis also revealed the cost benefits to hospitals.

Micro-spatial flood risk assessment in Nagaon district, Assam (India) using GIS-based multi-criteria decision analysis (MCDA) and analytical hierarchy process (AHP).

Nagaon is one of the highly flood-prone districts of Assam, India that recurrently experiences devastating floods resulting in the loss of lives and property and wreaking havoc on the district's socioeconomic infrastructure. Identification and mapping of spatial patterns of flood hazards, flood vulnerability, and flood risk zones (FRZs) of the district are, therefore, crucial for flood management and mitigation. The present study, therefore, attempts to delineate the FRZs of more than 930 villages in the Nagaon district by integrating the flood hazard and vulnerability layers in the geospatial environment using the multi-criteria decision analysis and analytical hierarchy process techniques. Here, seven flood hazard and vulnerability indicators are considered to derive each layer separately. The results indicate that about 15.14% of the district's total villages are in the very high FRZ, 27.93% in the high, 46.62% in the moderate, and 10.3% in the low FRZ. Further, bivariate correlation analysis is used to evaluate the results with the percentages of the population, cropland, and animals affected by floods at different temporal scales in order to ensure that the revenue circles with a higher percentage of area under high and very high FRZs genuinely have higher percentages of flood-affected cropland, people, and livestock. The significance of this research is evident in its pragmatic findings that could aid the stakeholders in managing and reducing flood risk at micro-spatial scales.

Constructing an evaluation index system for clinical nursing practice teaching quality using a Delphi method and analytic hierarchy process-based approach.

BACKGROUND: The key step in evaluating the quality of clinical nursing practice education lies in establishing a scientific, objective, and feasible index system. Current assessments of clinical teaching typically measure hospital learning environments, classroom teaching, teaching competency, or the internship quality of nursing students. As a result, clinical evaluations are often insufficient to provide focused feedback, guide faculty development, or identify specific areas for clinical teachers to implement change and improvement. Therefore, the purpose of our study was to to construct a scientific, systematic, and clinically applicable evaluation index system of clinical nursing practice teaching quality and determine each indicator's weight to provide references for the scientific and objective evaluation of clinical nursing practice teaching quality. METHODS: Based on the "Structure-Process-Outcome" theoretical model, a literature review and Delphi surveys were conducted to establish the evaluation index system of clinical nursing practice teaching quality. Analytic Hierarchy Process (AHP) was employed to determine the weight of each indicator. RESULTS: The effective response rate for the two rounds of expert surveys was 100%. The expert authority coefficients were 0.961 and 0.975, respectively. The coefficient of variation for the indicators at each level ranged from 0 to 0.25 and 0 to 0.21, and the Kendall harmony coefficients were 0.209 and 0.135, respectively, with statistically significant differences (P < 0.001). The final established index system included 3 first-level, 10 second-level, and 29 third-level indicators. According to the weights computed by the AHP, first-level indicators were ranked as "Process quality" (39.81%), "Structure quality" (36.67%), and "Outcome quality" (23.52%). Among the secondary indicators, experts paid the most attention to "Teaching staff" (23.68%), "Implementation of teaching rules and regulations (14.14%), and "Teaching plans" (13.20%). The top three third-level indicators were "Level of teaching staff" (12.62%), "Structure of teaching staff" (11.06%), and "Implementation of the management system for teaching objects" (7.54%). CONCLUSION: The constructed evaluation index system of clinical nursing practice teaching quality is scientific and reliable, with reasonable weight. The managers' focus has shifted from outcome-oriented to process-oriented approaches, and more focus on teaching team construction, teaching regulations implementation, and teaching design is needed to improve clinical teaching quality.

Allied health professional support for children and young adults living in and leaving care: A systematic scoping review.

BACKGROUND: Children and young people living in and leaving care are known to have experienced significant childhood adversity and trauma resulting in potentially deleterious impact on their health and well-being across the life course. Studies point to the complex needs of this population who may benefit from allied health professional (AHP)-related support with limited studies located. This review sought to address this gap by systematically scoping empirical literature focused on the provision of AHP support to this cohort of children and young adults to assist an understanding of the service needs for this vulnerable population. METHODS: This scoping review followed Arskey and O'Malley's five steps framework (2005) to identify and review relevant literature. A focus on identifying the evidence, challenges and gaps in research relating to AHP support for children and young people living in and leaving care was initially agreed, followed by a systematic search using a combination of three key concepts to identify relevant studies in five AHP disciplinary areas to identify best evidence in the past decade (2011-2021). Study inclusion criteria were based on empirical studies of children and young people living in care (0-17 years) and leaving care (18-25 years). A data extraction table was formulated as a means of charting the data, aligned with the scope and objectives of this review. Finally, data were subsequently collated, synthesised and reported based on key thematic areas emerging from included studies regarding AHP support to children and young people living in and leaving care. RESULTS: A total of 13 studies met the review inclusion criteria. Included studies reported specifically on speech and language therapist (SLT; n = 5), occupational therapist (OT; n = 3) and arts-based therapies (n = 5). No studies were identified with regard to the use of physiotherapy and dietetics with this population. Results indicated that children and young people living in and leaving care have high rates of speech, language, communication and sensory needs. More rigorous screening, assessment and early intervention were identified as essential for this vulnerable group. Increased multidisciplinary collaboration and OT support for young adults in preparation for transition to independent living was identified as an urgent requirement. Included studies indicate promising results in relation to access to arts-based therapies with particular reference to identity formation for children and young people living in and leaving care. CONCLUSIONS: Although evidence of effectiveness remains limited, AHP service provision (specifically speech and language therapy, occupational therapy and arts-based therapies) has the potential to contribute positively to addressing the complex and interacting needs of this vulnerable population. As a result, it is recommended that AHP service provision is integrated into the collaborative, multidisciplinary care available to children living in and leaving care. More extensive, higher quality research related to the benefits of AHP provision for this population of children and young people is essential to provide a more robust evidence base across the various professional disciplines that constitute allied health provision.

Optimization of the Zhou Tian Formula extraction technology based on AHP-CRITIC method and analysis of transfer efficiency of key components based on HPLC fingerprinting.

INTRODUCTION: Zhou Tian Formula (ZTF) is an antidepressant traditional Chinese medicine utilized widely in clinical settings for the treatment of patients with depression. However, shortcomings persist in its extraction technology and quality control. OBJECTIVE: This study aimed to propose a methodology for ZTF extraction technology based on the analytic hierarchy process (AHP)-criteria importance through intercriteria correlation (CRITIC) method and to establish a quality control framework for the efficient transfer of index components. METHOD: Firstly, we analyzed the chemical components of ZTF and determined the optimal extraction technology. Secondly, we calculated the transfer efficiency of the index components during the conversion of water decoction to extract powder and subsequently to granules. Thirdly, we established HPLC fingerprints for 15 batches of ZTF water decoction, extract powder, and granules. We employed SIMCA software to analyze the chemicals responsible for variations in quality among different batches of ZTF granules. RESULTS: We determined the optimal extraction process. The average transfer efficiency of ferulic acid, puerarin, mirificin, isoferulic acid, and calycosin during the conversion of water decoction to extract powder and subsequently to granules exceeded 41%. The HPLC fingerprints of ZTF exhibited a similarity exceeding 0.890. Variable importance in projection values indicated that calycosin, ferulic acid, and puerarin were the primary contributors to quality variations. CONCLUSIONS: The AHP-CRITIC method, coupled with an orthogonal array design, could be used for exploring extraction technology. In addition, the rules governing the transfer of index components from water decoction to extract powder, and subsequently to granules, could be applied for the evaluation and quality assessment of ZTF.

Defining desired genetic gains for Pacific white shrimp (Litopenaeus vannamei) breeding objective using participatory approaches.

The objective of this study was to define desired genetic gains from economically important traits of Pacific white shrimp (Litopenaeus vannamei) using participatory approaches. Two questionnaires were sent out to 100 Pacific white shrimp farmers in all five Iranian shrimp farming provinces. Questionnaire A (Q-A) includes management factors and farming environments. Moreover, in this questionnaire, farmers were asked to rank the fourth most important traits in shrimp among 10 economic traits in the list for genetic improvement. In questionnaire B (Q-B), priorities of the four traits with the highest value were obtained using pairwise comparison. The results showed that the four most important traits were white spot syndrome virus resistance (WSSV), growth rate before 4 months (GR), acute hepatopancreatic necrosis disease resistance (AHPND), and female total weight at ablation (FTW). Medians of the best individual preference values were WSSV (0.222), GR (0.173), AHPND (0.157), and FTW (0.053). Most disagreements were found between the social group preference values in the commercial products and water salinity categories. Desired genetic gains were 1.71%, 1.57%, 0.53% and 0.31% for GR, AHPND, WSSV and FTW, respectively. This study highlighted that despite environmental and management differences, participatory approaches can achieve desired genetic results for Pacific white shrimp breeding programme.

Innovating Household Food Waste Management: A User-Centric Approach with AHP-TRIZ Integration.

Food waste management remains a paramount issue in the field of social innovation. While government-led public recycling measures are important, the untapped role of residents in food waste management at the household level also demands attention. This study aims to propose the design of a smart system that leverages sensors, mobile terminals, and cloud data services to facilitate food waste reduction. Unlike conventional solutions that rely on mechanical and biological technologies, the proposed system adopts a user-centric approach. By integrating the analytical hierarchy process and the theory of inventive problem solving, this study delves into users' actual needs and explores intelligent solutions that are alternatives to traditional approaches to address conflicts in the problem solving phase. The study identifies five main criteria for user demands and highlights user-preferred subcriteria. It determines two physical conflicts and two technical conflicts and explores corresponding information and communications technology (ICT)-related solutions. The tangible outcomes encompass a semi-automated recycling product, a mobile application, and a data centre, which are all designed to help residents navigate the challenges regarding food waste resource utilisation. This study provides an approach that considers users' genuine demands, empowering them to actively engage in and become practitioners of household food waste reduction. The findings serve as valuable references for similar smart home management systems, providing insights to guide future developments.

Multi-Objective Task-Aware Offloading and Scheduling Framework for Internet of Things Logistics.

IoT-based smart transportation monitors vehicles, cargo, and driver statuses for safe movement. Due to the limited computational capabilities of the sensors, the IoT devices require powerful remote servers to execute their tasks, and this phenomenon is called task offloading. Researchers have developed efficient task offloading and scheduling mechanisms for IoT devices to reduce energy consumption and response time. However, most research has not considered fault-tolerance-based job allocation for IoT logistics trucks, task and data-aware scheduling, priority-based task offloading, or multiple-parameter-based fog node selection. To overcome the limitations, we proposed a Multi-Objective Task-Aware Offloading and Scheduling Framework for IoT Logistics (MT-OSF). The proposed model prioritizes the tasks into delay-sensitive and computation-intensive tasks using a priority-based offloader and forwards the two lists to the Task-Aware Scheduler (TAS) for further processing on fog and cloud nodes. The Task-Aware Scheduler (TAS) uses a multi-criterion decision-making process, i.e., the analytical hierarchy process (AHP), to calculate the fog nodes' priority for task allocation and scheduling. The AHP decides the fog nodes' priority based on node energy, bandwidth, RAM, and MIPS power. Similarly, the TAS also calculates the shortest distance between the IoT-enabled vehicle and the fog node to which the IoT tasks are assigned for execution. A task-aware scheduler schedules delay-sensitive tasks on nearby fog nodes while allocating computation-intensive tasks to cloud data centers using the FCFS algorithm. Fault-tolerant manager is used to check task failure; if any task fails, the proposed system re-executes the tasks, and if any fog node fails, the proposed system allocates the tasks to another fog node to reduce the task failure ratio. The proposed model is simulated in iFogSim2 and demonstrates a 7% reduction in response time, 16% reduction in energy consumption, and 22% reduction in task failure ratio in comparison to Ant Colony Optimization and Round Robin.

Exploring climate-smart forestry in Mediterranean forests through an innovative composite climate-smart index.

In recent years, Climate-Smart Forestry (CSF) has emerged as an innovative approach to sustainable forest management, aiming to enhance forest resilience and to balance the provision of ecosystem services facing climate-related threats. This study introduces for the first time a new composite climate-smart index (ICSF) to assess CSF. The methodological approach comprises the following steps: (i) the selection and evaluation of CSF indicators; (ii) the weighting of these indicators; and (iii) the assessment of CSF for Mediterranean forests in two distinct periods, specifically 2005 and 2015. Eight indicators were selected from a systematic literature review. The Analytic Hierarchy Process was applied to translate the preferences obtained through an online questionnaire from a network of CSF-expert stakeholders into weights, at both indicators and criteria levels (i.e., adaptation, mitigation, and the social dimension). Results reveals that indicators "tree species composition", "forest damage", and "regeneration" are of crucial importance for CSF assessment. The comparison of the CSF value between the years 2005 and 2015, shows a slight increase in CSF ratings. The ICSF serves as a comprehensive index of CSF covering all aspects of that concept, i.e. adaptation, mitigation, and the social dimension (including production). The national-scale analysis provides an overview of the dynamics that involve forest management of Mediterranean forests against climate change. The study offers a practicable method for CSF evaluation with its allover set of indicators, representing a suitable tool for supporting forest managers to mitigate the negative impacts of climate change.

Assessment of site suitability for centralized photovoltaic power stations in Northwest China's six provinces.

Northwest China has abundant solar energy resources and extensive land, making it a pivotal site for solar energy development. However, restrictions on site selection and severe weather conditions have hindered the establishment and operation of photovoltaic (PV) power stations. Previous studies have not considered meteorological factors when evaluating site suitability, leading to research gaps in identifying suitable areas and establishing indicator systems. We aimed to address these gaps by considering seven factors constraining the construction of centralized PV power stations (CPPS) and developing an indicator system based on terrain, climate, soil, and economic factors. Furthermore, we conducted analyses to quantify the solar energy generation potential (SEGP), carbon emissions reduction benefits, and land utilization potential at different sites. The findings indicate that areas rated as very suitable and extremely suitable comprised the largest proportion (62.35%) of site suitability. The correlation between site suitability and electricity consumption was largely non-significant, highlighting the need for enhanced coordination. Additionally, we forecast the electricity consumption in Xinjiang, Gansu, Inner Mongolia, Qinghai, Ningxia, and Shaanxi for 2030 to be 56.62, 19.86, 54.54, 13.59, 15.96, and 33.34 ( × 1011 kWh), respectively, with corresponding carbon emissions reduction potentials of 20.2, 7.1, 19.4, 4.8, 5.7, and 11.9 ( × 109 kg). Consequently, PV carbon reduction and land utilization potential are substantial.

Analyzing the effectiveness of environmental impact assessment in Paraná, Brazil and California, United States with fuzzy-set qualitative comparative analysis and the analytical hierarchy process.

Environmental impact assessment (EIA) has become one of the most widespread environmental management instruments. Despite this, EIA is routinely criticized for being ineffective at impacting decision-making. This study compared the EIA systems of Paraná, Brazil and California, United States using the effectiveness dimensions from the EIA literature. This study formats the cases into contextual conditions using the fuzzy-set qualitative comparative analysis (fsQCA) to identify the necessary or sufficient conditions that cause effective outcomes. These effectiveness outcomes are then ranked by EIA stakeholders via the analytical hierarchy process (AHP) to identify stakeholder priorities and to improve stakeholder management. The results show that in Paraná stakeholders identified normative effectiveness as the most important dimension, while stakeholders in California identified this dimension as the second-most important following substantive effectiveness. Public participation was found to be a necessary condition for both substantive and normative effectiveness to occur. Early project definition was found to be sufficient for substantive effectiveness and necessary for normative effectiveness, for which stakeholder coordination was a sufficient condition. This suggests that in order for EIA to influence decision-making and foster sustainable development, greater care needs to be taken to actively engage stakeholders in public participation, with clear roles and project design communicated early on, and a clear role for regulatory authority to promote stakeholder coordination for acceptable outcomes. These findings suggest that some effectiveness dimensions are caused by similar conditions, which could help focus stakeholder management efforts and point to new avenues for future EIA effectiveness research.

Comparative evaluation of different pre-treatment alternatives for granular media filters treating greywater and their ranking using analytical hierarchy process.

In this study, the performance of four different pre-treatment alternatives for granular media filtration, namely, settling, aeration, coarse media filtration and chemical coagulation were compared experimentally. Further, analytical hierarchy process (AHP) was used to compare their performance based on economic, environmental, technical and performance criteria. Performance of settling and aeration were evaluated up to 24 h duration. The coarse media filter was intermittently operated with 10 L of greywater in downflow mode while alum was used for chemical coagulation. Experimental results showed that settling up to 6 h did not show significant removal of different pollutants whereas 24 h settling resulted in moderate removal of turbidity and organic content but was not efficient in the removal of nutrients and faecal coliforms. Chemical coagulation reduced 93, 66, 48 and 97% of turbidity, COD, NH4-N and faecal coliforms, respectively from greywater but resulted in excessive sludge generation and is difficult to adopt on-site and requires skilled supervision. Coarse filtration of greywater resulted in 61, 41, 36 and 35% removal of turbidity, COD, PO4-P and faecal coliforms, respectively. Considering different criteria AHP gave coarse filtration as the best pre-treatment option to the granular media filters treating greywater.

Investigation of port intervention to coastal and nearshore dynamics: a GIS and AHP approach.

The goal of the study was attempted to understand the impact of selected ports on the coastal and nearshore dynamics. Dwelling activities along the coastal zone were particularly high in the last two decades. Hence, ports constructed between 2000 and 2022 such as Mundra, Hazira, Karaikal, Krishnapatnam, Gangavaram, and Gopalpur were considered for the study. The land and shore dynamics were assessed prior and after their construction. The landward impact was identified by the changes in LULC, and the dynamics of coastal sediments were assessed from numerical model before and after the ports' construction. The highly impacted land and shore features were used in analytical hierarchy process (AHP) to assess the extent of their impact due to port construction. The extent of impacts was indicated as "criteria weight (CW)" expressed as percentage. Village-wise local sensitivity analysis (LSA) of the port environs due to port activities was also assessed with six parameters such as changes in LULC, cyclone, population, road network, cultural heritage site and shore stability based on their influence on the coast on case-by-case basis using AHP. The obtained influence of the impact of these parameters was used to determine the category of local sensitivity of the adjacent villages around the ports. For effective management planning, the LSA was classified into five classes, namely, very low, low, moderate, high, and very high. Villages categorized with different sensitivities can be attempted for suitable management plans and similar studies can be attempted for all villages impacted by port interventions.

Spatial assessment of flood vulnerability and waterlogging extent in agricultural lands using RS-GIS and AHP technique-a case study of Patan district Gujarat, India.

Assessing and mapping flood risks are fundamental tools that significantly contribute to the enhancement of flood management strategies. Identifying areas that are susceptible to floods and devising strategies to reduce the risk of waterlogging is of utmost importance. In the present study, an integrated approach, combining advanced remote sensing technologies, Geographic Information Systems (GIS), and analytic hierarchy process (AHP), was adopted in the Patan district of Gujarat, India, with a coastline spanning over 1600 km, to evaluate the numerous variables that contribute to the risk of flooding and waterlogging. After evaluating the flood conditioning factors and their respective weights using the analytic hierarchy process (AHP), the results were processed in GIS to accurately delineate areas that are prone to flooding. The results highlighted exceptional precision in identifying vulnerable areas, allowing for a thorough evaluation of the impact severity. The integrated approach yields valuable insights for multi-criteria assessments. The findings indicate that a significant portion of the district's land, precisely 8.94%, was susceptible to very high- risk of flooding, while 27.76% were classified as high-risk areas. Notably, 35.17% of the region was identified as having a moderate level of risk. Additionally, 20.96% and 7.15% were categorized as low-risk and very low-risk areas, respectively. Overall, the study highlights the need for proactive measures to mitigate the impact of floods on vulnerable communities. The research findings were verified by conducting ground truth and visual assessments using microwave satellite imagery (Sentinel-1). The aim of this validation was to test the accuracy of the study in identifying waterlogged agricultural areas and their extent based on AHP analysis. The ground verification and analysis of satellite images confirmed that the model accurately identified approximately 74% of the area categorized under high and very high flood vulnerability to be waterlogged and flooded. This research can provide valuable assistance to policymakers and authorities responsible for flood management by gathering necessary information about floods, including their intensity and the regions that are most susceptible to their impact. Additionally, it is crucial to implement corrective measures to improve soil drainage in vulnerable areas during heavy rainfall events. Prioritizing the adoption of sustainable agricultural practices and improving land use are also crucial for environmental conservation.

Multi-criteria GIS-based land suitability analysis for rice cultivation: a case study in Guilan Province, Iran.

Ensuring food security and sustainable resource management has become a paramount global concern, prompting significant attention to land suitability analysis for enhancing agricultural production. In this study, an AHP-weighted overlay method was employed to delineate rice cultivation suitability in Guilan province, Iran, a central hub for rice production. Sixteen climatic, topographic, and soil variables were integrated, and individual maps were reclassified to align with the specific requisites for rice production. The results revealed three suitability classes: including 'very suitable,' 'suitable,' and 'moderately suitable', covering 91%, 6%, and 3% of the land, respectively. Soil attributes, particularly organic matter, significantly influenced suitability (weight value of 0.745), with topographic and soil factors outweighing climate in assessment. While salinity is generally absent, organic matter deficiency affects 44% of the land. Phosphorus imbalances are prevalent, with potassium toxicity observed in 10%. Microelement deficiencies, especially in iron and zinc, are noted. Additionally, the results indicated that topographic and soil attributes played a more significant role than climate-related factors in assessing land suitability for rice cultivation within the study area. This research provides a comprehensive spatial analysis of all variables in the study region, shedding light on the complexities of land suitability for rice cultivation. These findings contribute to the understanding of agricultural sustainability and resource management strategies in the context of food security.

Geospatial insights into Alphonso mango cultivation: a comprehensive land suitability study in the coastal belt of Maharashtra, India.

Land suitability assessment is integral to the advancement of precision agriculture. This inquiry is focused on identifying optimal regions for cultivating Alphonso mango in the coastal belt of Maharashtra, spanning across Palghar, Raigad, Thane, Ratnagiri, and Sindhudurg districts. Employing a GIS-based Analytic Hierarchy Process (AHP) methodology, 10 crucial parameters have been considered, encompassing climatic, physical, and chemical soil characteristics: cation exchange capacity, organic carbon, slope, rainfall, soil pH, soil texture, mean annual soil temperature, base saturation, soil drainage, and soil depth. Weights are assigned to these parameters based on expert opinions and existing literature to determine their significance in developing a soil suitability map. The study reveals distinct land suitability zones for Alphonso mango cultivation. The land suitability map designates 25.78% of the study area as highly suitable, while 9.18% is considered unsuitable for Alphonso mango cultivation. To validate the study, the Receiver Operating Characteristic (ROC) curve has been employed, indicating an 83% approval rate for the reliability and performance of the soil suitability. The results categorise soil suitability classes, providing valuable insights for farmers and agricultural planners to make informed decisions regarding Alphonso mango cultivation in similar geoenvironmental regions.

Land capability assessment of Sali watershed for agricultural suitability using a multi-criteria-based decision-making approach.

The assessment of land's agricultural potential, through a land capability evaluation, delves into its innate limitations, crop suitability, and responses to soil management. In regions where agriculture reigns supreme, socio-economic development is inexorably linked to the agricultural sector, making the optimal utilization of land resources an imperative pursuit. The pursuit of this objective is underpinned by the selection of new agricultural areas and the determination of which crops thrive in specific locations, for which the multi-criteria decision-making (MCDM) method emerges as an ideal choice. This comprehensive research endeavour revolves around the intricate interplay of climatic, edaphic, fertility, topographical, and socioeconomic determinants. Within this intricate web, a total of 15 determinants play a pivotal role, including precipitation, potential evapotranspiration (PET), soil texture, drainage, soil organic-carbon, nitrogen content, pH, clay content, river proximity, land use/land cover (LULC), slope, temperature, social suitability, irrigation density, and elevation. To weigh these determinants, the Analytical Hierarchy Process (AHP) comes into play, ultimately revealing that the dominant influences on land capability stem from the realms of climate and soil. The watershed's terrain analysis revealed a distinct suitability contrast: 168 km2 highly suitable, 181.3 km2 moderate, and 429 km2 low. The eastern and northeastern sectors were notably promising. Rigorous validation, using the ROC curve, confirmed the reliability and precision. The process yielded an impressive 83.2% AUC, unequivocally confirming the assessment's remarkable accuracy and dependability.

Assessment of vulnerability to flood risk in the Padma River Basin using hydro-morphometric modeling and flood susceptibility mapping.

An evaluation of flood vulnerability is needed to identify flood risk locations and determine mitigation methods. This research introduces an integrated method combining hydro-morphometric modeling and flood susceptibility mapping to assess Padma River Basin's flood risk. Flood zoning, flooding classes, and resource flood risk were explicitly analyzed in this river basin study. Flood risk was calculated using GIS-based hydro-morphometric modeling. Using Horton's and Strahler's methods, drainage density, stream density, and stream order of the Padma River Basin were determined. The Padma River Basin has five sub-basins: A, B, C, D, and E, with stream densities of 0.53 km-2, 0.13 km-2, 0.25 km-2, 0.30 km-2, and 0.28 km-2 and drainage densities of 0.63 km-1, 0.16 km-1, 0.29 km-1, 0.35 km-1, and 0.33 km-1, respectively. Sub-basin A is the most prone to floods due to its high stream and drainage density, whereas B and C are the least susceptible. This study used elevation, TWI, slope, precipitation, NDVI, distance from road, drainage density, distance from river, LU/LC, and soil type to create a flood vulnerability map incorporating GIS and AHP with pair-wise comparison matrix (PCM). The study's flood zoning shows that the northeastern part of this basin is more likely to flood than the southwestern part due to its elevation and high-order streams. Moderate River Flooding, the region's most hazardous flood class, covers 48.19% of the flooding area, including 1078.30 km2 of agricultural land, 94.86 km2 of bare soil, 486.39 km2 of settlements, 586.42 km2 of vegetation cover, and 39.34 km2 of water bodies. The developed hydro-morphometric model, the flood susceptibility map, and the analysis of this data may be utilized to offer long-term advance alarm insight into areas potentially to be invaded by a flood catastrophe, boosting hazard mitigation and planning.