Analytic Hierarchy Process Technique Research Articles

Flood risk zone mapping and future projections for the Thamirabarani river basin, Southern India: insights from decadal rainfall trends and GIS-based analytical hierarchy process technique

Flood risk zone mapping and future projections for the Thamirabarani river basin, Southern India: insights from decadal rainfall trends and GIS-based analytical hierarchy process technique

Land degradation vulnerability modelling for landscape restoration planning in Eastern DR Congo using the analytical hierarchy process (AHP) and geospatial techniques

Land degradation is a major issue for attainment of sustainable development in Eastern DR Congo. This study aims to assess and model the spatial pattern of land degradation vulnerability (LDV) in this region through the application of the Analytical Hierarchy Process (AHP) and geospatial techniques by using the Kalehe territory as a case study. LDV has been defined as the resultant of three components: the exposure to erosion risk, the sensitivity associated with biophysical factors, and the adaptability associated with socioeconomic factors of vulnerability. These factors were weighted using the EasyAHP plugin and multisource open spatial data to obtain the LDV model. This model was validated using the frequency ratio of physical land degradation features. The LDV map was obtained through the application of the weighted overlay technique in QGIS. The results indicated that about 32% of the territory is prone to high to very high vulnerability, representing the hotspot of LDV. The developed model has an overall accuracy of 77.8% in predicting the area with high to very high LDV. Thus, it can be used during land conservation planning to identify the priority areas for implementation of landscape restoration initiatives at the territorial level in Eastern DR Congo.

Development of a Comprehensive Safety Evaluation Mechanism for the Highway Bus Industry

A suitable mechanism for evaluating the safety of the highway bus industry is crucial for ensuring public transportation safety in a country. In this study, we have combined indices from the Compliance Safety Accountability (CSA) program of the Federal Motor Carrier Safety Administration (FMCSA) in the US, the safety evaluation of renting buses in Japan, and some existing safety evaluation methods in Taiwan to develop a new safety evaluation mechanism for the highway bus industry. This paper introduces a two-stage decision making approach that includes the use of fuzzy analytic hierarchy process (Fuzzy AHP) and machine learning techniques such as decision trees, support vector machines, and random forests to develop this mechanism. The data used in this research were collected from the internet and directly from highway bus transportation companies. We calculated the safety performance of each company and assigned them different safety ranks. Compared with the causes of accidents in Taiwan, the results of this study showed that work hours, driver fitness, and administrative penalties are the three most important sub-attributes that affect the safety rank of the company.

The effects of global product design on supply chain efficiency and natural resources management

The significance of global product design as a primary factor in determining supply chain efficiency and natural resource management. This study examines the impact of global product design changes on process and supply chain practices. Specifically, it seeks to understand in promoting sustainable practices across global supply chains. This study, utilizes the fuzzy Analytical Hierarchy Process (AHP) and fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods to evaluate the criteria, sub-criteria, and various significant opportunities influencing sustainable product design and supply chain management. The fuzzy AHP method is used to assess the key criteria and their respective sub-criteria. The fuzzy TOPSIS method is used to rank the opportunities in driving sustainable practices across global supply chains. The results of fuzzy AHP show that innovation and product differentiation (C4), economic viability (C1), and supply chain efficiency (C5) are the crucial criteria. While the results of fuzzy TOPSIS present that supply chain transparency (O2) is the most suitable opportunity, followed by circular economy practices (O1) and policy advocacy and compliance (O6) in adopting global product design on supply chain efficiency and natural resources management. Thus, this study is highly relevant to the governments, industry specialists, and policymakers who aim at enhancing the dynamic business environment and sustainable development.

Geo-spatial based cyclone shelter suitability assessment using analytical hierarchy process (AHP) in the coastal region of Bangladesh

The coastal district of Bangladesh is susceptible to cyclones, which cause significant damage to infrastructure, economy, and social structures every year. The importance of protecting lives and property in these vulnerable areas is a top priority, especially in times of cyclones and storm surges. Therefore, the identification of potentially suitable shelter locations is essential for disaster risk resilience planning and implementation in the coastal regions. In this context, our research focuses on Barguna, which has witnessed severe damage from previous cyclones over the last several decades. We aim to identify and map feasible cyclone shelter suitability by utilizing GIS, AHP, Hotspot Analysis, and Remote Sensing techniques. The use of advanced techniques enables a comprehensive assessment of multiple variables that influence shelter suitability, ensuring the selection of the most strategically significant locations. The goal is to enhance disaster preparedness and resilience in Barguna District, reducing the risk and vulnerability of its coastal communities to cyclones. Seven variables associated with cyclone hazards, such as elevation, slope, distance from roads and rivers, population density, land use, and cover, and proximity to healthcare facilities, are considered to identify the safest and most suitable cyclone shelter areas. The assessment of the appropriateness of shelter locations for the study area was facilitated by the collection of 181 GPS locations regarding existing cyclone shelters. The findings reveal that approximately 15.53 % (1956 ha) of the total land area is considered less suitable, 67.31 % (84763 ha) moderately suitable, 16.70 % (21024 ha) suitable, and 0.29 % (362 ha) highly suitable for optimal cyclone shelter establishment in the study area. This study has a major limitation due to the use of Landsat imagery, which may not always provide fully accurate image classification. To validate our methods, we collected existing cyclone shelter data and performed a pixel-based accuracy assessment, achieving high accuracy and confirming the reliability of our approach. This research addresses a critical gap in cyclone shelter planning, offering valuable insights for residents and decision-makers to mitigate cyclone risks not only in the Barguna district but also in similar coastal regions of Bangladesh. The framework developed in this study can aid non-government organizations and the government in determining the most appropriate locations to construct cyclone shelters to build a safer, more resilient future for the region, capable of withstanding the relentless threats posed by cyclones every year.

Assessing critical flood-prone districts and optimal shelter zones in the Brahmaputra Valley: Strategies for effective flood risk management

Frequent flooding has become a persistent issue in floodplain regions, causing significant disasters during each rainy season due to insufficient disaster management planning. This study proposes a methodology to prioritize flood susceptibility areas at the district level and identify suitable sites for flood shelters using a combination of machine learning algorithms and multi-criteria analysis, supported by geospatial technology. Flood shelter suitability mapping was conducted using the Analytical Hierarchy Process (AHP), while flood susceptibility zones were assessed using four different machine learning models: Support Vector Machine (SVM), Random Forest, Decision Tree, and Naive Bayes. The integration of machine learning models with the AHP technique is vital in situations where conventional numerical models face challenges due to limited data, such as river discharge and water levels. The methodology includes a multicollinearity assessment to ensure the independence of selected flood-causing factors, information gain ratio to identify the most influential factors, Spearman's rho test to verify correlations between the machine learning models, and ROC-AUC along with statistical regression for validating the accuracy of the flood susceptibility maps. The findings indicate that the SVM algorithm, given its strong performance and effective training datasets, is recommended for areas with similar physical characteristics. The district-wise priority map generated from the weighted results of flood susceptibility assessments will be useful for flood management and mitigation strategies. Additionally, the study found that applying the AHP technique to determine flood shelter suitability, after assessing flood-prone areas, enhanced the efficiency of the flood management process. This research offers valuable insights for authorities to better address flooding and improve flood prevention and management efforts in floodplain regions, contributing to broader climate change adaptation strategies.

Optimizing marine vehicles industry: a hybrid analytical hierarchy process and additive ratio assessment approach for evaluating and selecting IoT-based marine vehicles

Rapid developments in the Internet of Things (IoT) have opened the door for game-changing applications in numerous sectors, especially the vehicle industry. There is a rising demand for efficient assessment and decision-making methodologies to pinpoint the most promising choices for the vehicle sector with the introduction of IoT-based maritime vehicles. To overcome this issue, the integrated multi-criteria decision-making analysis (MCDA) paradigm proposed in this research combines the additive ratio assessment (ARAS) and analytic hierarchy process (AHP) approaches to evaluate and choose IoT-based maritime vehicles based on their performance- and authenticity-related criteria in the vehicle sector. The selection issue is hierarchically organized, and the assessment criteria are prioritized using the AHP approach. There are seven performance and authentication related criteria are selected that might aid in the selection procedure. Using the AHP, we are assigned these criteria proportionate weights that reflect their respective significance and interrelationships. AHP, however, falls short of offering a thorough analysis of the alternatives that exist. To overcome these restrictions, this research presents the integration of AHP with the ARAS approach for the ranking of alternatives according to how well they perform against the set criteria. By using the ARAS technique, it is possible to get over the restrictions of AHP and achieve a more thorough assessment of maritime IoT-based vehicles. The efficiency of the framework is proven using empirical data and professional judgment. The findings show that the hybrid method successfully encapsulates the intricate relationships between the factors being evaluated and objectively appraises the potential of IoT-based maritime vehicles for the automotive sector. This study extends to the area by providing an organized and thorough method for assessing and choosing IoT-based maritime vehicles. Considering several factors and their mutual dependence, the hybrid AHP and ARAS technique gives decision-makers a powerful tool for evaluating the potential of IoT-based maritime vehicles in the automotive sector. Smart decisions on the deployment of IoT-based marine vehicles and maximizing the potential they present may be made by beneficiaries in the automotive sector using the study’s results.

Assessment of soil erosion risk and vulnerability in the transboundary Sio-Malaba-Malakisi watershed in Kenya and Uganda

Persistent soil erosion poses a significant threat to water quality, ecosystem viability and soil health in many regions of the world. Addressing this challenge requires a comprehensive understanding of local soil erosion rates, including the identification of vulnerable areas, to facilitate effective and integrated environmental management. In East Africa, however, many affected regions are data poor and lack measured hydrological data from which soil erosion estimates can be derived. This study used the physically based Revised Universal Soil Loss Equation (RUSLE) model to estimate the annual rate of soil erosion in the transboundary Sio-Malaba-Malakisi catchment, straddling Kenya and Uganda. Soil erosion events were quantified by integrating input factors derived from physical datasets using a Geographical Information System (GIS). The Analytical Hierarchy Process (AHP) technique was then used to assess the importance of selected physical factors influencing the soil erosion process in order to identify regions of increasing environmental vulnerability. The results obtained indicate a wide range of soil erosion rates across the region, with an estimated mean annual rate of 250 t ha−1 yr−1 for the whole catchment. Upstream areas characterized by intensive agricultural activity had erosion rates of up to 2000 t ha−1 yr−1, while downstream areas recorded erosion rates below 600 t ha−1 yr−1. Areas with intensive agriculture, unprotected soils, and a soil loss tolerance value above 12 t ha−1 yr−1 were found to be most vulnerable. Rainfall and vegetation cover were identified as key factors influencing erosion susceptibility. Regions with soil erosion rates above 50 t ha−1 yr−1 were identified as high priority regions for targeted soil and water conservation measures. The identification of vulnerability zones, including the classification of their severity, provides an opportunity to prioritize sustainable environmental management interventions; a process that can be replicated for such affected regions elsewhere.

Flood vulnerability map of the Bagmati River basin, Nepal: a comparative approach of the analytical hierarchy process and frequency ratio model

AbstractThe analytical hierarchy process (AHP) and frequency ratio model (FR), along with the integration of GIS, have proven to be successful approaches for assessing flood-prone areas. However, in Nepal flood vulnerability mapping based on GIS decision analysis is limited. Thus, this study focused on comparing the data-driven FR method and expert knowledge-based AHP technique in a GIS environment to prepare a flood vulnerability map for the Bagmati River basin, helping to explore the gap in flood vulnerability mapping methodologies and approaches. By combining all class-weighted contributing factors, like elevation, precipitation, flow accumulation, drainage density, soil, distance from the river, land use land cover, normalized difference vegetative index, slope and topographic wetness index, the study evaluated the efficiency of FR and AHP in assessing flood vulnerability maps. An inventory map of floods containing 107 flood points was created. Subsequently, the flood vulnerability maps generated using FR and AHP models revealed that 9.30% and 11.36% of regions were in highly vulnerable areas, respectively. Receiver operating characteristics validated the model outcomes, indicating that the FR model’s accuracy of 91% outperformed the AHP model’s 84% accuracy. The study findings will assist decision-makers in enacting sustainable management techniques to reduce future damage in the Bagmati basin.

Evaluating the effectiveness of the TOPSIS approach for three-wire electrode machining of D2 steel using the wire EDM method

Due to the high demand for D2 steel as a tool material and the difficulty of the machine, optimization of process parameters in advanced manufacturing machines is needed. This study investigates three wire electrodes: brass, coated copper, and annealed copper, analyzing their impact on tool material. Employing 0.25 mm wires, 10 mm D2 steel cubes are cut for consistent comparison. An L27 orthogonal array tests six parameters at three levels, optimizing with analytic hierarchy process technique for order preference by similarity to the ideal solution (AHP-TOPSIS). The response parameters were the metal removal rate (MRR) and kerf width (KW). Pulse on/off time, wire tension, spark voltage, input current, and wire feed rate vary systematically for each wire. The tests validate the efficacy of the AHP-TOPSIS method in optimizing WEDM parameters and machining performance. ANOVA reveals pulse-on and pulse-off times as crucial factors for various wire electrodes. Under diverse conditions, pulse duration increases spark efficiency. Based on the AHP-TOPSIS method results, weights of outputs revealed that the annealed copper wire yields the highest MRR value (0.232 mm3/sec). The brass wire exhibited the lowest MRR value (0.127 mm3/sec) compared to the others.

Flood Susceptibility Mapping Using Analytic Hierarchy Process and Geographic Information System in Baitarani Basin of Odisha, India

Baitarani, the 6th largest river basin in Odisha, eastern India, faces water related challenges in its catchments. As one of the most frequent environmental disasters, floods are consistently cited in hotspots of the eastern region in India. Hence, in this study, flood susceptibility maps were generated for Baitarani River basin by using analytical hierarchy process technique and geographic information system. Eleven thematic maps of topographic ruggedness index, topographic wetness index, stream power index, elevation, lithology, soil, slope, drainage density, distance from drainage network, land use/land cover and normalized difference vegetation index were prepared for flood vulnerability mapping. The themes were assigned suitable weights depending on relative influence of each of the themes on flood risk. The results indicated that 87% of the basin falls under intermediate flood susceptibility zone, while 10% is classified under high flood hazard zone. Areas in the lower elevation catchment, particularly those with flat topography near the coastline, were identified as being highly susceptible to flooding. The impacts of severe flooding in the basin are most pronounced in specific blocks in Jajpur district (Jajpur, Dasarathpur, and Korei blocks), Bhadrak district (Dhamnagar and Bhandaripokhari blocks), and Anandapur block in the Keonjhar district of Odisha. Additionally, changes in land use/land cover during 1995-2020 revealed 8% decline in agricultural land and 13.93% increase in fallow land in the area. The outcomes of this research can help researchers, planners and managers in managing natural resources, find locations that could be vulnerable to flooding, and lessen the damage.

Analytical Hierarchy process and geospatial techniques for Delineation of Groundwater potential zones in Bundelkhand Craton Region, India

Analytical Hierarchy process and geospatial techniques for Delineation of Groundwater potential zones in Bundelkhand Craton Region, India

Harnessing the sustainable potential of groundwater in Saudi Arabia via remote sensing

Groundwater resources in water-stressed regions, such as the Middle East, face significant challenges due to low precipitation, high temperatures, and elevated evaporation rates, which are compounded by anthropogenic stresses. In this context, Saudi Aramco (the national petroleum and natural gas company of Saudi Arabia) has contributed in the search for potential groundwater sources to ensure an adequate supply of potable water for the country’s citizens, agricultural needs, and industrial usage. In this study, we combined geographic information system (GIS), remote sensing (RS) and analytic hierarchy process (AHP) techniques to evaluate the vulnerability of groundwater potential (taking Hail as a case study). With a total of 7 thematic layers (i.e., geology, slope, lineament density, land use/land cover, annual rainfall, drainage density, and soil texture), we found that groundwater potential zones with moderate to high coverage account for 91.9%, which is notably close to the borehole data, with a groundwater potential validation accuracy of 90.0%. The obtained results offer a crucial tool for decision-makers and managers, enabling them to develop timely protection and monitoring measures for groundwater resources.

Prioritisation of Specific Wheat Sowing Technologies Based on AnalyticHierarchy Process and Farmers’ Perceptions

Selection of an appropriate wheat-sowing technology in the combine harvested paddy fields is essential for the timely sowing of wheat after harvesting paddy. The importance of parameters that affect farmers' decisions in selection of wheat sowing technology (WST) in the combine harvested paddy fields is assessed in the present work by implementing an analytical hierarchy process (AHP) technique. AHP is the most comprehensive approach for making judgements with many criteria since it supports a hierarchical structure and quantitative and qualitative features. The suitability of four most commonly used wheat sowing technologies, namely, happy seeder (T1 ), super seeder (T2 ), smart seeder (T3 ) and roto seeder (T4 ), was assessed based on the importance given to six criteria, namely, ease of use (C1 ), machine capital cost (C2 ), fuel consumption (C3 ), machine field capacity (C4 ), crop yield (C5 ), and crop germination (C6 ). Based on farmers’ perceptions and using the analytical hierarchy process approach, criteria C5 was assigned a maximum criteria priority weights of 3.21 and 3.87 for selection of T1 and T2 , respectively; while criteria C6 was assigned a maximum criteria priority weights of 3.56 and 3.63 for selection of T3 and T4 , respectively. The results showed that farmers were less concerned with machine field capacity and more focused on crop output (yield) while selecting a wheat sowing technology. Besides crop yield, crop germination and ease of use were other important factors that farmers considered highly while prioritising and selecting a wheat sowing technology for a combine harvested paddy field.

BALANCED SCORECARD DESIGN AS A MODEL FOR MEASUREMENT OF PUSKESMAS PERFORMANCE IN EAST LOMBOK DISTRICT

The objective of this study is to develop a performance measuring model based on the balanced scorecard framework at the UPT Puskesmas Suela in East Lombok Regency, using the Analytic Hierarchy Process (AHP) technique. This research employed qualitative approaches. Data were gathered with a paired comparison questionnaire employing the Analytic Hierarchy Process (AHP) scale. The performance evaluation of community health centers is conducted using four reference standards, including the financial perspective, community perspective, internal business process perspective, and development and learning perspective. Utilizing this technique for data processing is beneficial in making judgments based on many factors. The research findings indicate that the primary criterion of utmost importance for community health centers is finance, with a weight of 0.465450. The subsequent prioritization criterion is society, assigned a weight of 0.214555. Additionally, both the internal business process criteria and development and learning criteria hold equal significance, each with a weight of 0.112321. Related indicates that Puskesmas as a public sector agency focuses on improving performance on financial perspectives.

Implementasi Metode AHP dalam Penentuan Penerimaan Bantuan Langsung Tunai (BLT) Berbasis Web

One of the government initiatives of the Sobawawi sub-district is Direct Cash Assistance, or BLT, which aims to lessen the financial strain on the population. Not everyone is eligible for direct cash assistance because it is meant to help low-income families fulfill their basic necessities. The government offers cash support to those who are considered impoverished through the Direct Cash support (BLT) program. This program is intended to help those who struggle to manage their financial situation. In order to help resolve and minimize errors when assessing who was eligible to receive BLT (Direct Cash Assistance) in the Sobawawi district, a decision support system was developed in response to the issues that arose throughout the selection process. The goal of this system is to support decision-making processes that employ the Analytical Hierarchy Process (AHP) technique. The results of both system and manual computations demonstrate how the decision-making system uses the AHP method's notion to determine. Based on the method employed, the system delivers findings that are legitimate and accurate. Ultimately, alternate priority weights based on ranking are determined.

Delineation of potential zones for groundwater recharge using integrated GIS-based AHP and CSI techniques in Ogun Waterside, Southwestern Nigeria

ABSTRACT Ogun Waterside region of southwestern Nigeria experiences seasonal water stress stemming from groundwater overdraft. Therefore, it became imperative to effectively manage the existing groundwater resources to fulfill the anticipated demand for potable water. To achieve this goal, an analysis involving seven spatial parameters: geomorphology, land use/land cover, slope, lineament density, drainage density, soil types, and geology was conducted. These parameters were examined using a combination of geographic information system (GIS)-based analytical hierarchy process (AHP) and cumulative score index (CSI) techniques to delineate potential zones for groundwater recharge. The AHP methodology was employed to assign relative weights to the thematic layers and their corresponding features, reflecting their importance. The AHP results were integrated using a weighted linear combination within a GIS environment to generate the groundwater recharge potential map. Further, the CSI was used to reclassify the composite groundwater recharge potential map. The resultant map revealed three zones which were classified into high, moderate, and low potential zones founded on the CSI approximation. About 98.56 km2 (8.28%) of the study area is categorized as having low potential, 803.77 km2 (67.54%) falls within the moderate potential zone and 287.71 km2 (24.18%) falls in the high potential zone.

Flood hazard assessment using machine learning and hydrodynamic modeling: case study in the Vu Ga–Thu Bon basin in Vietnam

ABSTRACT The objective of this study was the construction of a theoretical framework to evaluate flood hazards by integrating machine learning and hydrodynamic modeling in Vietnam's Vu Gia–Thu Bon basin. MIKE FLOOD was used to simulate historical floods in 2017 in order to obtain flood depth and velocity. Support vector machine-stochastic gradient descent (SVM-SGD), decision trees (DT), and dagging (DA) were used to determine flood susceptibility. Flood hazard was constructed by combining the flood depth, velocity, and susceptibility using the analytic hierarchy process technique. The statistical indices AUC, RMSE, MAE, and NASH were used to evaluate the precision of the hydrodynamic and machine learning models. The results showed that hydrodynamic modeling was highly accurate, with a NASH value of 0.86. The proposed models achieved AUC values of 0.98 for SVM-SGD, 0.93 for DT, and 0.92 for DA. The results showed that 7.59% of the flood zones is located in the very low flood hazard zone, 108.2 km2 in the low flood hazard zone, 24.59% in the moderate flood hazard zone, 22.53% in the high flood hazard zone, and 10.01% in the very high flood hazard zone.

Effective factors on Migration to Kerman City and Immigrants’ Problems Using Analytical Hierarchy Process Model and Technique for Order of Preference by Similarity to Ideal Solution

Introduction: Marginal immigrants cause many problems in economic, social and cultural, physical ,and environmental aspects of cities. This study aims to investigate the factors affecting migration to Kerman city and the problems of marginalized immigrants using the analytical hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS) models. Methods: This cross-sectional study was conducted in 2022, with a sample size of 147 households selected from Kerman suburban region. The data were collected by a standardized questionnaire measuring effective factors on the migration and the checklist of the immigrant problems. Results: The most important immigration factors were economic, social, geographical, political, educational and religious-cultural factors, respectively. Among 37 components studied, transferring the job of the family man as the family head, being on the main land, air and rail ways, having a special position at the international level, burying the family’s dead members in the desired city, and the education of the wife or one of the children of the family were the most important components. According to the results, more than 90% did not have access to public baths, sports facilities, government centers, private institutions, and parks, and recreational spaces. Conclusion: According to AHP model, economic factors ,and then social factors, were the most important reasons for migrating to the suburbs of Kerman. Therefore, it is suggested to create stable and long-term job opportunities , improve employment and business environment in the cities, and develop infrastructure to control migration to the city capitals.

Agricultural land suitability of Manimutha Nadhi watershed using AHP and GIS techniques

A precise land suitability assessment plays a vital role in modern agriculture to sustain production against population explosions worldwide without affecting natural resources or environmental factors. Further, recent urbanization has led to a reduction in arable land and forced the conversion of non-arable land to arable land. Therefore, developing land suitability with its potential and limitations is essential through advanced techniques like multi-criteria Decision Analysis for the particular area. The current research aimed to assess the land suitability of the Manimutha Nadhi watershed, Tamil Nadu, India, using geographical information system (GIS) and Analytic Hierarchy Process (AHP) methods. This study area, Manimutha Nadhi, covers a total area of 263.89 km2. Using a random sampling technique, 57 soil samples were obtained. The parameters for the AHP study were soil pH, EC, Soil organic carbon (SOC), available NPK, soil texture, and soil erosion. The climatic parameters, Annaula average rainfall (AAR) and Annual average temperature (AAT) and topographic parameter slope of the study area are also used as the criteria for generating final agricultural land suitability. The final suitability map through GIS and AHP was created using weighted overlay analysis. The land suitability class on the basis of FAO classification is highly suitable (S1), moderately suitable (S2), marginally suitable (S3), currently not suitable (N1), and permanently not suitable (N2) for agriculture. The results of the final land suitability map by AHP and GIS revealed that 150.58 km2 (57.09%) of the research region is highly suitable for agriculture (S1). The 29.60 km2 (10.08%) area was moderately suitable (S2) for irrigated agriculture. The 53.37 km2 (20.20%) area was marginally suitable (S3) for irrigated agriculture. The 10.56 km2 (7.53%) area was currently unsuitable (N1) for agricultural uses. The area of about 13.45 km2 (5.10%) was permanently unsuitable (N2) for agriculture. The outcomes can be used as baseline information on the land that could strengthen agriculture production in the Manimutha Nadhi watershed.