Spatial assessment of drought vulnerability using fuzzy-analytical hierarchical process: a case study at the Indian state of Odisha

Saha, Sunil; Kundu, Barnali; Paul, Goutam; Mukherjee, Kaustuv; Pradhan, Biswajeet; Dikshit, Abhirup; Maulud, Khairul Nizam Abdul; Alamri, Abdullah

doi:10.1080/19475705.2020.1861114

Cited by 39 publications

(15 citation statements)

References 94 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The areas having high water table may have less vulnerability to drought. Overexploitation of groundwater can have a significant impact on drought-related damages (Saha et al 2021). Thus, groundwater table data during pre and post-monsoon seasons was obtained from Central Groundwater Board for preparing spatial groundwater table layers.…”

Section: 𝑌 = 𝑎 + 𝑏 𝑋 + 𝜖mentioning

confidence: 99%

“…Eq. ( 4) was utilized to calculate it: High income and literacy rate can make the society resilient (Saha et al 2021). High rate of IMR reflect the impact of drought (Miyan 2015).…”

Section: 𝑌 = 𝑎 + 𝑏 𝑋 + 𝜖mentioning

confidence: 99%

“…The ROC curve is a threshold-based method for evaluating classified outcome (Saha et al 2021). The final drought vulnerability maps produced using ensemble model were divided into five classes based on equal interval namely, very low, low, moderate, high and very high (Djurovic et.…”

Section: Validation Of Drought Vulnerability Maps Using Roc Curvementioning

confidence: 99%

“…Liu et al (2020) utilized an Integrated agricultural drought index (IDI) based on remote sensing datasets and neural network for examining the agricultural drought in North China Plain. Another study by Saha et al (2021) examined drought vulnerability in Karnataka using GIS based bagging and artificial neural network (ANN) methods. Apart from these models, the new soft computation algorithms mainly ensemble adaptive neuro-fuzzy inference systems (ANFIS) with less or no statistical limitations are becoming popular for vulnerability assessment (Chen et al 2017).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Adaptive neuro fuzzy inference system (ANFIS) machine learning algorithm for assessing environmental and socio-economic vulnerability to drought: a study in Godavari middle sub-basin, India

Masroor

Razavi-Termeh

Rahaman

et al. 2022

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

Climate change has increased the frequency of drought occurrence in various parts of the world. Drought as a complex phenomenon causes severe impacts on ecological and socio-economic status. Short-term and long-term occurrences of drought have made many regions vulnerable globally. This paper makes an attempt to assess drought vulnerability in Godavari Middle Sub-basin of India. Twenty-four site specific socio-economic and environmental factors were identified based on the extensive literature review. Drought frequency was assessed using standardized precipitation index (SPI). These datasets were divided into training (70 %) and testing (30 %) data. Frequency ratio (FR) model was utilized to establish relationship among drought conditioning factors and drought frequency. Weights obtained from the FR model were used as input to the adaptive neuro-fuzzy inference systems (ANFIS) model. Drought vulnerability results were validated using the testing data and receiver operating characteristic (ROC). The accuracy of ANFIS models for one-month (0.957), three-months (0.882), six-months (0.964) and twelve-months (0.938) reflected high suitability of ANFIS model for the assessment of drought vulnerability. The findings revealed that very low normalized difference vegetation index (NDVI) and increasing trend of highest maximum and mean maximum temperature were major environmental factors influencing high drought vulnerability in the sub-basin. High proportion of area under fallow land, high infant mortality rate (IMR) and moderate literacy rate were identified major socio-economic factors making watersheds vulnerable during short and long-term droughts. Largest area of the sub-basin was found under high vulnerability during threemonths, six-months and twelve-months droughts. Thus, the study calls for policy intervention towards lessening the impact of drought in highly vulnerable watersheds.

show abstract

Section: 𝑌 = 𝑎 + 𝑏 𝑋 + 𝜖mentioning

confidence: 99%

“…Eq. ( 4) was utilized to calculate it: High income and literacy rate can make the society resilient (Saha et al 2021). High rate of IMR reflect the impact of drought (Miyan 2015).…”

Section: 𝑌 = 𝑎 + 𝑏 𝑋 + 𝜖mentioning

confidence: 99%

Section: Validation Of Drought Vulnerability Maps Using Roc Curvementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive neuro fuzzy inference system (ANFIS) machine learning algorithm for assessing environmental and socio-economic vulnerability to drought: a study in Godavari middle sub-basin, India

Masroor

Razavi-Termeh

Rahaman

et al. 2022

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

show abstract

“…Recently, Hoque et al (2020; and Saha et al (2021) used analytical hierarchical process (AHP) and Fuzzy-AHP methods to analyse drought vulnerability. Saha et al (2021a) used ANN and Bagging method for assessing the drought vulnerability situation of Karnataka state. In order to model the susceptibility of various hazards, a variety of ensemble machine learning algorithms (MLAs) were utilised, including landslide (Antronico et al 2020), gully erosion (Roy et al 2021), land subsidence (Tien Bui et al 2018), deforestation (Saha et al 2021b) and flood (Nhu et al 2020a, b, c), rather than drought.…”

Section: Introductionmentioning

confidence: 99%

Proposing an ensemble machine learning based drought vulnerability index using M5P, dagging, random sub-space and rotation forest models

Saha

Kundu

Paul

et al. 2023

Stoch Environ Res Risk Assess

Self Cite

View full text Add to dashboard Cite

Drought is one of the major barriers to the socio-economic development of a region. To manage and reduce the impact of drought, drought vulnerability modelling is important. The use of an ensemble machine learning technique i.e. M5P, M5P -Dagging, M5P-Random SubSpace (RSS) and M5P-rotation forest (RTF) to assess the drought vulnerability maps (DVMs) for the state of Odisha in India was proposed for the first time. A total of 248 drought-prone villages (samples) and 53 drought vulnerability indicators (DVIs) under exposure (28), sensitivity (15) and adaptive capacity (10) were used to produce the DVMs. Out of the total samples, 70% were used for training the models and 30% were used for validating the models. Finally, the DVMs were authenticated by the area under curve (AUC) of receiver operating characteristics, precision, mean-absolute-error, root-mean-square-error, K-index and Friedman and Wilcoxon rank test. Nearly 37.9% of the research region exhibited a very high to high vulnerability to drought. All the models had the capability to model the drought vulnerability. As per the Friedman and Wilcoxon rank test, significant differences occurred among the output of the ensemble models. The accuracy of the M5P base classifier improved after ensemble with RSS and RTF meta classifiers but reduced with Dagging. According to the validation statistics, M5P-RFT model achieved the highest accuracy in modelling the drought vulnerability with an AUC of 0.901. The prepared model would help planners and decision-makers to formulate strategies for reducing the damage of drought.

show abstract

A Comprehensive Evaluation of Agricultural Drought Vulnerability Using Fuzzy‐AHP‐Based Composite Index Integrating Sensitivity and Adaptive Capacity

Bera,

Dutta

2024

Hydrological Processes

View full text Add to dashboard Cite

With increasing extreme weather events, ground water crisis and population expansion, crop stress and production failure have emerged as critical challenges. Agricultural drought vulnerability (ADV) at local and regional scales has become a global concern as it is directly related to food security, hunger issues and poverty. The Kangsabati river basin is one of the major drought‐prone river basin in the eastern India and frequently affected by the reduction of crop production or crop failure because of fluctuation of monsoonal rainfalls, poor irrigation system and harsh edaphic factors. In this context, this study focuses on assessing agricultural vulnerability in the Kangsabati basin using multi‐sensor datasets and geospatial techniques. The ADV has been assessed through multi‐source data sets covering meteorological, agricultural, soil and socio‐economic aspects using a powerful, systematic, and flexible decision‐making fuzzy‐based analytic hierarchy process (fuzzy‐AHP) technique. The ADV index is a functional product of two composite indices: the sensitivity index (SI) and the adaptivity index. The SI is derived from components like the intensity of agricultural drought index, groundwater stress, soil erosion, percentage of cultivators, marginal workers and agricultural land. Adaptive capacity depends upon human, financial, physical, infrastructural and natural capital. Each index was derived considering various factors using fuzzy‐AHP methods for weightage calculation. The composite indices revealed the variation of resource distribution precisely in each geographically distinct zone. The study shows that almost 60% of the highly sensitive zone is situated in the upper basin region characterised by undulating lands. A large part of the entire basin (48%) is moderately drought‐sensitive. The result also shows that a significant part (35%) of the upper and middle basin is highly vulnerable to agricultural drought. In contrast, the lower basin exhibits low to very low levels of vulnerability to drought. The results indicate that even though some areas are moderate to less sensitive, the vulnerability of agricultural drought has become high due to their limited adaptive capacity. The comprehensive framework developed for assessing ADV has the potential for region‐specific policy implementation and sustainable growth.

show abstract

Spatial assessment of drought vulnerability using fuzzy-analytical hierarchical process: a case study at the Indian state of Odisha

Cited by 39 publications

References 94 publications

Adaptive neuro fuzzy inference system (ANFIS) machine learning algorithm for assessing environmental and socio-economic vulnerability to drought: a study in Godavari middle sub-basin, India

Adaptive neuro fuzzy inference system (ANFIS) machine learning algorithm for assessing environmental and socio-economic vulnerability to drought: a study in Godavari middle sub-basin, India

Proposing an ensemble machine learning based drought vulnerability index using M5P, dagging, random sub-space and rotation forest models

A Comprehensive Evaluation of Agricultural Drought Vulnerability Using Fuzzy‐AHP‐Based Composite Index Integrating Sensitivity and Adaptive Capacity

Contact Info

Product

Resources

About