Evaluating seismic risk by MCDM and machine learning for the eastern coast of India.

Natural disasters such as earthquakes endanger human lives and infrastructure, particularly in urban areas. With the advancements in science and technology in understanding natural hazards, recent studies have attempted to mitigate them by mapping the risks using geospatial technology. In this paper, we attempt to integrate the multi-criteria decision-making (MCDM) models, namely the Analytical Hierarchy Process (AHP) and the Criteria Importance Through Inter-criteria Correlation (CRITIC), besides using the artificial neural network (ANN) to assess the seismic risk in the eastern coast of India. The AHP-CRITIC technique is used to evaluate the earthquake coping capacity and vulnerability and has been further used to generate a training base for earthquake probability mapping by ANN. The earthquake probability and spatial intensity information are used to develop the hazard map. Following that, integrating vulnerability, hazard and coping capacity spatial information assessed earthquake risk. Our results indicate that approximately 5% of the study area is at high risk, whilst more than 11% of the population is at high risk due to seismic induced hazards. The area under the curve of the receiver operating characteristic curve is 0.85, which indicates reliable results. The results of this study may help various agencies involved in planning, development and disaster mitigation to develop seismic hazard mitigation methods by better understanding their impacts on the eastern coastal region of India.

Impact of anthropogenic and land use pattern change on spatio-temporal variations of groundwater quality in Odisha, India.

The groundwater physicochemical parameters were studied to understand their spatiotemporal variations and groundwater quality, using the statistical and entropy weights methods. Out of ten parameters that were considered, F-, TDS, and Cl- appear to be the major contributors influencing the quality of groundwater. The Principal Component Analysis (PC1, PC2) indicates that the majority of ions are derived from both natural and anthropogenic sources. Studies of saturation indices of gypsum, Halite, dolomite, and calcite indicate that dissolution of these salts also affects groundwater salinization. In coastal areas, a few of the water samples also appear to be contaminated by the mixing of seawater. The entropy weights, which are free from subjective biases were used to estimate the water quality index. The entropy-based water quality index (EWQI) varies from excellent to good quality for the year 2012-13, and appears to degrade after 2015 onwards. For the year 2018-19 and 2021-22, 71.42% and 68.42% of the study areas show excellent water quality, followed by 25.33% and 24.33% (good), 2.54% and 3.54% (average), 0.7% and 3.7% study area shows as poor quality respectively. The groundwater quality, particularly in the western, central, northern, and eastern parts of the region, appears to be average, poor, and very poor in several small patches, respectively. Industrial developments, mining activities, irrigation, changing land use patterns, agricultural activities, and increased anthropogenic activities may be contributing to the degradation of the water quality.

Estimating suitability of groundwater for drinking and irrigation, in Odisha (India) by statistical and WQI methods.

Twelve major hydro-geochemical parameters derived from about 1134 water samples were studied to understand spatial variation of groundwater quality in the coastal state of Odisha. Multivariate statistical analysis techniques, i.e., cluster analysis and principal component analysis (PCA), and varimax rotation were used to classify various types of groundwater, and plausible sources that control the quality of water in the region. The concentration of major ions varies in the order of Na+ > Ca2+ > Mg2+ > K+ and HCO3- > Cl- > SO42- > F-. Out of the three clusters identified, the 2nd cluster is having more mineralization and relatively poor quality of groundwater as compared to the first and the third cluster. Furthermore, estimates of the Water Quality Index (WQI) indicate that the groundwater in the area can be classified from excellent to medium quality. Furthermore, the sodium absorption ratio (SAR) and Kelly's ratio (KR) suggest that about 70% of groundwater samples are of low to medium salinity, whereas about 30% show higher salinity. The Wilcox diagram reveals that almost 90% of the groundwater is suitable for irrigation. The mining activity appears less likely to be affecting the quality of subsurface water. Water-rock interactions and evaporation-crystallization may be the two dominant factors that appear to control the groundwater away from the coastal areas. Results of this study may be useful to identify the suitable sites for groundwater extraction for drinking and irrigation purposes, besides being useful to the policy-makers in formulating effective plans for preventing further contamination of groundwater aquifers.