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Watershed Prioritisation of Drainage Basins Based on Geomorphometric Parameters, Neyyar Watershed, India
(Sciendo, 2023-06-27) Shekar, Padala Raja; Mathew, Aneesh
Prioritisation of sub-watersheds (SWs) is becoming increasingly important in the conservation of natural resources, particularly in watershed planning. In this study, sub-watershed for the Neyyar basin was prioritised using three methods: morphometric analysis, principal component analysis (PCA) and hypsometric analysis. Morphometric analysis and hypsometric analysis were carried out using remote sensing (RS) and geographic information system (GIS) techniques, while PCA was performed for dimensionality reduction of morphometric parameters. The watershed was divided into 11 sub-watersheds (SW1–SW11), and each sub-watershed was given priority. To rank and prioritise SWs, 15 morphometric parameters were selected from the quantitative measures of morphometric analysis, including linear, relief, and areal. PCA was used to rank and prioritise SWs based on three highly correlated morphometric parameters. The hypsometric integral (HI) values were determined using the elevation relief ratio approach, and HI values were utilised to prioritise SWs. For both methods, such as morphometric analysis and PCA, a higher priority has been given to SW1. Using hypsometric analysis, higher priorities have been assigned to SW1, SW7, SW8, SW9, SW10 and SW11. The most common SWs that belong to the same priority of SWs and have a high correlation between them among the three methods are SW1, SW2, and SW5.The results of this analysis indicate that SW1 is a common high priority area with a significant risk of soil erosion, runoff and peak discharge. Therefore, decision-makers may utilise the high-priority sub-watershed to guide planning and development, measure conservation efforts and manage the land to prevent.
Prioritizing sub‑watersheds for soil erosion using geospatial techniques based on morphometric and hypsometric analysis: a case study of the Indian Wyra River basin
(Springer Nature, 2023-06-26) Shekar, Padala Raja; Mathew, Aneesh; Abdo, Hazem Ghassan; Almohamad, Hussein; Abdullah Al Dughairi, Ahmed; Al-Mutiry, Motrih
The hydrological availability and scarcity of water can be affected by geomorphological processes occurring within a watershed. Hence, it is crucial to perform a quantitative evaluation of the watershed’s geometry to determine the impact of such processes on its hydrology. Geographic information systems (GIS) and remote sensing (RS) techniques have become increasingly significant because they enable decision-makers and strategists to make accurate and efficient decisions. To prioritize sub-watersheds within the Wyra watershed, this research employs two methods: morphometric analysis and hypsometric analysis. The watershed was divided into eleven sub-watersheds (SWs). The prioritization of sub-watersheds in the Wyra watershed involved assessing several morphometric parameters, such as relief, linear, and areal features, for each sub-watershed. Furthermore, the importance of the sub-watersheds was determined by computing hypsometric integral (HI) values using the elevation–relief ratio method. The final prioritization of sub-watersheds based on morphometric analysis was determined through the integration of principal component analysis (PCA) and weighted sum approach (WSA). SW2 and SW9 have had higher priorities using morphometric analysis, whereas SW6, SW7, and SW10 have obtained higher priorities using hypsometric analysis. SW4 is the most common SW that shares the same priority. The most vulnerable sub-watersheds are those with the highest priority, and therefore, programmes for soil and water conservation should pay more attention to them. The conclusions of the study may prove useful to various stakeholders involved in initiatives related to watershed development and management.
Sub-watershed prioritization using morphometric analysis, principal component analysis, hypsometric analysis, land use/land cover analysis, and machine learning approaches in the Peddavagu River Basin, India
(IWA Publishing, 2023-06-05) Shekar, Padala Raja; Mathew, Aneesh; Arun, P. S.; Gopi, Varun P.
Water resource management is critical in the face of climate change to reduce water scarcity and meet the demands of an expanding population. Prioritization of watersheds has gained significance in natural resource management, particularly in the context of watershed management. This study prioritizes sub-watersheds for the Peddavagu basin using five methods. The four methods mentioned above can be estimated utilizing remote sensing (RS) and geographic information system (GIS) approaches, while linear discriminant analysis (LDA) is estimated using machine learning techniques. The catchment resulted in the formation of 13 sub-watersheds. The quantitative measurements of morphometric analysis, including linear, relief, and areal, were considered, and 18 morphometric characteristics were chosen to rank and prioritize sub-watersheds. Principal component analysis (PCA) was used to rank and prioritize sub-watersheds based on four highly correlated morphometric parameters. The land use/land cover (LULC) analysis used four features to prioritize sub-watersheds. The LDA analysis used two features to prioritize sub-watersheds. Using hypsometric integral (HI) values, prioritization has been done. Sub-watersheds were prioritized. Based on five methods, the sub-watersheds were classified as low, medium, and high. Among the sub-watersheds identified as high priority, immediate priority is assigned to SW10. Decision-makers in the research region can use the findings to plan and implement watershed management techniques.
Geo-physical seasonal deviations of land use, terrain analysis, and water cooling effect on the surface temperature of Pune city
(IWA Publishing, 2023-12-12) Sharma, Kul Vaibhav; Kumar, Vijendra; Gautam, Lilesh; Choudhary, Sumit; Mathew, Aneesh
Urban heat islands are hotter than rural places. Sustainable urban growth and improving urban environments need understanding Urban Heat Island (UHI) causes and finding effective mitigation techniques. This research examines the seasonal deviations in surface temperatures for the UHI effect in Pune, India, focusing on land use patterns and water body cooling. Land use categorization included residential, commercial, industrial, vegetation, and open spaces. The research studied the cooling potential and temperature variance by distance from water bodies in the form of lakes, rivers, and ponds. These aquatic bodies have surface and ambient temperature sensors. Roads, soil, commercial areas, residential areas, industrial areas, and vegetation have all shown increases in NDBI, ranging from 15.84 to 36.45%. Urban regions with heat accumulation and dissipation have been revealed by DEM and contour maps. The research found that the water bodies have a cooling effect on LST till the distance of 350 m. The research finds hotter places and shows how natural features mitigate UHI by analyzing land use patterns and water body cooling. The findings emphasize the significance of green areas and water bodies in urban design and development to improve Pune's climate resilience and inhabitability.
GIS-based assessment of soil erosion and sediment yield using the revised universal soil loss equation (RUSLE) model in the Murredu Watershed, Telangana, India
(KeAi Communications Co. Ltd., 2024-05-17) Shekar, Padala Raja; Mathew, Aneesh
The current investigation was conducted in the Murredu watershed, situated in India. The essential datasets, such as the digital elevation model (DEM), soil, land use land cover (LULC), and rainfall parameters, were processed and analysed using a Geographic Information System (GIS) environment. The current research utilised the revised universal soil loss equation (RUSLE) model to assess the mean soil loss in the Murredu watershed. The mean annual soil loss was calculated to be 14.06 t/ha/year, indicating a high soil erosion risk. The RUSLE model results indicated a good outcome with an accuracy of 72.8%. Furthermore, the research area revealed that sub-watersheds (SW) 2 and SW 14 had the maximum and minimum mean annual soil loss, respectively. The sediment delivery ratio (SDR) for the Murredu watershed was determined to be 0.227. The Murredu watershed outlet received a mean annual sediment yield of 3.19 t/ha/year. Through investigation, it was determined that SW 2 had the maximum mean annual sediment yield, while SW 11 had the minimum. This current investigation provides valuable insights for stakeholders, decision-makers, and policymakers regarding sustainable ways of managing watersheds.
Trend Analysis of Aerosol Concentrations over Last Two Decades from MODIS Retrievals over Hyderabad District of India
(AGH University of Science and Technology Press, 2024-01-31) Nandan, A K; Mathew, Aneesh; Shekar, Padala Raja
Air pollution is one of the grave concerns of the modern era, claiming millions of lives and adversely impacting the economy. Aerosols have been observed to play a significant role in negatively influencing climatological variables and human health in given areas. The current study aimed to study the trend of aerosols and particulates on daily, monthly, seasonal, and annual levels using a 20-year (2002–2021) daily mean aerosol optical depth (AOD) product released by moderate resolution imaging spectrometer (MODIS) sensors for the Hyderabad district in India. The results of the daily mean analysis revealed a rising trend in the number of days with severe AOD (>1), whereas examinations of the seasonal and monthly mean data from 2017 through 2022 showed that peak AOD values alternated between the summer, autumn, and winter seasons over the years. Trend analysis using Mann–Kendall, modified Mann–Kendall, and innovative trend analysis (ITA) tests revealed that AOD increased significantly from 2002 through 2021 (p < 0.05; Z > 0). Furthermore, correlation analysis was performed to check for correlations between AOD levels and certain meteorological factors for the Charminar and Secunderabad regions; it was noticed that temperature had a weak positive correlation with AOD (p < 0.05; r = 0.283 [Secunderabad] – p < 0.05; r = 0.301 [Charminar]), whereas relative humidity developed a very weak negative correlation with AOD (p < 0.05; r = −0.079 [Secunderabad] – p < 0.05; r = −0.109 [Charminar]).
Water distribution system modelling of GIS-remote sensing and EPANET for the integrated efficient design
(IWA Publishing, 2024-03-05) Dongare, Pranit; Sharma, Kul Vaibhav; Kumar, Vijendra; Mathew, Aneesh
Urban settlement depends on water distribution networks for clean and safe drinking water. This research incorporates geographic information systems (GIS), remote sensing (RS), and hydraulic modelling software EPANET to analyse and construct water distribution systems in Bota town, India. Satellite images and hydrological data have been utilized for the management of the Bota town’s water supply network, sources to cater the demand for urban centres. EPANET simulates hydraulic behaviour in the water distribution system under different operating situations. EPANET simulation shows network leaks, low pressure, and substantial head loss. These findings have advised for water distribution system improvements by analysing network shortcomings. Booster pumps, new pipelines, and repairing of existing leakages are examples of such improvements. GIS, RS, and EPANET provided a comprehensive water distribution system study and more accurate and efficient improvement identification. This study emphasizes the necessity of new technologies in water distribution system analysis and design. The study solves Bota town’s water distribution system problems of low pressure, high head loss, and leaks utilizing GIS, RS, and EPANET. The findings of this research can help in enhancing the water delivery systems in other towns with comparable issues. © 2024 The Authors.
Sustainable data-driven insights: Statistical analysis and artificial intelligence-driven modelling of aerosol concentrations in Hyderabad district, India
(Elsevier Ltd., 2024-04-29) Nandan, A K; Mathew, Aneesh
Air pollution stands as a pressing issue in contemporary times, leading to the loss of millions of lives and exerting detrimental effects on the economy. The aerosols especially particulate matter, which are dispersions of matter in air medium play an important role in manipulating the climatological variables in an area. The current study was developed in response to the need to study aerosols and particulates on annual levels using 20-year (2002–2021) daily mean Aerosol Optical Depth (AOD) product released by Moderate Resolution Imaging Spectrometer (MODIS) sensors, and to generate prediction models for AOD using artificial intelligence (AI) techniques for Hyderabad district in India. The results of daily mean analysis revealed a rising trend in the number of days with severe AOD (> 1). Yearly mean AOD distribution showed a percentage increase of 45.31 % from 2002 to 2021. Furthermore, factor analysis was carried out to check for correlations of AOD and PM2.5 with various meteorological and pollutant variables. It was observed that both PM2.5 and AOD had significant weak to moderate (p < 0.05; r < 0.5) correlations with both pollutants and meteorological variables. The hybrid deep learning-based CNN-LSTM was identified as the best-fit model to predict AOD, outperforming MLP – ARIMA and MLP models. CNN – LSTM showed an R2 of 0.70, MAE of 0.08, MSE of 0.02 and RMSE of 0.14.
Exploring spatial machine learning techniques for improving land surface temperature prediction
(Elsevier B.V., 2024-05-05) Arunab, K.S.; Mathew, Aneesh
Land Surface Temperature (LST) is a crucial parameter in Earth observation and environmental studies due to its significance in various fields. The purpose of this study is to investigate the effects of including spatial information into the Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) models for forecasting LST. The significance and impact of each input parameter on the models' predictive capabilities are assessed using the SHAP (Shapley Additive exPlanations) approach and the model intercomparisons were done using the error evaluation metrices. The predictions were further validated using the Pearson correlation, independent samples t-test and potential geographic anomalies in the predictions are examined by spatial comparison of predicted errors using classification maps and error envelopes. The projected errors are within the acceptable range and range from −2.267 °C to 1.292 °C for the spatially enhanced RF model and from −1.675 °C to 1.439 °C for the spatially enhanced XGBoost model. These error ranges closely align with the training data's quality flag of ±2 °C, demonstrating the models' capability to predict LST accurately and within a reasonable error range. The findings show the significance of adding spatial information for precise LST prediction and draw attention to possible uses for such models in environmental monitoring and management. The work advances our understanding of spatial modelling strategies and offers practical guidelines for enhancing LST forecasts.
Assessment of soil erosion and sediment yield in the Peddavagu watershed, India, using a revised universal soil loss equation model (RUSLE) and GIS techniques
(IWA Publishing, 2024-05-12) Shekar, Padala Raja; Mathew, Aneesh
The present investigation was carried out within the Peddavagu watershed, which is located in India. The necessary datasets, including soil, land use land cover, rainfall, and digital elevation model, were processed and analysed within a Geographic Information System framework. To evaluate soil loss within the watershed, the present investigation employed the revised universal soil loss equation (RUSLE) model. Subsequently, the sediment yield is estimated based on the sediment delivery ratio (SDR). The average annual soil loss was estimated at 17.91 tonnes/hectare/year, which is high soil erosion risk. The RUSLE model's accuracy is 82.1%. Moreover, the findings revealed that sub-watersheds (SW) 9 and SW 3 exhibited the maximum and minimum average annual soil loss. The Peddavagu watershed's SDR was 0.210. Annually, 3.76 tonnes/hectare/year of sediment were transported to the Peddavagu watershed outlet. The findings revealed that SW 9 and SW 5 exhibited the maximum and minimum average annual sediment yield. The model's performance was evaluated by comparing its predictions with gauge data for validation. The observed actual data indicated a yield of 3.66 tonnes/hectare/year, while the model predicted a yield of 3.76 tonnes/hectare/year. This resource offers significant insights for policymakers and decision-makers on sustainable watershed management techniques.