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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.
Unveiling urban air quality dynamics during COVID-19: a Sentinel-5P TROPOMI hotspot analysis
(Nature portfolio, 2024-09-16) Mathew, Aneesh; Shekar, Padala Raja; Nair, Abhilash T; Mallick, Javed; Rathod, Chetan; Bindajam, Ahmed Ali; Alharbi, Maged Muteb; Abdo, Hazem Ghassan
In India, the spatial coverage of air pollution data is not homogeneous due to the regionally restricted number of monitoring stations. In a such situation, utilising satellite data might greatly influence choices aimed at enhancing the environment. It is essential to estimate significant air contaminants, comprehend their health impacts, and anticipate air quality to safeguard public health from dangerous pollutants. The current study intends to investigate the spatial and temporal heterogeneity of important air pollutants, such as sulphur dioxide, nitrogen dioxide, carbon monoxide, and ozone, utilising Sentinel-5P TROPOMI satellite images. A comprehensive spatiotemporal analysis of air quality was conducted for the entire country with a special focus on five metro cities from 2019 to 2022, encompassing the pre-COVID-19, during-COVID-19, and current scenarios. Seasonal research revealed that air pollutant concentrations are highest in the winter, followed by the summer and monsoon, with the exception of ozone. Ozone had the greatest concentrations throughout the summer season. The analysis has revealed that NO2 hotspots are predominantly located in megacities, while SO2 hotspots are associated with industrial clusters. Delhi exhibits high levels of NO2 pollution, while Kolkata is highly affected by SO2 pollution compared to other major cities. Notably, there was an 11% increase in SO2 concentrations in Kolkata and a 20% increase in NO2 concentrations in Delhi from 2019 to 2022. The COVID-19 lockdown saw significant drops in NO2 concentrations in 2020; specifically, − 20% in Mumbai, − 18% in Delhi, − 14% in Kolkata, − 12% in Chennai, and − 15% in Hyderabad. This study provides valuable insights into the seasonal, monthly, and yearly behaviour of pollutants and offers a novel approach for hotspot analysis, aiding in the identification of major air pollution sources. The results offer valuable insights for developing effective strategies to tackle air pollution, safeguard public health, and improve the overall environmental quality in India. The study underscores the importance of satellite data analysis and presents a comprehensive assessment of the impact of the shutdown on air quality, laying the groundwork for evidence-based decision-making and long-term pollution mitigation efforts.
A combined deep CNN-RNN network for rainfall-runoff modelling in Bardha Watershed, India
(KeAi Communications Co. Ltd., 2024-02-11) Shekar, Padala Raja; Mathew, Aneesh; Yeswanth, P.V.; Deivalakshmi, S
In recent years, there has been a growing interest in using artificial intelligence (AI) for rainfall-runoff modelling, as it has shown promising adaptability in this context. The current study involved the use of six distinct AI models to simulate monthly rainfall-runoff modelling in the Bardha watershed, India. These models included the artificial neural network (ANN), k-nearest neighbour regression model (KNN), extreme gradient boosting (XGBoost) regression model, random forest regression model (RF), convolutional neural network (CNN), and CNN-RNN (convolutional recurrent neural network). The years 2003–2007 are classified as the calibration or training period, while the years 2008–2009 are classified as the validation or testing period for the span of time 2003 to 2009. The available rainfall, maximum and minimum temperatures, and discharge data were collected and utilized in the models. To compare the performance of the models, five criteria were employed: R2, NSE, MAE, RMSE, and PBIAS. The CNN-RNN model simulates the rainfall-runoff model in the Bardha watershed best in both the training and testing periods (training: R2 is 0.99, NSE is 0.99, MAE is 1.76, RMSE is 3.11, and PBIAS is −1.45; testing: R2 is 0.97, NSE is 0.97, MAE is 2.05, RMSE is 3.60, and PBIAS is −3.94). These results demonstrate the superior performance of the CNN-RNN model in simulating monthly rainfall-runoff modelling when compared to the other models used in the study. The findings suggest that the CNN-RNN model could be a valuable tool for various applications related to sustainable water resource management, flood control, and environmental planning.
Machine learning and deep learning-based landslide susceptibility mapping using geospatial techniques in Wayanad, Kerala state, India
(KeAi Communications Co., 2024-10-12) Lokesh, P; Madhesh, C; Mathew, Aneesh; Shekar, Padala Raja
Landslide susceptibility mapping is vital for disaster management and sustainable land-use planning. This research was conducted in Wayanad, Kerala, India, to identify landslide susceptible zones. The study used large geospatial datasets, such as elevation, slope, aspect, curvature, stream power index, topographic wetness index, land use and land cover, rainfall, flow accumulation, geology, and geomorphology. It is followed by the application of various machine learning and deep learning models such as the support vector machine, artificial neural networks, logistic regression, random forest, gradient boosting machine, recurrent neural networks long short-term memory, and deep neural network models to map the landslide susceptible zones. The model was trained and validated using the landslide inventory map, which contains 298 sites of landslides. The random forest model, with 97 % accuracy, performed the best. It is possible to effectively mitigate landslides and plan long-term land use by identifying hazardous zones within the study region.
Towards Sustainable Development: Ranking of Soil Erosion-Prone Areas Using Morphometric Analysis and Multi-Criteria Decision-Making Techniques
(Multidisciplinary Digital Publishing Institute (MDPI), 2025-03-01) Shekar, Padala Raja; Mathew, Aneesh; Hasher, Fahdah Falah Ben; Mehmood, Kaleem; Zhran, Mohamed
Sub-watershed prioritization using morphometric analysis and multi-criteria decision-making (MCDM) techniques is a systematic approach to identifying and ranking sub-watersheds based on their susceptibility to soil erosion. This helps in implementing targeted soil conservation measures. In this study, sub-watersheds in the Narangi basin are prioritized by employing morphometric analysis integrated with advanced MCDM techniques, including additive ratio assessment (ARAS), complicated proportional assessment (COPRAS), multi-objective optimization by ratio analysis (MOORA), and the technique for order preference by similarity to ideal solution (TOPSIS). Weights for various MCDM methods are determined using the criteria importance through an inter-criteria correlation approach (CRITIC: criteria importance through inter-criteria correlation method), while geospatial techniques ensure precise spatial analysis. The results provide a unified ranking of sub-watersheds, revealing that sub-watershed 3 (SW3) and SW9 are in the high-priority soil erosion category; SW1, SW2, SW5, and SW8 are medium-priority; and SW4, SW6, SW7, and SW10 are low-priority. This comprehensive and sustainability-oriented approach equips decision-makers with robust tools to identify and manage sub-watersheds at risk of soil erosion, ensuring the long-term sustainability of land and water resources. This study aligns with sustainable development goal 15 (life on land) and promotes sustainable land use practices to combat soil degradation.
Urban heat island and pollutant correlations in Bangalore, India using geospatial techniques
(Elsevier Ltd, 2025-01-28) Mathew, Aneesh; Arunab, K.S.
The interaction between urban heat island (UHI) effects and urban air pollution significantly impacts urban ecology, climate dynamics, and inhabitants' well-being. This study examines into the correlation between UHI effects and various pollutants (CO, HCHO, aerosols, NO2, O3, and SO2) across Bangalore from 2019 to 2022, exploring their spatial and thermal connections. The study utilized satellite remote sensing data from TROPOMI for air pollutants (CO, NO₂, HCHO, SO₂, O₃, and aerosols) and MODIS for land surface temperature (LST). Data were collected over a four-year period (2019–2022) to analyze spatial and temporal pollutant distributions and UHI effects in Bangalore and employed statistical methods, including Pearson correlation, independent t-tests, and ANOVA, to assess the relationships between UHI indicators and pollutant concentrations. A weighted Urban Pollution Island (UPI) index was developed using Fuzzy AHP, while thermal categorization was achieved through spatial analysis techniques. Research indicates significantly elevated pollution levels in urban areas compared to rural regions. The research demonstrates positive correlation between UHI indicators and CO, HCHO, aerosols, NO2, and O3 in urban-rural environments. A negative correlation is observed between the UHI indicator and SO2 in these contexts, requiring a thorough investigation of the UHI-pollutant relationship. High-risk zones (HRZs) demonstrate significantly elevated yearly average concentrations of NO2 (66.614%), aerosols (13.610%), HCHO (8.816%), and CO (2.028%) relative to low-risk zones (LRZs). Ozone levels are consistently similar between HRZs and LRZs. In contrast, LRZs demonstrate a greater yearly average concentration of SO2 (7.562%) than HRZs. Furthermore, HRZs exhibit an elevated LST of 2.198 °C relative to LRZs. These results yield essential insights for urban planning and policy development, providing a thorough comprehension of UHI pollution dynamics. This research clarifies these dynamics, aiding informed decision-making to mitigate the effects of UHI and pollution in urban settings
Artificial recharge sites unveiled: Geospatial-MCDM solutions for Akeru watershed, Telangana, India
(Elsevier Inc., 2025-07-30) Shekar, Padala Raja; Mathew, Aneesh; Pramanik, Malay; Ben Hasher, Fahdah Falah; Zhran, Mohamed
Groundwater is vital for human health, agriculture, and ecological balance, making its sustainable management increasingly important amid rising demand. This study presents a geospatial and multi-criteria decision-making (MCDM) approach using the analytic hierarchy process (AHP) to identify suitable artificial recharge sites. Ten key thematic layers—drainage density, rainfall, topographic wetness index (TWI), curvature, elevation, geomorphology, topographic position index (TPI), distance from the river, land use and land cover, and slope—were selected based on their influence on groundwater recharge potential. Each layer was weighed using AHP, and the resulting normalized weights were integrated in a geographic information system (GIS) environment to delineate groundwater potential zones (GWPZs). The novelty of this research lies in overlaying the AHP-derived GWPZ map with identified artificial recharge locations, enabling precise site selection for recharge structures. The study area was classified into high, moderate, and poor recharge zones, with 74.6 % falling under moderate potential. Model validation using ground truth well locations and the area under the curve (AUC) method yielded a high prediction accuracy of 80.01 %, confirming the robustness of the methodology. A total of 176 suitable sites were identified, with recommendations for constructing percolation ponds and check dams. This approach enhances targeted groundwater recharge planning and supports sustainable water resource management. This research contributes directly to sustainable development goal 6 (clean water and sanitation) by promoting sustainable groundwater management and ensuring long-term water availability.