Chaitanya Baliram Pande

Scopus Publications

Source identification and potential health risks from elevated groundwater nitrate contamination in Sundarbans coastal aquifers, India
Subodh Chandra Pal, Tanmoy Biswas, Asit Kumar Jaydhar, Dipankar Ruidas, Asish Saha, Indrajit Chowdhuri, Sudipto Mandal, Aznarul Islam, Abu Reza Md. Towfiqul Islam, Chaitanya B. Pande,et al.
Springer Science and Business Media LLC
AbstractIn recent years groundwater contamination through nitrate contamination has increased rapidly in the managementof water research. In our study, fourteen nitrate conditioning factors were used, and multi-collinearity analysis is done. Among all variables, pH is crucial and ranked one, with a value of 0.77, which controls the nitrate concentration in the coastal aquifer in South 24 Parganas. The second important factor is Cl−, the value of which is 0.71. Other factors like—As, F−, EC and Mg2+ ranked third, fourth and fifth position, and their value are 0.69, 0.69, 0.67 and 0.55, respectively. Due to contaminated water, people of this district are suffering from several diseases like kidney damage (around 60%), liver (about 40%), low pressure due to salinity, fever, and headache. The applied method is for other regions to determine the nitrate concentration predictions and for the justifiable alterationof some management strategies.

Assessment of current and future trends in water resources in the Gambia River Basin in a context of climate change
Serigne Mory Khouma Séne, Cheikh Faye, and Chaitanya B. Pande
Springer Science and Business Media LLC
AbstractAccurate assessment of water resources at the watershed level is crucial for effective integrated watershed management. While semi-distributed/distributed models require complex structures and large amounts of input data, conceptual models have gained attention as an alternative to watershed modeling. In this paper, the performance of the GR4J conceptual model for runoff simulation in the Gambia watershed at Simenti station is analyzed over the calibration (1981–1990) and validation period (1991–2000 and 2001–2010). The main inputs to conceptual models like GR4J are daily precipitation data and potential evapotranspiration (PET) measured from the same catchment or a nearby location. Calibration of these models is typically performed using the Nash–Sutcliffe daily efficiency with a bias penalty as the objective function. In this case, the GR4J model is calibrated using four optimization parameters. To evaluate the effectiveness of the model's runoff predictions, various statistical measures such as Nash–Sutcliffe efficiency, coefficient of determination, bias, and linear correlation coefficient are calculated. The results obtained in the Gambia watershed at Simenti station indicate satisfactory performance of the GR4J model in terms of forecast accuracy and computational efficiency. The Nash–Sutcliffe (Q) values are 0.623 and 0.711 during the calibration period (1981–1990) and the validation period (1991–2000), respectively. The average annual flow observed during the calibration period is 0.385 mm while it increases with a value of 0.603 mm during the validation period. As for the average flow simulated by the model, it is 0.142 mm during the calibration period (i.e., a delay of 0.142 mm compared to the observed flow), 0.626 mm in the validation period (i.e., an excess of 0.023 mm compared to the observed flow). However, this study is significant because it shows significant changes in all metrics in the watershed sample under different scenarios, especially the SSP245 and SSP585 scenarios over the period 2021–2100. These changes suggest a downward trend in flows, which would pose significant challenges for water management. Therefore, it is clear that sustainable water management would require substantial adaptation measures to cope with these changes.

GIS-based multi-influencing factor (MIF) application for optimal site selection of solar photovoltaic power plant in Nashik, India
Nitin Liladhar Rane, Mehmet Akif Günen, Suraj Kumar Mallick, Jayesh Rane, Chaitanya B. Pande, Monica Giduturi, Javed Khan Bhutto, Krishna Kumar Yadav, Abebe Debele Tolche, and Maha Awjan Alreshidi
Springer Science and Business Media LLC
AbstractThe significant natural energy sources for reducing the global usage of fossil fuels are renewable energy (RE) sources. Solar energy is a crucial and reliable RE source. Site selection for solar photovoltaic (PV) farms is a crucial issue in terms of spatial planning and RE policies. This study adopts a Geographic Information System (GIS)-based Multi-Influencing Factor (MIF) technique to enhance the precision of identifying and delineating optimal locations for solar PV farms. The choice of GIS and MIF is motivated by their ability to integrate diverse influencing factors, facilitating a holistic analysis of spatial data. The selected influencing factors include solar radiation, wind speed, Land Surface Temperature (LST), relative humidity, vegetation, elevation, land use, Euclidean distance from roads, and aspect. The optimal sites of solar PV power plant delineated revealed that ‘very low’ suitability of site covering 4.866% of the study area, ‘low’ suitability of site 13.190%, ‘moderate’ suitability of site 31.640%, ‘good’ suitability of site 32.347%, and ‘very good’ suitability of site for solar PV power plant encompassing 17.957% of the study area. The sensitivity analysis results show that the solar radiation, relative humidity, and elevation are the most effective on the accuracy of the prediction. The validation of the results shows the accuracy of solar PV power plant prediction using MIF technique in the study area was 81.80%. The integration of GIS and MIF not only enhances the accuracy of site suitability assessment but also provides a practical implementation strategy. This research offers valuable insights for renewable energy policymakers, urban planners, and other stakeholders seeking to identify and develop optimal locations for solar energy power farms in their respective regions.

Predictive modelling of nitrogen dioxide using soft computing techniques in the Agra, Uttar Pradesh, India
Parveen Sihag, Tamanna Mehta, Saad Sh Sammen, Chaitanya Baliram Pande, Diksha Puri, and Neyara Radwan
Elsevier BV

Appraisal on suitable landfill site selection for municipal solid waste disposal using GIS and MIF methods
Nitin Liladhar Rane, Suraj Kumar Mallick, Arjun Saha, Chaitanya Baliram Pande, Jayesh Rane, Ranjan Roy, Fahad Alshehri, and Neyara Radwan
Elsevier BV

Characterizing seasonal, environmental and human-induced factors influencing the dynamics of Rispana River's water quality: Implications for sustainable river management
Sushmita Bhatt, Arun Pratap Mishra, Naveen Chandra, Himanshu Sahu, Shardesh Kumar Chaurasia, Chaitanya B. Pande, Johnson C. Agbasi, Mohd Yawar Ali Khan, Sani I. Abba, Johnbosco C. Egbueri,et al.
Elsevier BV

Flood mapping based on novel ensemble modeling involving the deep learning, Harris Hawk optimization algorithm and stacking based machine learning
Romulus Costache, Subodh Chandra Pal, Chaitanya B. Pande, Abu Reza Md. Towfiqul Islam, Fahad Alshehri, and Hazem Ghassan Abdo
Springer Science and Business Media LLC
AbstractAmong the various natural disasters that take place around the world, flood is considered to be the most extensive. There have been several floods in Buzău river basin, and as a result of this, the area has been chosen as the study area. For the purpose of this research, we applied deep learning and machine learning benchmarks in order to prepare flood potential maps at the basin scale. In this regard 12 flood predictors, 205 flood and 205 non-flood locations were used as input data into the following 3 complex models: Deep Learning Neural Network-Harris Hawk Optimization-Index of Entropy (DLNN-HHO-IOE), Multilayer Perceptron-Harris Hawk Optimization-Index of Entropy (MLP-HHO-IOE) and Stacking ensemble-Harris Hawk Optimization-Index of Entropy (Stacking-HHO-IOE). The flood sample was divided into training (70%) and validating (30%) sample, meanwhile the prediction ability of flood conditioning factors was tested through the Correlation-based Feature Selection method. ROC Curve and statistical metrics were involved in the results validation. The modeling process through the stated algorithms showed that the most important flood predictors are represented by: slope (importance ≈ 20%), distance from river (importance ≈ 17.5%), land use (importance ≈ 12%) and TPI (importance ≈ 10%). The importance values were used to compute the flood susceptibility, while Natural Breaks method was used to classify the results. The high and very high flood susceptibility is spread on approximately 35–40% of the study zone. The ROC Curve, in terms of Success, Rate shows that the highest performance was achieved FPIDLNN-HHO-IOE (AUC = 0.97), followed by FPIStacking-HHO-IOE (AUC = 0.966) and FPIMLP-HHO-IOE (AUC = 0.953), while the Prediction Rate indicates the FPIStacking-HHO-IOE as being the most performant model with an AUC of 0.977, followed by FPIDLNN-HHO-IOE (AUC = 0.97) and FPIMLP-HHO-IOE (AUC = 0.924).

Times Series Forecasting of Monthly Rainfall using Seasonal Auto Regressive Integrated Moving Average with EXogenous Variables (SARIMAX) Model
Shahenaz Mulla, Chaitanya B. Pande, and Sudhir K. Singh
Springer Science and Business Media LLC

Predictive modeling of land surface temperature (LST) based on Landsat-8 satellite data and machine learning models for sustainable development
Chaitanya Baliram Pande, Johnbosco C. Egbueri, Romulus Costache, Lariyah Mohd Sidek, Qingzheng Wang, Fahad Alshehri, Norashidah Md Din, Vinay Kumar Gautam, and Subodh Chandra Pal
Elsevier BV

Prediction of weighted arithmetic water quality index for urban water quality using ensemble machine learning model
Usman Mohseni, Chaitanya B. Pande, Subodh Chandra Pal, and Fahad Alshehri
Elsevier BV

Groundwater quality characterization using an integrated water quality index and multivariate statistical techniques
Vinay Kumar Gautam, Mahesh Kothari, Baqer Al-Ramadan, Pradeep Kumar Singh, Harsh Upadhyay, Chaitanya B. Pande, Fahad Alshehri, and Zaher Mundher Yaseen
Public Library of Science (PLoS)
This study attempts to characterize and interpret the groundwater quality (GWQ) using a GIS environment and multivariate statistical approach (MSA) for the Jakham River Basin (JRB) in Southern Rajasthan. In this paper, analysis of various statistical indicators such as the Water Quality Index (WQI) and multivariate statistical methods, i.e., principal component analysis and correspondence analysis (PCA and CA), were implemented on the pre and post-monsoon water quality datasets. All these methods help identify the most critical factor in controlling GWQ for potable water. In pre-monsoon (PRM) and post-monsoon (POM) seasons, the computed value of WQI has ranged between 28.28 to 116.74 and from 29.49 to 111.98, respectively. As per the GIS-based WQI findings, 63.42 percent of the groundwater samples during the PRM season and 42.02 percent during the POM were classed as ‘good’ and could be consumed for drinking. The Principal component analysis (PCA) is a suitable tool for simplification of the evaluation process in water quality analysis. The PCA correlation matrix defines the relation among the water quality parameters, which helps to detect the natural or anthropogenic influence on sub-surface water. The finding of PCA’s factor analysis shows the impact of geological and human intervention, as increased levels of EC, TDS, Na+, Cl-, HCO3-, F-, and SO42- on potable water. In this study, hierarchical cluster analysis (HCA) was used to categories the WQ parameters for PRM and POR seasons using the Ward technique. The research outcomes of this study can be used as baseline data for GWQ development activities and protect human health from water-borne diseases in the southern region of Rajasthan.

Monitoring of wetland turbidity using multi-temporal Landsat-8 and Landsat-9 satellite imagery in the Bisalpur wetland, Rajasthan, India
Raj Singh, Vara Saritha, and Chaitanya B. Pande
Elsevier BV

Delineation of environmentally sustainable urban settlement using GIS-based MIF and AHP techniques
Nitin Liladhar Rane, Saurabh Purushottam Choudhary, Arjun Saha, Aman Srivastava, Chaitanya Baliram Pande, Fahad Alshehri, Ranjan Roy, Okan Mert Katipoğlu, and Hazem Ghassan Abdo
Informa UK Limited

The impact of climate change on plant diseases and food security
Maksud Hasan Shah, Sk Naim Aktar, Kalipada Pramanik, Chaitanya B. Pande, and Golam Torab Ali
Elsevier

GIS Based Delineation of Flood Susceptibility Mapping Using Analytic Hierarchy Process in East Vidarbha Region, India
Kanak Moharir, Manpreet Singh, Chaitanya B. Pande, and Abhay M. Varade
Springer International Publishing

Soil erosion and sediment yield estimation in a tropical monsoon dominated river basin using GIS-based models
Biplab Sarkar, Abdur Rahman, Aznarul Islam, Atiqur Rahman, Sk. Mafizul Haque, Swapan Talukdar, Abu Reza Md Towfiqul Islam, Subodh Chandra Pal, Chaitanya B. Pande, Edris Alam,et al.
Informa UK Limited

Development of landslide susceptibility maps of Tripura, India using GIS and analytical hierarchy process (AHP)
Nirmalya Kumar Nath, Vinay Kumar Gautam, Chaitanya B. Pande, Leena Rani Mishra, Jaripiti T. Raju, Kanak N. Moharir, and Nitin Liladhar Rane
Springer Science and Business Media LLC

Impact of land use/land cover changes on evapotranspiration and model accuracy using Google Earth engine and classification and regression tree modeling
Chaitanya B. Pande, Pranaya Diwate, Israel R. Orimoloye, Lariyah Mohd Sidek, Arun Pratap Mishra, Kanak N. Moharir, Subodh Chandra Pal, Fahad Alshehri, and Abebe Debele Tolche
Informa UK Limited

Assessment of water quality index using Analytic Hierarchy Process (AHP) and GIS: a case study of a struggling Asan River
Arun Pratap Mishra, Sachchidanand Singh, Mohit Jani, Kunwar Abhishek Singh, Chaitanya B. Pande, and Abhay M. Varade
Informa UK Limited

Sustainable Water Management for Small Farmers with Center-Pivot Irrigation: A Hydraulic and Structural Design Perspective
Muhammad Rashid, Saif Haider, Muhammad Umer Masood, Chaitanya B. Pande, Abebe Debele Tolche, Fahad Alshehri, Romulus Costache, and Ismail Elkhrachy
MDPI AG
In Pakistan, surface water supply for irrigation is decreasing, while water demand is increasing for agriculture production. Also, due to the fast rate of population growth, land holding capacity is decreasing. So, there is a need to develop appropriate technologies and design approaches for small-scale farmers to improve modern irrigation practices. In this study, a hydraulic and structural layout of CPIS was designed for small-scale farmers with some modifications. The hydraulic parameters and structural design of the CPIS were designed using IrriExpress and SAP2000 software, respectively. An economic analysis of the modified CPIS was carried out. The results revealed that in one complete revolution of the whole system, its span slope varied from 2.98 to 0.1%, and the wheel slope varied from 2.35 to −2.4%. The timing setting was 60% for one revolution, and the irrigation depth was 10 mm. When the time setting was reduced from 100% to 10%, the irrigation hours per cycle and irrigation depth both increased. Variendeel type-II trusses were designed for structural purposes using SAP2000 software. This design led to a 17% reduction in weight by lowering it from 1.916 to 1.5905 tons and a 44% reduction in joint count, decreasing it from 32 to 18. Our economic analysis revealed that the structural part of the system is more expensive than the hydraulic, electric and power parts for small-scale design. So, it was suggested that CPIS is suitable for land holdings from 100 to 250 acres, because when the area increases to more than 250 acres, there is no significant change in the cost. A towable system is more economical for small-scale farmers due to its lower cost per acre. This study will be helpful for the optimization of CPISs to improve water use efficiency and crop yield.

The Effect of the El Nino Southern Oscillation on Precipitation Extremes in the Hindu Kush Mountains Range
Muhammad Umer Masood, Saif Haider, Muhammad Rashid, Muhammad Usama Khan Lodhi, Chaitanya B. Pande, Fahad Alshehri, Kaywan Othman Ahmed, Miklas Scholz, and Saad Sh. Sammen
MDPI AG
The El Nino Southern Oscillation (ENSO) phenomenon is devastating as it negatively impacts global climatic conditions, which can cause extreme events, including floods and droughts, which are harmful to the region’s economy. Pakistan is also considered one of the climate change hotspot regions in the world. Therefore, the present study investigates the effect of the ENSO on extreme precipitation events across the Upper Indus Basin. We examined the connections between 11 extreme precipitation indices (EPIs) and two ENSO indicators, the Southern Oscillation Index (SOI) and the Oceanic Niño Index (ONI). This analysis covers both annual and seasonal scales and spans the period from 1971 to 2019. Statistical tests (i.e., Mann–Kendall (MK) and Innovative Trend Analysis (ITA)) were used to observe the variations in the EPIs. The results revealed that the number of Consecutive Dry Days (CDDs) is increasing more than Consecutive Wet Days (CWDs); overall, the EPIs exhibited increasing trends, except for the Rx1 (max. 1-day precipitation) and Rx5 (max. 5-day precipitation) indices. The ENSO indicator ONI is a temperature-related ENSO index. The results further showed that the CDD value has a significant positive correlation with the SOI for most of the UIB (Upper Indus Basin) region, whereas for the CWD value, high elevated stations gave a positive relationship. A significant negative relationship was observed for the lower portion of the UIB. The Rx1 and Rx5 indices were observed to have a negative relationship with the SOI, indicating that El Nino causes heavy rainfall. The R95p (very wet days) and R99p (extreme wet days) indices were observed to have significant negative trends in most of the UIB. In contrast, high elevated stations depicted a significant positive relationship that indicates they are affected by La Nina conditions. The PRCPTOT index exhibited a negative relationship with the SOI, revealing that the El Nino phase causes wet conditions in the UIB. The ONI gave a significant positive relationship for the UIB region, reinforcing the idea that both indices exhibit more precipitation during El Nino. The above observations imply that while policies are being developed to cope with climate change impacts, the effects of the ENSO should also be considered.

Hydrological response of tropical rivers basins to climate change using the GR2M model: the case of the Casamance and Kayanga-Géva rivers basins
Cheikh Abdoul Aziz Sy Sadio, Cheikh Faye, Chaitanya B. Pande, Abebe Debele Tolche, Mohd Sajid Ali, Marina M. S. Cabral-Pinto, and Mohamed Elsahabi
Springer Science and Business Media LLC
AbstractThe main objective of this research is to evaluate the effects of climate change first on precipitation and temperature, and then on the runoff characteristics of two tropical watersheds located in Senegal and Guinea-Bissau. To achieve this, eighteen General Circulation Models (GCMs) were selected to measure various climate change scenarios under the Shared Socioeconomic Pathways (SSP) SSP1-2.6 and SSP5-8.5, using the reference period of 1985–2014. The GR2M hydrological model was employed to replicate past monthly surface runoff patterns for the Casamance and Kayanga-Géva watersheds. After calibrating and validating the GR2M model, the researchers simulated the predictable effect of climate change on the flow for the near future (2021–2040), medium future (2041–2060), and distant future (2081–2100) for each watershed, using the GCM multi-model ensemble mean. The quantile method was used to correct bias in temperature and precipitation data. The results of bias correction give a correlation coefficient greater than 0.9% for temperatures and 0,6% precipitation between the outputs of the multi-model ensemble and observations used. The results indicate also that all watersheds are expected to experience drier conditions in the near-future, mid-future, and far-future periods under both the SSP1-2.6 and SSP5-8.5 scenarios. Furthermore, the predictable temperature trends consistently show a warmer situation with growing radiative making in the future times. However, the primary factor influencing changes in flow for all watersheds is the projected precipitation changes. The anticipated drier conditions in the near-future, mid-future, and far-future horizons under both scenarios would lead to significantly reduced runoff volumes at the beginning and middle of the rainy season. Consequently, the projected seasonal changes in river flow for all catchments (e.g., under SSP5-8.5 scenario, a decline of -34.47%, -56.01%, and -68.01% was noted, respectively, for the horizons 2050, 2070, and 2090 for the Casamance basin) could lead to new frequent occurrences of drought and water scarcity associated with past hydrological regimes. These scenarios enhance the necessity of improving water management, water prizing, and water recycling policies, to ensure water supply and to reduce tensions among regions and countries.

Hydrological evaluation of conservation bench terrace system in outer foothills of Himalayas using soft computing techniques
Ambrish Kumar, Narinder Kumar Sharma, Bihari Lal Dhyani, Manish Kumar, and Chaitanya B. Pande
Elsevier BV

Assessment of rainwater harvesting potential for urban area under climate and land use changes using geo-informatics technology
Saif Haider, Muhammad Umer Masood, Muhammad Rashid, Tauqeer Ali, Chaitanya B. Pande, Fahad Alshehri, and Ismail Elkhrachy
Elsevier BV

Quantifying the Impacts of Climate and Land Cover Changes on the Hydrological Regime of a Complex Dam Catchment Area
Muhammad Umer Masood, Saif Haider, Muhammad Rashid, Mohammed Suleman Aldlemy, Chaitanya B. Pande, Bojan Đurin, Raad Z. Homod, Fahad Alshehri, and Ismail Elkhrachy
MDPI AG
In this study, hydrological modeling at the watershed level is used to assess the impacts of climate and land use changes on the catchment area of the Khanpur Dam, which is an important water source for Rawalpindi and Islamabad. The hydrological impact of past and anticipated precipitation in the Khanpur Dam watershed was forecast by using a HEC-HMS model. After calibration, the framework was employed to analyze the effects of changes in land cover and climate on the hydrological regime. The model used information from three climatic gauge stations (Murree, Islamabad Zero Point, and Khanpur Dam) to split the Khanpur Dam catchment area into five sub-basins that encompass the entire watershed region, each with distinctive characteristics. The model was evaluated and checked for 2016–2018 and 2019–2020, and it produced an excellent match with the actual and anticipated flows. After statistical downscaling with the CMhyd model, the most effective performing GCM (MPI-ESM1-2-HR) among the four GCMs was chosen and used to forecast projections of temperature and precipitation within two shared socioeconomic pathways (SSP2 and SSP5). The predictions and anticipated changes in land cover were incorporated into the calibrated HEC-HMS model to evaluate the potential impact of climate change and land cover change at the Khanpur Dam. The starting point era (1990–2015) and the projected period (2016–2100), which encompassed the basis in the present century, were analyzed annually. The results indicated a spike in precipitation for the two SSPs, which was predicted to boost inflows all year. Until the end of the twenty-first century, SSP2 predicted a 21 percent rise in precipitation in the Khanpur Dam catchment area, while SSP5 predicted a 28% rise in precipitation. Increased flows were found to be projected in the future. It was found that the calibrated model could also be used effectively for upcoming studies on hydrological effects on inflows of the Khanpur Dam basin.

Chaitanya Baliram Pande

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