Anurag Ohri

GIS based environmental decision support system for municipal landfill site selection

Purpose – A GIS based environmental decision support system for municipal solid waste management under Indian socio‐economic and regulatory conditions, named as EDSS‐MSWI has been developed. This paper intends to report the methodology and application of the EDSS‐MSWI for municipal landfill site selection taking a case study of Varanasi city (India).Design/methodology/approach – EDSS‐MSWI has been developed using VB.NET and ArcGIS Engine programming tools. A set of 13 criteria are selected for primary landfill site selection. Analytical hierarchy process (AHP) has been used to give weights to different criteria. The criteria were aggregated and suitability index (S) is generated using weighted linear combination (WLC) technique in GIS environment. The suitability index (0‐10) values are classified into four categories (Excluded, Less Preferable, Suitable and Best Suitable) to select the landfill site.Findings – The results indicate that there are at least four locations under the “best suitable” category ...

Evaluating the effects of spatial resolution on land use and land cover classification accuracy

The choice of appropriate spatial resolution is a key factor to extract desired information from remotely sensed images. Optical data collected by two different sensors (LISS IV with 5.8 m and Landsat 8-OLI with 30 m spatial resolution respectively) were investigated against the capability to classify accurately into distinct land use and land cover (LULC) classes. To evaluate the quality of training samples class separability analysis using transformed divergence (TD) method was performed. Furthermore, supervised maximum likelihood classifier (MLC) was used to carry out LULC classification. The results indicated that the overall accuracy 83.28% and Kappa coefficient 0.805 for LISS IV image was found higher in comparison to Landsat 8-OLI image having overall accuracy 77.93% and Kappa coefficient 0.742 respectively.

Application of Artificial Neural Networks for Identifying Optimal Groundwater Pumping and Piping Network Layout

The simulation-optimization approach is often used to solve water resource management problem although repeated use of the simulation model enhances the computational load. In this study, Artificial Neural Network (ANN) and Bagged Decision Trees (BDT) models were developed as an approximator for Analytic Element Method (AEM) based groundwater flow model. Developed ANN and BDT models were coupled with Particle Swarm Optimization (PSO) model to solve the well-field management problem. The groundwater flow model was developed for the study area and used to generate the dataset for the training and testing of the ANN & BDT models. These coupled ANN-PSO & BDT-PSO models were employed to find the optimal design and cost of the new well-field system by optimizing discharge & co-ordinate of wells along with the cost effective layout of piping network. The Minimum Spanning Tree (MST) based model was used to find out the optimal piping network layout and checking the hydraulic constraints in the piping network. The results show that the ANN & BDT models are good approximators of AEM model and they can reduce the computational burden significantly although ANN model performs better than BDT model. The results show that the coupling of piping network model with simulation-optimization model is very significant for finding the cost effective and realistic design of the new well-field system.

Assessment of groundwater quality using WQI and GIS near the Karsara municipal landfill site, Varanasi, India

The present paper reports the evaluation of the groundwater quality around the municipal landfill site in Varanasi City, Uttar Pradesh, India. Leachate pollution index (LPI) and water quality index (WQI) have been used to determine the quality of leachate of landfill and groundwater in the study area. LPI value (16.81) of landfill leachate revealed the presence of significant amount of the pollutants. WQI of groundwater samples near the landfill site has been calculated and water quality map has been prepared by using WQI in GIS environment for both pre- and post-monsoon periods of the year 2016. Results of WQI revealed that 35% groundwater samples are good, 35% marginal, 20% excellent, and 10% are in fair category in pre-monsoon period while in post-monsoon 70% samples are marginal, 15% excellent, 10% fair, and 5% are in good category. WQI map shows that most of the study area near the landfill site is in fair category during pre-monsoon season but in threatened category during post-monsoon season. The results of the physicochemical analysis of groundwater have shown that the water is not safe for drinking purpose as some parameters like TDS, hardness, total alkalinity, and nitrate and iron content were observed to be above the acceptable limit (WHO and BIS) of drinking water quality. This study revealed that WQI and LPI can also be an important monitoring tool for landfill policy makers and the public to safeguard groundwater pollution risk from the landfill.

Land Use Classification and Watershed Analysis of Assi River, Varanasi, Uttar Pradesh, India

Watershed analysis is essential for planning development activities or improving the features of a terrain. It gives an idea for various features like - aspect, elevation, slope, drainage, urban distribution, etc. in the area. This study is done either by field survey or with the help of various software tools. Varanasi has been selected as one of the cities to be developed into a smart city. But being one of the oldest cities of the world, a proper sustain planning is really essential to make this a reality. In the present study, river Assi (a tributary of Ganges) , geographically located between - 25°16’59.0” N and 83°00’35.3” E, in Varanasi district of Uttar Pradesh (India), has been considered. Continuous dumping of waste, heavy encroachments and improper planning has reduced this river into nothing but just a drain. Being a tributary of Ganges, all of this waste further reaches Ganges water, depleting its water quality too. Software’s like ArcGIS, ERDAS Imagine 2016 and SWAT has been used for the study of the watershed of this Assi River. The overall classification accuracy from the Land Use map for the 3rd order watershed has been computed as 89.32% with Kappa Coefficient being 0.7751. A digitized map of the watershed is prepared to compute the percentage of various features like - settlement, water bodies, cultivated land, etc. in the area of the watershed. Through SWAT, watershed has been divided into various sub-watersheds, which enables to in identification of key drains of the river. This study will thus not only help in identifying urban pattern of the area, but will also help in identifying key aspects that are to be answered in order to remediate Assi back to its river form through proper planning of development activities around Assi River without affecting its ecology.

Hydrological Modeling of Mahi Basin Using SWAT

In general, hydrological models such as SWAT incorporate many parameters (both statistical and of physical significance). Most of these parameters obtain their values via extensive field surveys and experiments; the resulting values are then used to calibrate the model. And few of these parameters have a significant impact on the modeling results, and so they are referred to as “sensitive parameters” while others don’t have much impact or very little impact. The other parameters (“insensitive parameters”) can be ignored in the mathematical model, leading to a simplification of the model’s structure. Sensitivity analysis of parameters aims to explore the sensitivity of prediction variables to parameter variability. Sensitivity analysis helps reduce the number of parameters in the calibration process, while an automatic calibration technique allows the user to avoid tedious, time

Crop variables estimation by adaptive neuro-fuzzy inference system using bistatic scatterometer data

consuming, manual calibration. The re sult is a computationally efficient calibration [19]. By including uncertainty analysis in the model parameters and output variable, more information can be conveyed about the degree of risk associated with a specific action. Uncertainty and sensitivity analyses are critical for decision

Bistatic measurements for the estimation of rice crop variables using artificial neural network

maki ng. While calibrating the model, two concepts must be kept in mind: (1) parameter non-uniqueness , which states that there are many other solutions (different parameter values) that produce equally good results; and (2) parameter conditionality , which means that a calibrated model is only locally conditional and cannot be applied globally [2]. This paper’s objective is to check the SWAT model’s

Urban Sprawl Mapping and Land Use Change Detection Using Remote Sensing and GIS

The aim of present study is to estimate the crop variables by means of high performing technique like adaptive neuro-fuzzy inference system (ANFIS) using the bistatic scatterometer data. An outdoor 4×4 m2 crop bed of rice crop was prepared for performing all the experiments. The bistatic measurements were carried out over the entire growing stages of the rice crop from transplanting to ripening stage at the angular range of 200 to 700 with the steps 50 at both HH- and VV-polarizations in X-band. The ANFIS algorithm was used for the estimation of rice crop variables. The observed bistatic scattering coefficients and crop variables (biomass, leaf area index, plant height and chlorophyll content) were interpolated with the phenological stages of the rice crop. The 80% data sets were used for training while the remaining 20% were kept separately for the testing purposes. The bistatic scattering coefficients were used as the input data sets and the rice crop variables as the target data sets of fuzzy inference system for both the polarizations. The estimated values were found closer to the observed values of rice crop variables that indicate a satisfactory performance of ANFIS algorithm for estimating rice crop variables.