Evaluation of soil texture classification from orthodox interpolation and machine learning techniques.

The current investigation examines the effectiveness of various approaches in predicting the soil texture class (clay, silt, and sand contents) of the Rawalpindi district, Punjab province, Pakistan. The employed techniques included artificial neural networks (ANNs), kriging, co-kriging, and inverse distance weighting (IDW). A total of 44 soil specimens from depths of 10-15 cm were gathered, and then the hydrometer method was adopted to measure their texture. The map of soil grain sets was formulated in the ArcGIS environment, utilizing distinct interpolation approaches. The MATLAB software was used to evaluate soil texture. The gradient fraction, latitude and longitude, elevation, and soil texture fragments of points were proposed to an ANN. Several statistical values, such as correlation coefficient (R), geometric mean error ratios (GMER), and root mean square error (RMSE), were utilized to evaluate the precision of the intended techniques. In assessing grain size and spatial dissemination of clay, silt, and sand, the effectiveness and precision of ANN were superior compared to kriging, co-kriging, and inverse distance weighting. Still, less than a 50% correlation was observed using the ANN. In this examination, the IDW had inferior precision compared to the other approaches. The results demonstrated that the practices produced acceptable results and can be used for future research. Soil texture is among the most central variables that can manipulate agriculture plans. The prepared maps exhibiting the soil texture groups are imperative for crop yield and pastoral scheduling.

Impact of LULC on debris flow using linear aggression model from Gilgit to Khunjerab with emphasis on urban sprawl.

In this research, the impact of land use and land cover (LULC) on debris flow was evaluated in the Gilgit to Khunjerab region. Two events have been done: (i) LULC stimulations for 2026 and 2030 using the MOLUSCE plugin and (ii) debris flow susceptibility mapping using linear aggression model. The evaluation of LULC on debris flow susceptibility is based on two scenarios: (i) existing (2010, 2014, 2018, 2022) LULC scenarios and (ii) stimulated (2026, 2030) LULC scenarios. The linear aggression model has 16 contributing factors to developing the debris flow susceptibility mapping. The main contributing components in debris flow susceptibility mapping are slope and LCCS. According to the linear aggressiveness model, debris flow susceptibility grows as the LULC changes, and the high susceptibility zones' share increases. For the current years 2010, 2014, 2018, and 2022, as well as the stimulated years 2026 and 2030, the model had high success rates (> 90.0%) and prediction rates (> 85.0%). The findings backed up prior research and suggested that the impact of LULC will grow in the future.

Evaluation of Different Landslide Susceptibility Models for a Local Scale in the Chitral District, Northern Pakistan.

This work evaluates the performance of three machine learning (ML) techniques, namely logistic regression (LGR), linear regression (LR), and support vector machines (SVM), and two multi-criteria decision-making (MCDM) techniques, namely analytical hierarchy process (AHP) and the technique for order of preference by similarity to ideal solution (TOPSIS), for mapping landslide susceptibility in the Chitral district, northern Pakistan. Moreover, we create landslide inventory maps from LANDSAT-8 satellite images through the change vector analysis (CVA) change detection method. The change detection yields more than 500 landslide spots. After some manual post-processing correction, the landslide inventory spots are randomly split into two sets with a 70/30 ratio for training and validating the performance of the ML techniques. Sixteen topographical, hydrological, and geological landslide-related factors of the study area are prepared as GIS layers. They are used to produce landslide susceptibility maps (LSMs) with weighted overlay techniques using different weights of landslide-related factors. The accuracy assessment shows that the ML techniques outperform the MCDM methods, while SVM yields the highest accuracy of 88% for the resulting LSM.

Effect of multiple climate change scenarios and predicted land-cover on soil erosion: a way forward for the better land management.

The ecosystem, biodiversity, and anthropological existence in the Chitral district are in danger due to the sediments and soil erosion stemming from the changes in the land-cover and climate. This research aims to practice the RUSLE model with the changes in the land-cover and climate in upcoming situations for 2030 and 2040 to evaluate soil erosion annually as per the spatial dissemination and the tendency of sediment yield. The multilayer perceptron (MLP), an artificial neural network (ANN), besides the Markov chain analysis was used to model upcoming land-cover. The Max Planck Institute model, which demonstrated a revised bias as well as downscaled grid size under the Representative Concentration Pathways (RCPs), was used for examining the future changes in the climate. The modeled land-cover showed that the areas that are primarily comprised of natural trees and shrubs were transformed largely to agriculture and build-up areas. The average rainfall in the future under different RCP situations was elevated compared to the rainfall through historical time. The continuous variability in the R and C factors affects the probable soil erosion rate and sediment yield. Under RCP8.5 for both future years of 2030 and 2040, the extreme erosion rate was assessed at around 500 and 550 t/ha/year. Additionally, under the different RCP scenarios in 2030 and 2040, the outcomes of sediment yield were more significant than the sediment yield through historical time. The results showed that lower regions of the Chitral district are at risk of amplified soil erosion and sediment yield presently, as shown by the historical data and in the future. The produced soil erosion maps using ArcGIS 10.2 can play a valuable role in managing sustainable development, conservation of the watershed of the Chitral River, and reducing soil loss. Effective measures to overcome these concerns and mitigate the possible effects need to be planned and practiced, particularly the decrease in the storage volume of the reservoirs situated on the river.

A study on the performance of coke resistive cerium modified zeolite Y catalyst for the pyrolysis of scrap tyres in a two-stage fixed bed reactor.

Catalytic pyrolysis is a useful technique for the conversion of scrap tyres into liquid fuels. Zeolite catalysts were employed in the pyrolysis of scrap tyres for the production of aromatic rich fuel. Deactivation of zeolite catalysts during pyrolysis reaction was investigated which played an important role in the product quality and composition. Herein, the performance of microporous zeolite catalysts and mesoporous MCM-41 catalyst was evaluated in a two-stage fixed bed reactor for the pyrolysis of scrap tyres. Comparative studies showed the increase in the production of aromatic compounds up to 23.7% over zeolite catalyst as compared to 18.7% over MCM-41 catalyst. However, Zeolite Y catalyst exhibited higher coke formation led to the rapid deactivation. The stability of zeolite catalysts is addressed by the incorporation of Cerium metal within the framework of two zeolite catalysts namely Zeolite Y and ZSM-5 through the ion-exchange technique. Parent and spent catalysts were characterised using synchrotron FT-IR spectroscopy, temperature-programmed desorption of ammonia (NH3-TPD), N2 Physisorption, scanning electron microscopy (SEM), inductively coupled plasma-optical emission spectrometry (ICP-OES), energy-dispersive X-ray spectroscopy (EDX), and hydrogen temperature-programmed reduction (H2-TPD). A higher percentage of aromatics were produced over the large pore Zeolite Y. Cerium ion-exchange decreased the formation of coke from 8.1% to 5.7% over submicron and large pore Zeolite Y catalyst. Moreover, naphthalene production decreased over both Ce-Zeolite Y and Ce-ZSM-5.