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.

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.

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.

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.

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.