Search Results (5)

Lithological mapping using satellite images, particularly hyperspectral data, helps in effectively defining the best initial targets for regional exploration. In this study, ZY1-02D hyperspectral image (HSI) data with moderate spectral and very high spatial resolution were employed for lithological mapping using spectral indices along with support vector machine (SVM) machine learning and spatial–spectral transformer (SSTF) deep learning methods in the Kohat–Pothohar Plateau at the eastern edge of the Main Boundary Thrust (MBT) in Pakistan. The research was accomplished using spectral profiles of minerals accompanied by false color composite (FCC), principal component analysis (PCA), SVM, and SSTF methods for classifying the main lithological units. The lithological discrimination map derived from the ZY1-02D data matched well with the known deposits and field inspections. The principal component analysis (PCA) obtained the highest eigenvalues and provided a significant discrimination of lithologies, particularly with hyperspectral data. The results revealed lithological units, three of which contained limestone and gypsum, while other lithological units were defined as sandstone, clay, and conglomerates. Field investigation and laboratory sample analysis through X-ray diffraction (XRD), photomicrographs, and spectral analysis confirmed the occurrence of limestone, gypsum, and sandstone, which are useful in identifying lithological units in the study area. This study will assist in more accurate geological discrimination and play a vital role in identifying oil and gas reservoirs, coal, gypsum, uranium, salt, and limestone deposits. Furthermore, the results of the SVM and SSTF techniques were quantitatively compared with the geological boundaries mapped in the field, showing an accuracy of nearly 89.7% and 92.1%, respectively. Overall, the methodology adopted showed great performance and strong potential for mapping alteration areas and lithological discriminations applied on the ZY1-02D hyperspectral data. Full article

Agricultural rainfed areas of Pakistan have been facing several issues in recent years, e.g., soil erosion, nutrient runoff, and soil dependency on rainfall for crop growth. Wheat is considered a major staple crop in Pakistan. The main concern in these regions is to conserve soil moisture as the crop depends on the moisture obtained by rainfall. Gypsum is considered one of the best moisture conservers, especially for rainfed areas. Hence, this study was initiated (1) to explore the effects of gypsum on soil moisture conservation and (2) to reveal the effects of gypsum on soil macronutrients, sulfur (S), calcium (Ca), nitrogen (N), phosphorus (P), and potassium (K) uptake, and, eventually, wheat yield. The study was conducted from July 2014 to April 2015 in the rainfed Pothohar region of Pakistan. The recommended mineral fertilizers (N120P80K60) along with the following gypsum treatments: T1 = gypsum @ 0 Mg ha⁻¹ (0 kg plot⁻¹), T2 = gypsum @ 1 Mg ha⁻¹ (0.6 kg plot⁻¹), T3 = gypsum @ 3 Mg ha⁻¹ (1.8 kg plot⁻¹), and T4 = gypsum @ 4 Mg ha⁻¹ (2.4 kg plot⁻¹) were applied. The Chakwal-50 wheat variety was sown, followed by gypsum application. The maximum moisture was recorded under the soil of treatment T4 each month. The soil moisture was conserved up to 21% (surface) and 23% (sub-surface) in January and February 2015, respectively, with a 4 Mg ha⁻¹ gypsum application. The highest nitrogen (N, 448.68 kg ha⁻¹), phosphorus (P, 50.6 kg ha⁻¹), potassium (K, 185.7 Kg ha⁻¹), sulfur (S, 9.75 kg ha⁻¹), and calcium (Ca, 35.5 kg ha⁻¹) uptake values were observed in treatment with a 3 Mg ha⁻¹ gypsum application (p < 0.05). The mean values of the grain yield ranged between 1903.4 (T1) and 2387.2 (T4) kg ha⁻¹. Compared with the yield under T1, the grain yields under treatment T2, T3, and T4 were increased by 11%, 24%, and 25%, respectively. The straw yield ranged between 2446 and 2767 kg ha⁻¹. There was no noticeable impact of gypsum application rates on the straw yield of the wheat crop (p > 0.05). Overall, treatment T3 was found to be optimal for conserving soil moisture, a better nutrient uptake, and, ultimately, the wheat crop yield with less input cost. Full article

The science of hydrological modeling has continuously evolved under the influence of rapid advancements in software and hardware technologies. Starting from simple rational formulae for estimating peak discharge and developing into sophisticated univariate predictive models, accurate conversion of rainfall into runoff and the assessment of inherent uncertainty has been a prime focus for researchers. Therefore, alternative data-driven methods have gained widespread attention in hydrology. Moreover, scientists often couple conventional machine learning models with data pre-processing techniques, i.e., wavelet transformation (WT), to enhance modelling accuracy. In this context, this research work attempts to explore the latent linkage between rainfall and runoff in Pothohar region of Pakistan by developing a novel linkage of five streamline techniques of machine learning, including single decision tree (SDT), decision tree forest (DTF), tree boost (TB), multilayer perceptron (MLP), and gene expression modeling (GEP), with a more sophisticated variant of WT, i.e., maximal overlap discrete wavelet transformation (MODWT), for boundary correction of the transformed components of timeseries data. This study also implements these machine learning models in a stand-alone mode for a more comprehensive comparative analysis of performances. Furthermore, the study uses a combined-basin approach that divides Pothohar region into two basins to compensate for the complex topographic division of the study area. The results indicate that MODWT-based DTF outperformed other stand-alone and hybrid models in terms of modeling accuracy. In the first scenario, considering the Bunha-Kahan River basin, MODWT-DTF yielded the highest NSE (0.86) and the lowest RMSE (220.45 mm) and R2 (0.92 at lag order 3 (Lo3)) when transformed with daubechies4 (db4) at level three. While in the Soan-Haro River basin, MODWT-DTF produced the highest accuracy modeling at lag order 4 (Lo4) (NSE = 0.88, RMSE = 21.72 m³/s, and R2 = 0.91). The highly accurate performance of 3- and 4-days lagged models reflects the temporal consistency in hydrological response of the study area. The comparison of simple and hybrid model performance indicates up to a 55% increase in modeling accuracy due to data pre-processing with wavelet transformation. Full article

The drought along with climate variation has become a serious issue for human society and the ecosystem in the arid region like the Soan basin (the main source of water resources for the capital of Pakistan and the Pothohar arid region). The increasing concerns about drought in the study area have brought about the necessity of spatiotemporal analysis and assessment of the linkage between different drought types for an early warning system. Hence, the streamflow drought index (SDI) and standard precipitation index (SPI) were used for the analysis of the spatiotemporal variations in hydrological and meteorological drought, respectively. Furthermore, statistical approaches, including regression analysis, trend analysis using Mann Kendall, and moving average, have been used for investigation of the linkage between these drought types, the significance of the variations, and lag time identification, respectively. The overall analysis indicated an increase in the frequency of both hydrological and meteorological droughts during the last three decades. Moreover, a strong linkage between hydrological and meteorological droughts was found; and this relationship varied on the spatiotemporal scale. Significant variations between hydrological and meteorological droughts also resulted during the past three (3) decades. These discrepancies would be because of different onset and termination times and specific anthropogenic activities in the selected basin for the minimization of hydrological drought. Conclusively, the present study contributes to comprehending the linkage between hydrological and meteorological droughts and, thus, could have a practical use for local water resource management practices at the basin scale. Full article

Rainfall–runoff modelling has been at the essence of research in hydrology for a long time. Every modern technique found its way to uncover the dynamics of rainfall–runoff relation for different basins of the world. Different techniques of machine learning have been extensively applied to understand this hydrological phenomenon. However, the literature is still scarce in cases of extensive research work on the comparison of streamline machine learning (ML) techniques and impact of wavelet pre-processing on their performance. Therefore, this study compares the performance of single decision tree (SDT), tree boost (TB), decision tree forest (DTF), multilayer perceptron (MLP), and gene expression programming (GEP) in rainfall–runoff modelling of the Soan River basin, Pakistan. Additionally, the impact of wavelet pre-processing through maximal overlap discrete wavelet transformation (MODWT) on the model performance has been assessed. Through a comprehensive comparative analysis of 110 model settings, we concluded that the MODWT-based DTF model has yielded higher Nash–Sutcliffe efficiency (NSE) of 0.90 at lag order (Lo4). The coefficient of determination for the model was also highest among all the models while least root mean square error (RMSE) value of 23.79 m³/s was also produced by MODWT-DTF at Lo4. The study also draws inter-technique comparison of the model performance as well as intra-technique differentiation of modelling accuracy. Full article