In this paper, an attempt has been made to interconnect the 32 dam sites within Tlawng Watershed ... more In this paper, an attempt has been made to interconnect the 32 dam sites within Tlawng Watershed in the state of Mizoram. Computational technique for interlinking of dam sites has been proposed for the purpose. The elevation of dam and the distance between dams are the main parameters considered. We tried to establish a path among dams that would maximize the area coverage, starting from the highest elevation to lowest elevation. An attempt has also been made to incorporate additional dams along this path depending on the distance and elevation. It was found that 11 dam sites could be connected out of 32 dam sites. Interlinking of dams would be highly suitable in hilly region like Mizoram due to dissimilarity of height of different dams (elevation factor) and naturally gravity could be used as a means of water transfer among the interconnect dams.
Abstract Litter production and its decomposition are major drivers of soil carbon input and nutri... more Abstract Litter production and its decomposition are major drivers of soil carbon input and nutrient cycling in forest ecosystems. The litterfall patterns and decomposition rate still need to be explored in the geographically diverse Himalayan region. The study aims to investigate the factor affecting litterfall and its decomposition patterns in tropical, subtropical, and temperate forests by synthesizing a comprehensive database for the Indian Himalayan Region (IHR). We compiled a large dataset of annual litterfall, litter decomposition rate (k), and litter quality data from 102 published research articles and explore its relationship with environmental factors using a linear mixed effect model. Mean annual litterfall in the IHR ranged between 0.26 and 13.4 Mg ha−1 yr−1 and k-values varied from 0.006 to 4.71 yr−1. The study shows greater annual litterfall (5.89 Mg ha−1 yr−1) and k-values (2.47 yr−1) in tropical forests while litter C:N ratio (48) and lignin content (20.2%) were significantly higher in subtropical forests. The data distribution indicates higher but insignificantly different litterfall (5.45 Mg ha−1 yr−1) and mean k-values (1.86 yr−1) in the 1000–2000 m altitudinal range. Both litterfall and k-values were greater at higher temperatures (20–30 °C; 4.72 Mg ha−1 yr−1, 1.81 yr−1) and moderate precipitation (1000–2000 mm; 5.57 Mg ha−1 yr−1, 1.60 yr−1). The linear mixed effect model revealed a significant effect of moisture and precipitation on annual litterfall patterns, while aridity and precipitation significantly influence litter decomposition rate. Our study concluded that tropical forests have greater annual litterfall and k-values but subtropical forests have higher litter quality. The present study suggests environmental factors and forest types instead of litter quality in the IHR mainly control litter decomposition. The outcome of the study can be used to predict the forest carbon and nutrient cycle in future climate change scenarios.
Crop diseases are the main threat to agricultural products. Fast, accurate, and automatic detecti... more Crop diseases are the main threat to agricultural products. Fast, accurate, and automatic detection of diseases can help to overcome this problem. Literature suggests, machine learning techniques are capable of achieving these goals in near real-time. This article presents a comprehensive review of machine learning techniques for crop disease detection and classification. Existing plant disease classification systems are explored and commonly used processing steps are investigated. Analysis of machine learning techniques, accuracy factor, and current state-of-the-art in this domain have been reviewed and presented through this manuscript. The survey resulted in the identification of the strengths and limitations of existing techniques and provides a road map for future research works. These would help researchers to understand and pursue machine learning applications in the field of disease detection and classification
Identification of dam sites is a strategic priority in water management scheme to preserve and co... more Identification of dam sites is a strategic priority in water management scheme to preserve and conserve water. The selection of such sites relies on a number of biophysical as well as socio-economic factors. Clustering technique and several Multi Criteria Decision Making (MCDM) approach such as Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) provides valuable tools in the selection of suitable dam sites. This paper presents the application of TOPSIS and k-means clustering technique in the selection of dam site. We have used four criteria in selecting the dam site which were found to be influential criteria in such a problem through literature survey. Eight potential dam sites were selected in Tlawng watershed based on expert opinion and applied TOPSIS and k-means clustering methods to obtain the most ideal solution out of the eight potential dam sites. The computational time of TOPSIS in finding the ideal solution has been reduced when combined with k-mean...
Rainfall-Runoff modeling plays a crucial role in various aspects of water resource management. It... more Rainfall-Runoff modeling plays a crucial role in various aspects of water resource management. It helps significantly in resolving the issues related to flood control, protection of agricultural lands, etc. Various Machine learning and statistical-based algorithms have been used for this purpose. These techniques resulted in outcomes with an acceptable rate of success. One of the pertinent machine learning algorithms namely Adaptive Neuro Fuzzy Inference System (ANFIS) has been reported to be a very effective tool for the purpose. However, the computational complexity of ANFIS is a major hindrance in its application. In this paper, we resolved this problem of ANFIS by incorporating one of the evolutionary algorithms known as Particle Swarm Optimization (PSO) which was used in estimating the parameters pertaining to ANFIS. The results of the modified ANFIS were found to be satisfactory. The performance of this modified ANFIS is then compared with conventional ANFIS and another popula...
In this paper, an attempt has been made to interconnect the 32 dam sites within Tlawng Watershed ... more In this paper, an attempt has been made to interconnect the 32 dam sites within Tlawng Watershed in the state of Mizoram. Computational technique for interlinking of dam sites has been proposed for the purpose. The elevation of dam and the distance between dams are the main parameters considered. We tried to establish a path among dams that would maximize the area coverage, starting from the highest elevation to lowest elevation. An attempt has also been made to incorporate additional dams along this path depending on the distance and elevation. It was found that 11 dam sites could be connected out of 32 dam sites. Interlinking of dams would be highly suitable in hilly region like Mizoram due to dissimilarity of height of different dams (elevation factor) and naturally gravity could be used as a means of water transfer among the interconnect dams.
Abstract Litter production and its decomposition are major drivers of soil carbon input and nutri... more Abstract Litter production and its decomposition are major drivers of soil carbon input and nutrient cycling in forest ecosystems. The litterfall patterns and decomposition rate still need to be explored in the geographically diverse Himalayan region. The study aims to investigate the factor affecting litterfall and its decomposition patterns in tropical, subtropical, and temperate forests by synthesizing a comprehensive database for the Indian Himalayan Region (IHR). We compiled a large dataset of annual litterfall, litter decomposition rate (k), and litter quality data from 102 published research articles and explore its relationship with environmental factors using a linear mixed effect model. Mean annual litterfall in the IHR ranged between 0.26 and 13.4 Mg ha−1 yr−1 and k-values varied from 0.006 to 4.71 yr−1. The study shows greater annual litterfall (5.89 Mg ha−1 yr−1) and k-values (2.47 yr−1) in tropical forests while litter C:N ratio (48) and lignin content (20.2%) were significantly higher in subtropical forests. The data distribution indicates higher but insignificantly different litterfall (5.45 Mg ha−1 yr−1) and mean k-values (1.86 yr−1) in the 1000–2000 m altitudinal range. Both litterfall and k-values were greater at higher temperatures (20–30 °C; 4.72 Mg ha−1 yr−1, 1.81 yr−1) and moderate precipitation (1000–2000 mm; 5.57 Mg ha−1 yr−1, 1.60 yr−1). The linear mixed effect model revealed a significant effect of moisture and precipitation on annual litterfall patterns, while aridity and precipitation significantly influence litter decomposition rate. Our study concluded that tropical forests have greater annual litterfall and k-values but subtropical forests have higher litter quality. The present study suggests environmental factors and forest types instead of litter quality in the IHR mainly control litter decomposition. The outcome of the study can be used to predict the forest carbon and nutrient cycle in future climate change scenarios.
Crop diseases are the main threat to agricultural products. Fast, accurate, and automatic detecti... more Crop diseases are the main threat to agricultural products. Fast, accurate, and automatic detection of diseases can help to overcome this problem. Literature suggests, machine learning techniques are capable of achieving these goals in near real-time. This article presents a comprehensive review of machine learning techniques for crop disease detection and classification. Existing plant disease classification systems are explored and commonly used processing steps are investigated. Analysis of machine learning techniques, accuracy factor, and current state-of-the-art in this domain have been reviewed and presented through this manuscript. The survey resulted in the identification of the strengths and limitations of existing techniques and provides a road map for future research works. These would help researchers to understand and pursue machine learning applications in the field of disease detection and classification
Identification of dam sites is a strategic priority in water management scheme to preserve and co... more Identification of dam sites is a strategic priority in water management scheme to preserve and conserve water. The selection of such sites relies on a number of biophysical as well as socio-economic factors. Clustering technique and several Multi Criteria Decision Making (MCDM) approach such as Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) provides valuable tools in the selection of suitable dam sites. This paper presents the application of TOPSIS and k-means clustering technique in the selection of dam site. We have used four criteria in selecting the dam site which were found to be influential criteria in such a problem through literature survey. Eight potential dam sites were selected in Tlawng watershed based on expert opinion and applied TOPSIS and k-means clustering methods to obtain the most ideal solution out of the eight potential dam sites. The computational time of TOPSIS in finding the ideal solution has been reduced when combined with k-mean...
Rainfall-Runoff modeling plays a crucial role in various aspects of water resource management. It... more Rainfall-Runoff modeling plays a crucial role in various aspects of water resource management. It helps significantly in resolving the issues related to flood control, protection of agricultural lands, etc. Various Machine learning and statistical-based algorithms have been used for this purpose. These techniques resulted in outcomes with an acceptable rate of success. One of the pertinent machine learning algorithms namely Adaptive Neuro Fuzzy Inference System (ANFIS) has been reported to be a very effective tool for the purpose. However, the computational complexity of ANFIS is a major hindrance in its application. In this paper, we resolved this problem of ANFIS by incorporating one of the evolutionary algorithms known as Particle Swarm Optimization (PSO) which was used in estimating the parameters pertaining to ANFIS. The results of the modified ANFIS were found to be satisfactory. The performance of this modified ANFIS is then compared with conventional ANFIS and another popula...
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Papers by Amitabha Nath